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DTSTART;TZID=America/New_York:20230407T140000
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SUMMARY:Current Developments in Mathematics Conference 2023
DESCRIPTION:Current Developments in Mathematics 2023\nHarvard University Science Center\, Lecture Hall C\nApril 7-8\, 2023\nSpeakers: \nAmol Aggarwal – Columbia University\nBhargav Bhatt – Institute for Advanced Study\, Princeton University\, & University of Michigan\nPaul Bourgade – New York University\, Courant Institute\nVesselin Dimitrov – Institute for Advanced Study & Georgia Institute of Technology\nGreta Panova – University of Southern California\n\n\n\n\nFor more information\, and to register\, please visit:\nCurrent Developments in Mathematics 2023 \n \n  \nOrganizers: David Jerison\, Paul Seidel\, Nike Sun (MIT); Denis Auroux\, Mark Kisin\, Lauren Williams\, Horng-Tzer Yau \nSponsored by the National Science Foundation\, Harvard University Mathematics\, Harvard University Center of Mathematical Sciences and Applications\, and the Massachusetts Institute of Technology. \nHarvard University is committed to maintaining a safe and healthy educational and work environment in which no member of the University community is\, on the basis of sex\, sexual orientation\, or gender identity\, excluded from participation in\, denied the benefits of\, or subjected to discrimination in any University program or activity. More information can be found here.
URL:https://cmsa.fas.harvard.edu/event/cdm-2023/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Conference,Event
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DTSTART;TZID=America/New_York:20230227T090000
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SUMMARY:Conference on Geometry and Statistics
DESCRIPTION:On Feb 27-March 1\, 2023 the CMSA will host a Conference on Geometry and Statistics. \nLocation: G10\, CMSA\, 20 Garden Street\, Cambridge MA 02138 \nOrganizing Committee:\nStephan Huckemann (Georg-August-Universität Göttingen)\nEzra Miller (Duke University)\nZhigang Yao (Harvard CMSA and Committee Chair) \nScientific Advisors:\nHorng-Tzer Yau (Harvard CMSA)\nShing-Tung Yau (Harvard CMSA) \nSpeakers: \n\nTamara Broderick (MIT)\nDavid Donoho (Stanford)\nIan Dryden (Florida International University in Miami)\nDavid Dunson (Duke)\nCharles Fefferman (Princeton)\nStefanie Jegelka (MIT)\nSebastian Kurtek (OSU)\nLizhen Lin (Notre Dame)\nSteve Marron (U North Carolina)\nEzra Miller (Duke)\nHans-Georg Mueller (UC Davis)\nNicolai Reshetikhin (UC Berkeley)\nWolfgang Polonik (UC Davis)\nAmit Singer (Princeton)\nZhigang Yao (Harvard CMSA)\nBin Yu (Berkeley)\n\nModerator: Michael Simkin (Harvard CMSA) \n  \nSCHEDULE\nMonday\, Feb. 27\, 2023 (Eastern Time) \n\n\n\n8:30 am\nBreakfast\n\n\n8:45–8:55 am\nZhigang Yao\nWelcome Remarks\n\n\n8:55–9:00 am\nShing-Tung Yau*\nRemarks\n\n\n\nMorning Session Chair: Zhigang Yao\n\n\n9:00–10:00 am\nDavid Donoho\nTitle: ScreeNOT: Exact MSE-Optimal Singular Value Thresholding in Correlated Noise \nAbstract: Truncation of the singular value decomposition is a true scientific workhorse. But where to Truncate? \nFor 55 years the answer\, for many scientists\, has been to eyeball the scree plot\, an approach which still generates hundreds of papers per year. \nI will describe ScreeNOT\, a mathematically solid alternative deriving from the many advances in Random Matrix Theory over those 55 years. Assuming a model of low-rank signal plus possibly correlated noise\, and adopting an asymptotic viewpoint with number of rows proportional to the number of columns\, we show that ScreeNOT has a surprising oracle property. \nIt typically achieves exactly\, in large finite samples\, the lowest possible MSE for matrix recovery\, on each given problem instance – i.e. the specific threshold it selects gives exactly the smallest achievable MSE loss among all possible threshold choices for that noisy dataset and that unknown underlying true low rank model. The method is computationally efficient and robust against perturbations of the underlying covariance structure. \nThe talk is based on joint work with Matan Gavish and Elad Romanov\, Hebrew University.\n\n\n10:00–10:10 am\nBreak\n\n\n10:10–11:10 am\nSteve Marron\nTitle: Modes of Variation in Non-Euclidean Spaces \nAbstract: Modes of Variation provide an intuitive means of understanding variation in populations\, especially in the case of data objects that naturally lie in non-Euclidean spaces. A variety of useful approaches to finding useful modes of variation are considered in several non-Euclidean contexts\, including shapes as data objects\, vectors of directional data\, amplitude and phase variation and compositional data.\n\n\n11:10–11:20 am\nBreak\n\n\n11:20 am–12:20 pm\nZhigang Yao\nTitle: Manifold fitting: an invitation to statistics \nAbstract: While classical statistics has dealt with observations which are real numbers or elements of a real vector space\, nowadays many statistical problems of high interest in the sciences deal with the analysis of data which consist of more complex objects\, taking values in spaces which are naturally not (Euclidean) vector spaces but which still feature some geometric structure. This manifold fitting problem can go back to H. Whitney’s work in the early 1930s (Whitney (1992))\, and finally has been answered in recent years by C. Fefferman’s works (Fefferman\, 2006\, 2005). The solution to the Whitney extension problem leads to new insights for data interpolation and inspires the formulation of the Geometric Whitney Problems (Fefferman et al. (2020\, 2021a)): Assume that we are given a set $Y \subset \mathbb{R}^D$. When can we construct a smooth $d$-dimensional submanifold $\widehat{M} \subset \mathbb{R}^D$ to approximate $Y$\, and how well can $\widehat{M}$ estimate $Y$ in terms of distance and smoothness? To address these problems\, various mathematical approaches have been proposed (see Fefferman et al. (2016\, 2018\, 2021b)). However\, many of these methods rely on restrictive assumptions\, making extending them to efficient and workable algorithms challenging. As the manifold hypothesis (non-Euclidean structure exploration) continues to be a foundational element in statistics\, the manifold fitting Problem\, merits further exploration and discussion within the modern statistical community. The talk will be partially based on a recent work Yao and Xia (2019) along with some on-going progress. Relevant reference:https://arxiv.org/abs/1909.10228\n\n\n 12:20–1:50 pm\n12:20 pm Group Photo \nfollowed by Lunch\n\n\n\nAfternoon Session Chair: Stephan Huckemann\n\n\n1:50–2:50 pm\nBin Yu*\nTitle: Interpreting Deep Neural Networks towards Trustworthiness \nAbstract: Recent deep learning models have achieved impressive predictive performance by learning complex functions of many variables\, often at the cost of interpretability. This lecture first defines interpretable machine learning in general and introduces the agglomerative contextual decomposition (ACD) method to interpret neural networks. Extending ACD to the scientifically meaningful frequency domain\, an adaptive wavelet distillation (AWD) interpretation method is developed. AWD is shown to be both outperforming deep neural networks and interpretable in two prediction problems from cosmology and cell biology. Finally\, a quality-controlled data science life cycle is advocated for building any model for trustworthy interpretation and introduce a Predictability Computability Stability (PCS) framework for such a data science life cycle.\n\n\n2:50–3:00 pm\nBreak\n\n\n3:00-4:00 pm\nHans-Georg Mueller\nTitle: Exploration of Random Objects with Depth Profiles and Fréchet Regression \nAbstract: Random objects\, i.e.\, random variables that take values in a separable metric space\, pose many challenges for statistical analysis\, as vector operations are not available in general metric spaces. Examples include random variables that take values in the space of distributions\, covariance matrices or surfaces\, graph Laplacians to represent networks\, trees and in other spaces. The increasing prevalence of samples of random objects has stimulated the development of metric statistics\, an emerging collection of statistical tools to characterize\, infer and relate samples of random objects. Recent developments include depth profiles\, which are useful for the exploration of random objects. The depth profile for any given object is the distribution of distances to all other objects (with P. Dubey\, Y. Chen 2022). \nThese distributions can then be subjected to statistical analysis. Their mutual transports lead to notions of transport ranks\, quantiles and centrality. Another useful tool is global or local Fréchet regression (with A. Petersen 2019) where random objects are responses and scalars or vectors are predictors and one aims at modeling conditional Fréchet means. Recent theoretical advances for local Fréchet regression provide a basis for object time warping (with Y. Chen 2022). These approaches are illustrated with distributional and other data.\n\n\n4:00-4:10 pm\nBreak\n\n\n4:10-5:10 pm\nStefanie Jegelka\nTitle: Some benefits of machine learning with invariances \nAbstract: In many applications\, especially in the sciences\, data and tasks have known invariances. Encoding such invariances directly into a machine learning model can improve learning outcomes\, while it also poses challenges on efficient model design. In the first part of the talk\, we will focus on the invariances relevant to eigenvectors and eigenspaces being inputs to a neural network. Such inputs are important\, for instance\, for graph representation learning. We will discuss targeted architectures that can universally express functions with the relevant invariances – sign flips and changes of basis – and their theoretical and empirical benefits. \nSecond\, we will take a broader\, theoretical perspective. Empirically\, it is known that encoding invariances into the machine learning model can reduce sample complexity. For the simplified setting of kernel ridge regression or random features\, we will discuss new bounds that illustrate two ways in which invariances can reduce sample complexity. Our results hold for learning on manifolds and for invariances to (almost) any group action\, and use tools from differential geometry. \nThis is joint work with Derek Lim\, Joshua Robinson\, Behrooz Tahmasebi\, Lingxiao Zhao\, Tess Smidt\, Suvrit Sra\, and Haggai Maron.\n\n\n\n  \n  \n  \nTuesday\, Feb. 28\, 2023 (Eastern Time) \n\n\n\n8:30-9:00 am\nBreakfast\n\n\n\nMorning Session Chair: Zhigang Yao\n\n\n9:00-10:00 am\nCharles Fefferman*\nTitle: Lipschitz Selection on Metric Spaces \nAbstract: The talk concerns the problem of finding a Lipschitz map F from a given metric space X into R^D\, subject to the constraint that F(x) must lie in a given compact convex “target” K(x) for each point x in X. Joint work with Pavel Shvartsman and with Bernat Guillen Pegueroles.\n\n\n10:00-10:10 am\nBreak\n\n\n10:10-11:10 am\nDavid Dunson\nTitle: Inferring manifolds from noisy data using Gaussian processes \nAbstract: In analyzing complex datasets\, it is often of interest to infer lower dimensional structure underlying the higher dimensional observations. As a flexible class of nonlinear structures\, it is common to focus on Riemannian manifolds. Most existing manifold learning algorithms replace the original data with lower dimensional coordinates without providing an estimate of the manifold in the observation space or using the manifold to denoise the original data. This article proposes a new methodology for addressing these problems\, allowing interpolation of the estimated manifold between fitted data points. The proposed approach is motivated by novel theoretical properties of local covariance matrices constructed from noisy samples on a manifold. Our results enable us to turn a global manifold reconstruction problem into a local regression problem\, allowing application of Gaussian processes for probabilistic manifold reconstruction. In addition to theory justifying the algorithm\, we provide simulated and real data examples to illustrate the performance. Joint work with Nan Wu – see https://arxiv.org/abs/2110.07478\n\n\n11:10-11:20 am\nBreak\n\n\n11:20 am-12:20 pm\nWolfgang Polonik\nTitle: Inference in topological data analysis \nAbstract: Topological data analysis has seen a huge increase in popularity finding applications in numerous scientific fields. This motivates the importance of developing a deeper understanding of benefits and limitations of such methods. Using this angle\, we will present and discuss some recent results on large sample inference in topological data analysis\, including bootstrap for Betti numbers and the Euler characteristics process.\n\n\n\n\n\n\n12:20–1:50 pm\nLunch\n\n\n\nAfternoon Session Chair: Stephan Huckemann\n\n\n1:50-2:50 pm\nEzra Miller\nTitle: Geometric central limit theorems on non-smooth spaces \nAbstract: The central limit theorem (CLT) is commonly thought of as occurring on the real line\, or in multivariate form on a real vector space. Motivated by statistical applications involving nonlinear data\, such as angles or phylogenetic trees\, the past twenty years have seen CLTs proved for Fréchet means on manifolds and on certain examples of singular spaces built from flat pieces glued together in combinatorial ways. These CLTs reduce to the linear case by tangent space approximation or by gluing. What should a CLT look like on general non-smooth spaces\, where tangent spaces are not linear and no combinatorial gluing or flat pieces are available? Answering this question involves figuring out appropriate classes of spaces and measures\, correct analogues of Gaussian random variables\, and how the geometry of the space (think “curvature”) is reflected in the limiting distribution. This talk provides an overview of these answers\, starting with a review of the usual linear CLT and its generalization to smooth manifolds\, viewed through a lens that casts the singular CLT as a natural outgrowth\, and concluding with how this investigation opens gateways to further advances in geometric probability\, topology\, and statistics. Joint work with Jonathan Mattingly and Do Tran.\n\n\n2:50-3:00 pm\nBreak\n\n\n3:00-4:00 pm\nLizhen Lin\nTitle: Statistical foundations of deep generative models \nAbstract: Deep generative models are probabilistic generative models where the generator is parameterized by a deep neural network. They are popular models for modeling high-dimensional data such as texts\, images and speeches\, and have achieved impressive empirical success. Despite demonstrated success in empirical performance\, theoretical understanding of such models is largely lacking. We investigate statistical properties of deep generative models from a nonparametric distribution estimation viewpoint. In the considered model\, data are assumed to be observed in some high-dimensional ambient space but concentrate around some low-dimensional structure such as a lower-dimensional manifold structure. Estimating the distribution supported on this low-dimensional structure is challenging due to its singularity with respect to the Lebesgue measure in the ambient space. We obtain convergence rates with respect to the Wasserstein metric of distribution estimators based on two methods: a sieve MLE based on the perturbed data and a GAN type estimator. Such an analysis provides insights into i) how deep generative models can avoid the curse of dimensionality and outperform classical nonparametric estimates\, and ii) how likelihood approaches work for singular distribution estimation\, especially in adapting to the intrinsic geometry of the data.\n\n\n4:00-4:10 pm\nBreak\n\n\n4:10-5:10 pm\nConversation session\n\n\n\n  \n  \n  \nWednesday\, March 1\, 2023 (Eastern Time) \n\n\n\n8:30-9:00 am\nBreakfast\n\n\n\nMorning Session Chair: Ezra Miller\n\n\n9:00-10:00 am\nAmit Singer*\nTitle: Heterogeneity analysis in cryo-EM by covariance estimation and manifold learning \nAbstract: In cryo-EM\, the 3-D molecular structure needs to be determined from many noisy 2-D tomographic projection images of randomly oriented and positioned molecules. A key assumption in classical reconstruction procedures for cryo-EM is that the sample consists of identical molecules. However\, many molecules of interest exist in more than one conformational state. These structural variations are of great interest to biologists\, as they provide insight into the functioning of the molecule. Determining the structural variability from a set of cryo-EM images is known as the heterogeneity problem\, widely recognized as one of the most challenging and important computational problem in the field. Due to high level of noise in cryo-EM images\, heterogeneity studies typically involve hundreds of thousands of images\, sometimes even a few millions. Covariance estimation is one of the earliest methods proposed for heterogeneity analysis in cryo-EM. It relies on computing the covariance of the conformations directly from projection images and extracting the optimal linear subspace of conformations through an eigendecomposition. Unfortunately\, the standard formulation is plagued by the exorbitant cost of computing the N^3 x N^3 covariance matrix. In the first part of the talk\, we present a new low-rank estimation method that requires computing only a small subset of the columns of the covariance while still providing an approximation for the entire matrix. This scheme allows us to estimate tens of principal components of real datasets in a few minutes at medium resolutions and under 30 minutes at high resolutions. In the second part of the talk\, we discuss a manifold learning approach based on the graph Laplacian and the diffusion maps framework for learning the manifold of conformations. If time permits\, we will also discuss the potential application of optimal transportation to heterogeneity analysis. Based on joint works with Joakim Andén\, Marc Gilles\, Amit Halevi\, Eugene Katsevich\, Joe Kileel\, Amit Moscovich\, and Nathan Zelesko.\n\n\n10:00-10:10 am\nBreak\n\n\n10:10-11:10 am\nIan Dryden\nTitle: Statistical shape analysis of molecule data \nAbstract: Molecular shape data arise in many applications\, for example high dimension low sample size cryo-electron microscopy (cryo-EM) data and large temporal sequences of peptides from molecular dynamics simulations. In both applications it is of interest to summarize the shape evolution of the molecules in a succinct\, low-dimensional representation. However\, Euclidean techniques such as principal components analysis (PCA) can be problematic as the data may lie far from in a flat manifold. Principal nested spheres gives a fundamentally different decomposition of data from the usual Euclidean subspace based PCA. Subspaces of successively lower dimension are fitted to the data in a backwards manner with the aim of retaining signal and dispensing with noise at each stage. We adapt the methodology to 3D sub-shape spaces and provide some practical fitting algorithms. The methodology is applied to cryo-EM data of a large sliding clamp multi-protein complex and to cluster analysis of peptides\, where different states of the molecules can be identified. Further molecular modeling tasks include resolution matching\, where coarse resolution models are back-mapped into high resolution (atomistic) structures. This is joint work with Kwang-Rae Kim\, Charles Laughton and Huiling Le.\n\n\n11:10-11:20 am\nBreak\n\n\n11:20 am-12:20 pm\nTamara Broderick\nTitle: An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions? \nAbstract: One hopes that data analyses will be used to make beneficial decisions regarding people’s health\, finances\, and well-being. But the data fed to an analysis may systematically differ from the data where these decisions are ultimately applied. For instance\, suppose we analyze data in one country and conclude that microcredit is effective at alleviating poverty; based on this analysis\, we decide to distribute microcredit in other locations and in future years. We might then ask: can we trust our conclusion to apply under new conditions? If we found that a very small percentage of the original data was instrumental in determining the original conclusion\, we might not be confident in the stability of the conclusion under new conditions. So we propose a method to assess the sensitivity of data analyses to the removal of a very small fraction of the data set. Analyzing all possible data subsets of a certain size is computationally prohibitive\, so we provide an approximation. We call our resulting method the Approximate Maximum Influence Perturbation. Our approximation is automatically computable\, theoretically supported\, and works for common estimators. We show that any non-robustness our method finds is conclusive. Empirics demonstrate that while some applications are robust\, in others the sign of a treatment effect can be changed by dropping less than 0.1% of the data — even in simple models and even when standard errors are small.\n\n\n 12:20-1:50 pm\nLunch\n\n\n\nAfternoon Session Chair: Ezra Miller\n\n\n1:50-2:50 pm\nNicolai Reshetikhin*\nTitle: Random surfaces in exactly solvable models in statistical mechanics. \nAbstract: In the first part of the talk I will be an overview of a few models in statistical mechanics where a random variable is a geometric object such as a random surface or a random curve. The second part will be focused on the behavior of such random surfaces in the thermodynamic limit and on the formation of the so-called “limit shapes”.\n\n\n2:50-3:00 pm\nBreak\n\n\n3:00-4:00 pm\nSebastian Kurtek\nTitle: Robust Persistent Homology Using Elastic Functional Data Analysis \nAbstract: Persistence landscapes are functional summaries of persistence diagrams designed to enable analysis of the diagrams using tools from functional data analysis. They comprise a collection of scalar functions such that birth and death times of topological features in persistence diagrams map to extrema of functions and intervals where they are non-zero. As a consequence\, variation in persistence diagrams is encoded in both amplitude and phase components of persistence landscapes. Through functional data analysis of persistence landscapes\, under an elastic Riemannian metric\, we show how meaningful statistical summaries of persistence landscapes (e.g.\, mean\, dominant directions of variation) can be obtained by decoupling their amplitude and phase variations. This decoupling is achieved via optimal alignment\, with respect to the elastic metric\, of the persistence landscapes. The estimated phase functions are tied to the resolution parameter that determines the filtration of simplicial complexes used to construct persistence diagrams. For a dataset obtained under geometric\, scale and sampling variabilities\, the phase function prescribes an optimal rate of increase of the resolution parameter for enhancing the topological signal in a persistence diagram. The proposed approach adds to the statistical analysis of data objects with rich structure compared to past studies. In particular\, we focus on two sets of data that have been analyzed in the past\, brain artery trees and images of prostate cancer cells\, and show that separation of amplitude and phase of persistence landscapes is beneficial in both settings. This is joint work with Dr. James Matuk (Duke University) and Dr. Karthik Bharath (University of Nottingham).\n\n\n4:00-4:10 pm\nBreak\n\n\n4:10-5:10 pm\nConversation session\n\n\n5:10-5:20 pm\nStephan Huckemann\, Ezra Miller\, Zhigang Yao\nClosing Remarks\n\n\n\n* Virtual Presentation \n\n 
URL:https://cmsa.fas.harvard.edu/event/geometry-and-statistics/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220826T090000
DTEND;TZID=America/New_York:20220826T130000
DTSTAMP:20260504T060850
CREATED:20230705T044827Z
LAST-MODIFIED:20250328T145239Z
UID:10000058-1661504400-1661518800@cmsa.fas.harvard.edu
SUMMARY:Big Data Conference 2022
DESCRIPTION:On August 26\, 2022 the CMSA hosted our eighth annual Conference on Big Data. The Big Data Conference features speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. \nThe 2022 Big Data Conference took place virtually on Zoom. \nOrganizers: \n\nScott Duke Kominers\, MBA Class of 1960 Associate Professor\, Harvard Business\nHorng-Tzer Yau\, Professor of Mathematics\, Harvard University\nSergiy Verstyuk\, CMSA\, Harvard University\n\nSpeakers: \n\nXiaohong Chen\, Yale\nMiles Cranmer\, Princeton\nJessica Jeffers\, University of Chicago\nDan Roberts\, MIT\n\nSchedule \n\n\n\n\n9:00 am\nConference Organizers\nIntroduction and Welcome\n\n\n9:10 am – 9:55 am\nXiaohong Chen\nTitle: On ANN optimal estimation and inference for policy functionals of nonparametric conditional moment restrictions \nAbstract:  Many causal/policy parameters of interest are expectation functionals of unknown infinite-dimensional structural functions identified via conditional moment restrictions. Artificial Neural Networks (ANNs) can be viewed as nonlinear sieves that can approximate complex functions of high dimensional covariates more effectively than linear sieves. In this talk we present ANN optimal estimation and inference on  policy functionals\, such as average elasticities or value functions\, of unknown structural functions of endogenous covariates. We provide ANN efficient estimation and optimal t based confidence interval for regular policy functionals such as average derivatives in nonparametric instrumental variables regressions. We also present ANN quasi likelihood ratio based inference for possibly irregular policy functionals of general nonparametric conditional moment restrictions (such as quantile instrumental variables models or Bellman equations) for time series data. We conduct intensive Monte Carlo studies to investigate computational issues with ANN based optimal estimation and inference in economic structural models with endogeneity. For economic data sets that do not have very high signal to noise ratios\, there are current gaps between theoretical advantage of ANN approximation theory vs inferential performance in finite samples.\nSome of the results are applied to efficient estimation and optimal inference for average price elasticity in consumer demand and BLP type demand. \nThe talk is based on two co-authored papers:\n(1) Efficient Estimation of Average Derivatives in NPIV Models: Simulation Comparisons of Neural Network Estimators\n(Authors: Jiafeng Chen\, Xiaohong Chen and Elie Tamer)\nhttps://arxiv.org/abs/2110.06763 \n(2) Neural network Inference on Nonparametric conditional moment restrictions with weakly dependent data\n(Authors: Xiaohong Chen\, Yuan Liao and Weichen Wang). \nView/Download Lecture Slides (pdf)\n\n\n10:00 am – 10:45 am\nJessica Jeffers\nTitle: Labor Reactions to Credit Deterioration: Evidence from LinkedIn Activity \nAbstract: We analyze worker reactions to their firms’ credit deterioration. Using weekly networking activity on LinkedIn\, we show workers initiate more connections immediately following a negative credit event\, even at firms far from bankruptcy. Our results suggest that workers are driven by concerns about both unemployment and future prospects at their firm. Heightened networking activity is associated with contemporaneous and future departures\, especially at financially healthy firms. Other negative events like missed earnings and equity downgrades do not trigger similar reactions. Overall\, our results indicate that the build-up of connections triggered by credit deterioration represents a source of fragility for firms.\n\n\n10:50 am – 11:35 am\nMiles Cranmer\nTitle: Interpretable Machine Learning for Physics \nAbstract: Would Kepler have discovered his laws if machine learning had been around in 1609? Or would he have been satisfied with the accuracy of some black box regression model\, leaving Newton without the inspiration to discover the law of gravitation? In this talk I will explore the compatibility of industry-oriented machine learning algorithms with discovery in the natural sciences. I will describe recent approaches developed with collaborators for addressing this\, based on a strategy of “translating” neural networks into symbolic models via evolutionary algorithms. I will discuss the inner workings of the open-source symbolic regression library PySR (github.com/MilesCranmer/PySR)\, which forms a central part of this interpretable learning toolkit. Finally\, I will present examples of how these methods have been used in the past two years in scientific discovery\, and outline some current efforts. \nView/Download Lecture Slides (pdf) \n\n\n11:40 am – 12:25 pm\nDan Roberts\nTitle: A Statistical Model of Neural Scaling Laws \nAbstract: Large language models of a huge number of parameters and trained on near internet-sized number of tokens have been empirically shown to obey “neural scaling laws” for which their performance behaves predictably as a power law in either parameters or dataset size until bottlenecked by the other resource. To understand this better\, we first identify the necessary properties allowing such scaling laws to arise and then propose a statistical model — a joint generative data model and random feature model — that captures this neural scaling phenomenology. By solving this model using tools from random matrix theory\, we gain insight into (i) the statistical structure of datasets and tasks that lead to scaling laws (ii) how nonlinear feature maps\, i.e the role played by the deep neural network\, enable scaling laws when trained on these datasets\, and (iii) how such scaling laws can break down\, and what their behavior is when they do. A key feature is the manner in which the power laws that occur in the statistics of natural datasets are translated into power law scalings of the test loss\, and how the finite extent of such power laws leads to both bottlenecks and breakdowns. \nView/Download Lecture Slides (pdf) \n \n\n\n12:30 pm\nConference Organizers\nClosing Remarks\n\n\n\n\n  \nInformation about last year’s conference can be found here.
URL:https://cmsa.fas.harvard.edu/event/big-data-conference-2022/
LOCATION:Virtual
CATEGORIES:Big Data Conference,Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Big-Data-2022_web.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220730T090000
DTEND;TZID=America/New_York:20220801T134500
DTSTAMP:20260504T060850
CREATED:20230705T041718Z
LAST-MODIFIED:20250305T170940Z
UID:10000056-1659171600-1659361500@cmsa.fas.harvard.edu
SUMMARY:Advances in Mathematical Physics
DESCRIPTION:A Conference in Honor of Elliott H. Lieb on his 90th Birthday\nOn July 30 – Aug 1\, 2022 the Harvard Mathematics Department and the CMSA co-hosted a birthday conference in honor of Elliott Lieb. \nThis meeting highlights Elliott’s vast contribution to math and physics. Additionally\, this meeting features Prof. Lieb’s more recent impact in strong subadditivity of entropy and integrable systems (ice model\, Temperley-Lieb algebra etc.). \nVenue:\nJuly 30–31\, 2022: Hall B\, Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138\nAugust 1\, 2022: Hall C\, Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138 \nSchedule (pdf) \nOrganizers:\nMichael Aizenman\, Princeton University\nJoel Lebowitz\, Rutgers University\nRuedi Seiler\, Technische Universität Berlin\nHerbert Spohn\, Technical University of Munich\nHorng-Tzer Yau\, Harvard University\nShing-Tung Yau\, Harvard University\nJakob Yngvason\, University of Vienna \nSPEAKERS:\nRafael Benguria\, Pontificia Universidad Catolica de Chile\nEric Carlen\, Rutgers University\nPhilippe Di Francesco\, University of Illinois\nHugo Duminil-Copin\, IHES\nLászló Erdös\, Institute of Science and Technology Austria\nRupert Frank\, Ludwig Maximilian University of Munich\nJürg Fröhlich\, ETH Zurich\nAlessandro Giuliani\, Università degli Studi Roma Tre\nBertrand Halperin\, Harvard University\nKlaus Hepp\, Institute for Theoretical Physics\, ETH Zurich\nSabine Jansen\, Ludwig Maximilian University of Munich\nMathieu Lewin\, Université Paris-Dauphine\nBruno Nachtergaele\, The University of California\, Davis\nYoshiko Ogata\, University of Tokyo\nRon Peled\, Tel Aviv University\nBenjamin Schlein\, University of Zurich\nRobert Seiringer\, Institute of Science and Technology Austria\nJan Philip Solovej\, University of Copenhagen\nHal Tasaki\, Gakushuin University\nSimone Warzel\, Technical University of Munich\nJun Yin\, The University of California\, Los Angeles \n 
URL:https://cmsa.fas.harvard.edu/event/advances-in-mathematical-physics/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Elliott-Lieb-conference-2022_banner-2-1536x734-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220621T090000
DTEND;TZID=America/New_York:20220624T170000
DTSTAMP:20260504T060850
CREATED:20230706T183302Z
LAST-MODIFIED:20250305T175141Z
UID:10000894-1655802000-1656090000@cmsa.fas.harvard.edu
SUMMARY:Joint BHI/CMSA Conference on Flat Holography
DESCRIPTION:On June 21–24\, 2022\, the Harvard Black Hole Initiative and the CMSA hosted the Joint BHI/CMSA Conference on Flat Holography (and related topics). \nThe recent discovery of infinitely-many soft symmetries for all quantum theories of gravity in asymptotically flat space has provided a promising starting point for a bottom-up construction of a holographic dual for the real world. Recent developments have brought together previously disparate studies of soft theorems\, asymptotic symmetries\, twistor theory\, asymptotically flat black holes and their microscopic duals\, self-dual gravity\, and celestial scattering amplitudes\, and link directly to AdS/CFT. \nThe conference was held in room G10 of the CMSA\, 20 Garden Street\, Cambridge\, MA. \nOrganizers: \n\nDaniel Kapec\, CMSA\nAndrew Strominger\, BHI\nShing-Tung Yau\, Harvard & Tsinghua\n\nConfirmed Speakers: \n\nNima Arkani-Hamed\, IAS\nShamik Banerjee\, Bhubaneswar\, Inst. Phys.\nMiguel Campiglia\, Republica U.\, Montevido\nGeoffrey Compere\, Brussels\nLaura Donnay\, Vienna\nNetta Engelhardt\, MIT\nLaurent Freidel\, Perimeter\nAlex Lupsasca\, Princeton\nJuan Maldacena\, IAS\nLionel Mason\, Oxford\nNatalie Paquette\, U. Washington\nSabrina Pasterski\, Princeton/Perimeter\nAndrea Puhm\, Ecole Polytechnique\nAna-Maria Raclariu\, Perimeter\nMarcus Spradlin\, Brown\nTomasz Taylor\, Northeastern\nHerman Verlinde\, Princeton\nAnastasia Volovich\, Brown\nBin Zhu\, Northeastern\n\nShort talks by: Gonçalo Araujo-Regado (Cambridge)\, Adam Ball (Harvard)\, Eduardo Casali (Harvard)\, Jordan Cotler (Harvard)\, Erin Crawley (Harvard)\, Stéphane Detournay (Brussels)\, Alfredo Guevara (Harvard)\, Temple He (UC Davis)\, Elizabeth Himwich (Harvard)\, Yangrui Hu (Brown)\, Daniel Kapec (Harvard)\, Rifath Khan (Cambridge)\, Albert Law (Harvard)\, Luke Lippstreu (Brown)\, Noah Miller (Harvard)\, Sruthi Narayanan (Harvard)\, Lecheng Ren (Brown)\, Francisco Rojas (UAI)\, Romain Ruzziconi (Vienna)\, Andrew Strominger (Harvard)\, Adam Tropper (Harvard)\, Tianli Wang (Harvard)\, Walker Melton (Harvard) \n\n\nSchedule\nMonday\, June 20\, 2022 \n\n\n\n\n\nArrival\n\n\n7:00–9:00 pm\nWelcome Reception at Andy’s residence\n\n\n\n\n  \nTuesday\, June 21\, 2022 \n\n\n\n\n9:00–9:30 am\nBreakfast\nlight breakfast provided\n\n\n\nMorning Session\nChair: Dan Kapec\n\n\n9:30–10:00 am\nHerman Verlinde\nTitle: Comments on Celestial Dynamics\n\n\n10:00–10:30 am\nJuan Maldacena\nTitle: What happens when you spend too much time looking at supersymmetric\nblack holes?\n\n\n10:30–11:00\nCoffee break\n\n\n\n11:00–11:30 am\nMiguel Campiglia\nTitle: Asymptotic symmetries and loop corrections to soft theorems\n\n\n11:30–12:00 pm\nGeoffrey Compere\nTitle: Metric reconstruction from $Lw_{1+\infty}$ multipoles \nAbstract: The most general vacuum solution to Einstein’s field equations with no incoming radiation can be constructed perturbatively from two infinite sets of canonical multipole moments\, which are found to be exchanged under gravitational electric-magnetic duality at the non-linear level. We demonstrate that in non-radiative regions such spacetimes are completely determined by a set of conserved celestial charges\, which uniquely label transitions among non-radiative regions caused by radiative processes. The algebra of the conserved celestial charges is derived from the real $Lw_{1+\infty}$ algebra. The celestial charges are expressed in terms of multipole moments\, which allows to holographically reconstruct the metric in de Donder\, Newman-Unti or Bondi gauge outside of sources.\n\n\n12:00–2:00 pm\nLunch break\n\n\n\n\nAfternoon Session\nChair: Eduardo Casali\n\n\n2:00–2:30 pm\nNatalie Paquette\nTitle: New thoughts on old gauge amplitudes\n\n\n2:30–3:00 pm\nLionel Mason\nTitle: An open sigma model for celestial gravity \nAbstract: A global twistor construction for conformally self-dual split signature metrics on $S2\times S2$  was developed 15 years ago by Claude LeBrun and the speaker.  This encodes the conformal metric into the location of a finite deformation of the real twistor space inside the flat complex twistor space\, $\mathbb{CP}3$. This talk adapts the construction to construct global SD Einstein metrics from conformal boundary data and perturbations around the self-dual sector.  The construction entails determining a family of holomorphic discs in $\mathbb{CP}3$ whose boundaries lie on the deformed real slice and the (chiral) sigma model controls these discs in the Einstein case and provides amplitude formulae.\n\n\n3:00–3:30 pm\nCoffee break\n\n\n\n3:30–4:30 pm\nShort Talks\nDaniel Kapec: Soft Scalars and the Geometry of the Space of Celestial CFTs \nAlbert Law: Soft Scalars and the Geometry of the Space of Celestial CFTs \nSruthi Narayanan: Soft Scalars and the Geometry of the Space of Celestial CFTs \nStéphane Detournay: Non-conformal symmetries and near-extremal black holes \nFrancisco Rojas: Celestial string amplitudes beyond tree level \nTemple He: An effective description of energy transport from holography\n\n\n4:30–5:00 pm\nNima Arkani-Hamed\n(Dual) surfacehedra and flow particles know about strings\n\n\n\n\n  \nWednesday\, June 22\, 2022 \n\n\n\n\n9:00–9:30 am\nBreakfast\nlight breakfast provided\n\n\n\nMorning Session\nChair: Alfredo Guevara\n\n\n9:30–10:00 am\nLaurent Freidel\nTitle: Higher spin symmetry in gravity \nAbstract: In this talk\, I will review how the gravitational conservation laws at infinity reveal a tower of symmetry charges in an asymptotically flat spacetime.\nI will show how the conservation laws\, at spacelike infinity\, give a tower of soft theorems that connect to the ones revealed by celestial holography.\nI’ll present the expression for the symmetry charges in the radiative phase space\, which opens the way to reveal the structure of the algebra beyond the positive helicity sector. Then\, if time permits I’ll browse through many questions that these results raise:\nsuch as the nature of the spacetime symmetry these charges represent\, the nature of the relationship with multipole moments\, and the insights their presence provides for quantum gravity.\n\n\n10:00–10:30 am\nAna-Maria Raclariu\nTitle: Eikonal approximation in celestial CFT\n\n\n10:30–11:00 am\nCoffee break\n\n\n\n11:00–11:30 am\nAnastasia Volovich\nTitle: Effective Field Theories with Celestial Duals\n\n\n11:30–12:00 pm\nMarcus Spradlin\nTitle: Loop level gluon OPE’s in celestial holography\n\n\n12:00–2:00 pm\nLunch break\n\n\n\n\nAfternoon Session\nChair: Chiara Toldo\n\n\n2:00–2:30 pm\nNetta Engelhardt\nTitle: Wormholes from entanglement: true or false?\n\n\n2:30–3:00 pm\nShort Talks\nLuke Lippstreu: Loop corrections to the OPE of celestial gluons \nYangrui Hu: Light transforms of celestial amplitudes \nLecheng Ren: All-order OPE expansion of celestial gluon and graviton primaries from MHV amplitudes\n\n\n3:00–3:30 pm\nCoffee break\n\n\n\n3:30–4:30 pm\nShort Talks\nNoah Miller: C Metric Thermodynamics \nErin Crawley: Kleinian black holes \nRifath Khan: Cauchy Slice Holography: A New AdS/CFT Dictionary \nGonçalo Araujo-Regado: Cauchy Slice Holography: A New AdS/CFT Dictionary \nTianli Wang: Soft Theorem in the BFSS Matrix Model \nAdam Tropper: Soft Theorem in the BFSS Matrix Model\n\n\n7:00–9:00 pm\nBanquet\nMaharaja Restaurant\, 57 JFK Street\, Cambridge\, MA\n\n\n\n\n  \nThursday\, June 23\, 2022 \n\n\n\n\n9:00–9:30 am\nBreakfast\nlight breakfast provided\n\n\n\nMorning Session\nChair: Jordan Cotler\n\n\n9:30–10:00 am\nLaura Donnay\nTitle: A Carrollian road to flat space holography\n\n\n10:00–10:30 am\nAndrea Puhm\nTitle: Celestial wave scattering on Kerr-Schild backgrounds\n\n\n10:30–11:00 am\nCoffee break\n\n\n\n11:00–11:30 am\nSabrina Pasterski\nTitle: Mining Celestial Symmetries \nAbstract: The aim of this talk is to delve into the common thread that ties together recent work with H. Verlinde\, L. Donnay\, A. Puhm\, and S. Banerjee exploring\, explaining\, and exploiting the symmetries encoded in the conformally soft sector. \nCome prepared to debate the central charge\, loop corrections\, contour prescriptions\, and orders of limits!\n\n\n11:30–12:00 pm\nShamik Banerjee\nTitle: Virasoro and other symmetries in CCFT \nAbstract:  In this talk I will briefly describe my ongoing work with Sabrina Pasterski. In this work we revisit the standard construction of the celestial stress tensor as a shadow of the subleading conformally soft graviton.  In its original formulation\, we find that there is an obstruction to reproducing the expected $TT$ OPE in the double soft limit. This obstruction is related to the existence of the $SL_2$ current algebra symmetry of the CCFT. We propose a modification to the definition of the stress tensor which circumvents this obstruction and also discuss its implications for the existence of other current algebra (w_{1+\infty}) symmetries in CCFT.\n\n\n12:00–2:00 pm\nLunch break\n\n\n\n\nAfternoon Session\nChair: Albert Law\n\n\n2:00–2:30 pm\nTomasz Taylor\nTitle: Celestial Yang-Mills amplitudes and D=4 conformal blocks\n\n\n2:30–3:00 pm\nBin Zhu\nTitle:  Single-valued correlators and Banerjee-Ghosh equations \nAbstract:  Low-point celestial amplitudes are plagued with singularities resulting from spacetime translation. We consider a marginal deformation of the celestial CFT which is realized by coupling Yang-Mills theory to a background dilaton field\, with the (complex) dilaton source localized on the celestial sphere. This picture emerges from the physical interpretation of the solutions of the system of differential equations discovered by Banerjee and Ghosh. We show that the solutions can be written as Mellin transforms of the amplitudes evaluated in such a dilaton background. The resultant three-gluon and four-gluon amplitudes are single-valued functions of celestial coordinates enjoying crossing symmetry and all other properties expected from standard CFT correlators.\n\n\n3:00–3:30 pm\nCoffee break\n\n\n\n3:30–4:00 pm\nAlex Lupsasca\nTitle: Holography of the Photon Ring\n\n\n4:00–5:30 pm\nShort Talks\nElizabeth Himwich: Celestial OPEs and w(1+infinity) symmetry of massless and massive amplitudes \nAdam Ball: Perturbatively exact $w_{1+\infty}$ asymptotic symmetry of quantum self-dual gravity \nRomain Ruzziconi: A Carrollian Perspective on Celestial Holography \nJordan Cotler: Soft Gravitons in 3D \nAlfredo Guevara: Comments on w_1+\inf \nAndrew Strominger: Top-down celestial holograms \nEduardo Casali: Celestial amplitudes as AdS-Witten diagrams \nWalker Melton: Top-down celestial holograms\n\n\n\n\n  \nFriday\, June 24\, 2022 \n\n\n\n\n9:00–9:30 am\nBreakfast\n\n\n9:30–12:30 pm\nOpen Discussion\n\n\n12:30–2:30 pm\nLunch provided at the BHI\n\n\n\nDeparture\n\n\n\n\n 
URL:https://cmsa.fas.harvard.edu/event/joint-bhi-cmsa-conference-on-flat-holography/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Flat-Holography_2022_small.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220606T090000
DTEND;TZID=America/New_York:20220608T170000
DTSTAMP:20260504T060850
CREATED:20230706T182850Z
LAST-MODIFIED:20250305T172950Z
UID:10000893-1654506000-1654707600@cmsa.fas.harvard.edu
SUMMARY:Symposium on Foundations of Responsible Computing (FORC)
DESCRIPTION:On June 6-8\, 2022\, the CMSA hosted the 3rd annual Symposium on Foundations of Responsible Computing (FORC). \nThe Symposium on Foundations of Responsible Computing (FORC) is a forum for mathematical research in computation and society writ large.  The Symposium aims to catalyze the formation of a community supportive of the application of theoretical computer science\, statistics\, economics and other relevant analytical fields to problems of pressing and anticipated societal concern. \nOrganizers: Cynthia Dwork\, Harvard SEAS | Omer Reingold\, Stanford | Elisa Celis\, Yale \nSchedule\nJune 6\, 2022 \n\n\n\n\n9:15 am–10:15 am\nOpening Remarks \nKeynote Speaker: Caroline Nobo\, Yale University\nTitle: From Theory to Impact: Why Better Data Systems are Necessary for Criminal Legal Reform \nAbstract: This talk will dive into the messy\, archaic\, and siloed world of local criminal justice data in America. We will start with a 30\,000 foot discussion about the current state of criminal legal data systems\, then transition to the challenges of this broken paradigm\, and conclude with a call to measure new things – and to measure them better! This talk will leave you with an understanding of criminal justice data infrastructure and transparency in the US\, and will discuss how expensive case management software and other technology are built on outdated normative values which impede efforts to reform the system. The result is an infuriating paradox: an abundance of tech products built without theoretical grounding\, in a space rich with research and evidence.\n\n\n10:15 am–10:45 am\nCoffee Break\n\n\n\n10:45 am–12:15 pm\nPaper Session 1\nSession Chair: Ruth Urner\n\n\n\nGeorgy Noarov\, University of Pennsylvania\nTitle: Online Minimax Multiobjective Optimization \nAbstract: We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. The learner’s objective is to minimize the maximum cumulative loss over all coordinates. We give a simple algorithm that lets the learner do almost as well as if she knew the adversary’s actions in advance. We demonstrate the power of our framework by using it to (re)derive optimal bounds and efficient algorithms across a variety of domains\, ranging from multicalibration to a large set of no-regret algorithms\, to a variant of Blackwell’s approachability theorem for polytopes with fast convergence rates. As a new application\, we show how to “(multi)calibeat” an arbitrary collection of forecasters — achieving an exponentially improved dependence on the number of models we are competing against\, compared to prior work.\n\n\n\nMatthew Eichhorn\, Cornell University\nTitle: Mind your Ps and Qs: Allocation with Priorities and Quotas \nAbstract: In many settings\, such as university admissions\, the rationing of medical supplies\, and the assignment of public housing\, decision-makers use normative criteria (ethical\, financial\, legal\, etc.) to justify who gets an allocation. These criteria can often be translated into quotas for the number of units available to particular demographics and priorities over agents who qualify in each demographic. Each agent may qualify in multiple categories at different priority levels\, so many allocations may conform to a given set of quotas and priorities. Which of these allocations should be chosen? In this talk\, I’ll formalize this reserve allocation problem and motivate Pareto efficiency as a natural desideratum. I’ll present an algorithm to locate efficient allocations that conform to the quota and priority constraints. This algorithm relies on beautiful techniques from integer and linear programming\, and it is both faster and more straightforward than existing techniques in this space. Moreover\, its clean formulation allows for further refinement\, such as the secondary optimization of some heuristics for fairness.\n\n\n\nHaewon Jeong\, Harvard University\nTitle: Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values \nAbstract: We investigate the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the literature\, most of them require a complete training set as input. In practice\, data can have missing values\, and data missing patterns can depend on group attributes (e.g. gender or race). Simply applying off-the-shelf fair learning algorithms to an imputed dataset may lead to an unfair model. In this paper\, we first theoretically analyze different sources of discrimination risks when training with an imputed dataset. Then\, we propose an integrated approach based on decision trees that does not require a separate process of imputation and learning. Instead\, we train a tree with missing incorporated as attribute (MIA)\, which does not require explicit imputation\, and we optimize a fairness-regularized objective function. We demonstrate that our approach outperforms existing fairness intervention methods applied to an imputed dataset\, through several experiments on real-world datasets.\n\n\n\nEmily Diana\, University of Pennsylvania\nTitle: Multiaccurate Proxies for Downstream Fairness \nAbstract: We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time — in other words\, how can we train a model to be fair by race when we don’t have data about race? We adopt a fairness pipeline perspective\, in which an “upstream” learner that does have access to the sensitive features will learn a proxy model for these features from the other attributes. The goal of the proxy is to allow a general “downstream” learner — with minimal assumptions on their prediction task — to be able to use the proxy to train a model that is fair with respect to the true sensitive features. We show that obeying multiaccuracy constraints with respect to the downstream model class suffices for this purpose\, provide sample- and oracle efficient-algorithms and generalization bounds for learning such proxies\, and conduct an experimental evaluation. In general\, multiaccuracy is much easier to satisfy than classification accuracy\, and can be satisfied even when the sensitive features are hard to predict.\n\n\n12:15 pm–1:45 pm\nLunch Break\n\n\n\n1:45–3:15 pm\nPaper Session 2\nSession Chair: Guy Rothblum\n\n\n\nElbert Du\, Harvard University\nTitle: Improved Generalization Guarantees in Restricted Data Models \nAbstract: Differential privacy is known to protect against threats to validity incurred due to adaptive\, or exploratory\, data analysis — even when the analyst adversarially searches for a statistical estimate that diverges from the true value of the quantity of interest on the underlying population. The cost of this protection is the accuracy loss incurred by differential privacy. In this work\, inspired by standard models in the genomics literature\, we consider data models in which individuals are represented by a sequence of attributes with the property that where distant attributes are only weakly correlated. We show that\, under this assumption\, it is possible to “re-use” privacy budget on different portions of the data\, significantly improving accuracy without increasing the risk of overfitting.\n\n\n\nRuth Urner\, York University\nTitle: Robustness Should not be at Odds with Accuracy \nAbstract: The phenomenon of adversarial examples in deep learning models has caused substantial concern over their reliability and trustworthiness: in many instances an imperceptible perturbation can falsely flip a neural network’s prediction. Applied research in this area has mostly focused on developing novel adversarial attack strategies or building better defenses against such. It has repeatedly been pointed out that adversarial robustness may be in conflict with requirements for high accuracy. In this work\, we take a more principled look at modeling the phenomenon of adversarial examples. We argue that deciding whether a model’s label change under a small perturbation is justified\, should be done in compliance with the underlying data-generating process. Through a series of formal constructions\, systematically analyzing the the relation between standard Bayes classifiers and robust-Bayes classifiers\, we make the case for adversarial robustness as a locally adaptive measure. We propose a novel way defining such a locally adaptive robust loss\, show that it has a natural empirical counterpart\, and develop resulting algorithmic guidance in form of data-informed adaptive robustness radius. We prove that our adaptive robust data-augmentation maintains consistency of 1-nearest neighbor classification under deterministic labels and thereby argue that robustness should not be at odds with accuracy.\n\n\n\nSushant Agarwal\, University of Waterloo\nTitle: Towards the Unification and Robustness of Perturbation and Gradient Based Explanations \nAbstract: As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice\, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work\, we analyze two popular post hoc interpretation techniques: SmoothGrad which is a gradient based method\, and a variant of LIME which is a perturbation based method. More specifically\, we derive explicit closed form expressions for the explanations output by these two methods and show that they both converge to the same explanation in expectation\, i.e.\, when the number of perturbed samples used by these methods is large. We then leverage this connection to establish other desirable properties\, such as robustness and linearity\, for these techniques. We also derive finite sample complexity bounds for the number of perturbations required for these methods to converge to their expected explanation. Finally\, we empirically validate our theory using extensive experimentation on both synthetic and real world datasets.\n\n\n\nTijana Zrnic\, University of California\, Berkeley\nTitle: Regret Minimization with Performative Feedback \nAbstract: In performative prediction\, the deployment of a predictive model triggers a shift in the data distribution. As these shifts are typically unknown ahead of time\, the learner needs to deploy a model to get feedback about the distribution it induces. We study the problem of finding near-optimal models under performativity while maintaining low regret. On the surface\, this problem might seem equivalent to a bandit problem. However\, it exhibits a fundamentally richer feedback structure that we refer to as performative feedback: after every deployment\, the learner receives samples from the shifted distribution rather than only bandit feedback about the reward. Our main contribution is regret bounds that scale only with the complexity of the distribution shifts and not that of the reward function. The key algorithmic idea is careful exploration of the distribution shifts that informs a novel construction of confidence bounds on the risk of unexplored models. The construction only relies on smoothness of the shifts and does not assume convexity. More broadly\, our work establishes a conceptual approach for leveraging tools from the bandits literature for the purpose of regret minimization with performative feedback.\n\n\n3:15 pm–3:45 pm\nCoffee Break\n\n\n\n3:45 pm–5:00 pm\nPanel Discussion\nTitle: What is Responsible Computing? \nPanelists: Jiahao Chen\, Cynthia Dwork\, Kobbi Nissim\, Ruth Urner \nModerator: Elisa Celis\n\n\n\n\n  \nJune 7\, 2022 \n\n\n\n\n9:15 am–10:15 am\nKeynote Speaker: Isaac Kohane\, Harvard Medical School\nTitle: What’s in a label? The case for and against monolithic group/ethnic/race labeling for machine learning \nAbstract: Populations and group labels have been used and abused for thousands of years. The scale at which AI can incorporate such labels into its models and the ways in which such models can be misused are cause for significant concern. I will describe\, with examples drawn from experiments in precision medicine\, the task dependence of how underserved and oppressed populations can be both harmed and helped by the use of group labels. The source of the labels and the utility models underlying their use will be particularly emphasized.\n\n\n10:15 am–10:45 am\nCoffee Break\n\n\n\n10:45 am–12:15 pm\nPaper Session 3\nSession Chair: Ruth Urner\n\n\n\nRojin Rezvan\, University of Texas at Austin\nTitle: Individually-Fair Auctions for Multi-Slot Sponsored Search \nAbstract: We design fair-sponsored search auctions that achieve a near-optimal tradeoff between fairness and quality. Our work builds upon the model and auction design of Chawla and Jagadeesan\, who considered the special case of a single slot. We consider sponsored search settings with multiple slots and the standard model of click-through rates that are multiplicatively separable into an advertiser-specific component and a slot-specific component. When similar users have similar advertiser-specific click-through rates\, our auctions achieve the same near-optimal tradeoff between fairness and quality. When similar users can have different advertiser-specific preferences\, we show that a preference-based fairness guarantee holds. Finally\, we provide a computationally efficient algorithm for computing payments for our auctions as well as those in previous work\, resolving another open direction from Chawla and Jagadeesan.\n\n\n\nJudy Hanwen Shen\, Stanford\nTitle: Leximax Approximations and Representative Cohort Selection \nAbstract: Finding a representative cohort from a broad pool of candidates is a goal that arises in many contexts such as choosing governing committees and consumer panels. While there are many ways to define the degree to which a cohort represents a population\, a very appealing solution concept is lexicographic maximality (leximax) which offers a natural (pareto-optimal like) interpretation that the utility of no population can be increased without decreasing the utility of a population that is already worse off. However\, finding a leximax solution can be highly dependent on small variations in the utility of certain groups. In this work\, we explore new notions of approximate leximax solutions with three distinct motivations: better algorithmic efficiency\, exploiting significant utility improvements\, and robustness to noise. Among other definitional contributions\, we give a new notion of an approximate leximax that satisfies a similarly appealing semantic interpretation and relate it to algorithmically-feasible approximate leximax notions. When group utilities are linear over cohort candidates\, we give an efficient polynomial-time algorithm for finding a leximax distribution over cohort candidates in the exact as well as in the approximate setting. Furthermore\, we show that finding an integer solution to leximax cohort selection with linear utilities is NP-Hard.\n\n\n\nJiayuan Ye\,\nNational University of Singapore\nTitle: Differentially Private Learning Needs Hidden State (or Much Faster Convergence) \nAbstract: Differential privacy analysis of randomized learning algorithms typically relies on composition theorems\, where the implicit assumption is that the internal state of the iterative algorithm is revealed to the adversary. However\, by assuming hidden states for DP algorithms (when only the last-iterate is observable)\, recent works prove a converging privacy bound for noisy gradient descent (on strongly convex smooth loss function) that is significantly smaller than composition bounds after a few epochs. In this talk\, we extend this hidden-state analysis to various stochastic minibatch gradient descent schemes (such as under “shuffle and partition” and “sample without replacement”)\, by deriving novel bounds for the privacy amplification by random post-processing and subsampling. We prove that\, in these settings\, our privacy bound is much smaller than composition for training with a large number of iterations (which is the case for learning from high-dimensional data). Our converging privacy analysis\, thus\, shows that differentially private learning\, with a tight bound\, needs hidden state privacy analysis or a fast convergence. To complement our theoretical results\, we present experiments for training classification models on MNIST\, FMNIST and CIFAR-10 datasets\, and observe a better accuracy given fixed privacy budgets\, under the hidden-state analysis.\n\n\n\nMahbod Majid\, University of Waterloo\nTitle: Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism \nAbstract: We give the first polynomial-time algorithm to estimate the mean of a d-variate probability distribution from O(d) independent samples (up to logarithmic factors) subject to pure differential privacy. \nOur main technique is a new approach to use the powerful Sum of Squares method (SoS) to design differentially private algorithms. SoS proofs to algorithms is a key theme in numerous recent works in high-dimensional algorithmic statistics – estimators which apparently require exponential running time but whose analysis can be captured by low-degree Sum of Squares proofs can be automatically turned into polynomial-time algorithms with the same provable guarantees. We demonstrate a similar proofs to private algorithms phenomenon: instances of the workhorse exponential mechanism which apparently require exponential time but which can be analyzed with low-degree SoS proofs can be automatically turned into polynomial-time differentially private algorithms. We prove a meta-theorem capturing this phenomenon\, which we expect to be of broad use in private algorithm design.\n\n\n12:15 pm–1:45 pm\nLunch Break\n\n\n\n1:45–3:15 pm\nPaper Session 4\nSession Chair: Kunal Talwar\n\n\n\nKunal Talwar\,\nApple\nTitle: Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation \nAbstract: Computing the noisy sum of real-valued vectors is an important primitive in differentially private learning and statistics. In private federated learning applications\, these vectors are held by client devices\, leading to a distributed summation problem. Standard Secure Multiparty Computation (SMC) protocols for this problem are susceptible to poisoning attacks\, where a client may have a large influence on the sum\, without being detected.\nIn this work\, we propose a poisoning-robust private summation protocol in the multiple-server setting\, recently studied in PRIO. We present a protocol for vector summation that verifies that the Euclidean norm of each contribution is approximately bounded. We show that by relaxing the security constraint in SMC to a differential privacy like guarantee\, one can improve over PRIO in terms of communication requirements as well as the client-side computation. Unlike SMC algorithms that inevitably cast integers to elements of a large finite field\, our algorithms work over integers/reals\, which may allow for additional efficiencies.\n\n\n\nGiuseppe Vietri\, University of Minnesota\nTitle: Improved Regret for Differentially Private Exploration in Linear MDP \nAbstract: We study privacy-preserving exploration in sequential decision-making for environments that rely on sensitive data such as medical records. In particular\, we focus on solving the problem of reinforcement learning (RL) subject to the constraint of (joint) differential privacy in the linear MDP setting\, where both dynamics and rewards are given by linear functions. Prior work on this problem due to Luyo et al. (2021) achieves a regret rate that has a dependence of O(K^{3/5}) on the number of episodes K. We provide a private algorithm with an improved regret rate with an optimal dependence of O(K^{1/2}) on the number of episodes. The key recipe for our stronger regret guarantee is the adaptivity in the policy update schedule\, in which an update only occurs when sufficient changes in the data are detected. As a result\, our algorithm benefits from low switching cost and only performs O(log(K)) updates\, which greatly reduces the amount of privacy noise. Finally\, in the most prevalent privacy regimes where the privacy parameter ? is a constant\, our algorithm incurs negligible privacy cost — in comparison with the existing non-private regret bounds\, the additional regret due to privacy appears in lower-order terms.\n\n\n\nMingxun Zhou\,\nCarnegie Mellon University\nTitle: The Power of the Differentially Oblivious Shuffle in Distributed Privacy MechanismsAbstract: The shuffle model has been extensively investigated in the distributed differential privacy (DP) literature. For a class of useful computational tasks\, the shuffle model allows us to achieve privacy-utility tradeoff similar to those in the central model\, while shifting the trust from a central data curator to a “trusted shuffle” which can be implemented through either trusted hardware or cryptography. Very recently\, several works explored cryptographic instantiations of a new type of shuffle with relaxed security\, called differentially oblivious (DO) shuffles. These works demonstrate that by relaxing the shuffler’s security from simulation-style secrecy to differential privacy\, we can achieve asymptotical efficiency improvements. A natural question arises\, can we replace the shuffler in distributed DP mechanisms with a DO-shuffle while retaining a similar privacy-utility tradeoff?\nIn this paper\, we prove an optimal privacy amplification theorem by composing any locally differentially private (LDP) mechanism with a DO-shuffler\, achieving parameters that tightly match the shuffle model. Moreover\, we explore multi-message protocols in the DO-shuffle model\, and construct mechanisms for the real summation and histograph problems. Our error bounds approximate the best known results in the multi-message shuffle-model up to sub-logarithmic factors. Our results also suggest that just like in the shuffle model\, allowing each client to send multiple messages is fundamentally more powerful than restricting to a single message.\n\n\n\nBadih Ghazi\,\nGoogle Research\nTitle: Differentially Private Ad Conversion Measurement \nAbstract: In this work\, we study conversion measurement\, a central functionality in the digital advertising space\, where an advertiser seeks to estimate advertiser site conversions attributed to ad impressions that users have interacted with on various publisher sites. We consider differential privacy (DP)\, a notion that has gained in popularity due to its strong and rigorous guarantees\, and suggest a formal framework for DP conversion measurement\, uncovering a subtle interplay between attribution and privacy. We define the notion of an operationally valid configuration of the attribution logic\, DP adjacency relation\, privacy\nbudget scope and enforcement point\, and provide\, for a natural space of configurations\, a complete characterization.\n\n\n3:15 pm–3:45 pm\nCoffee Break\n\n\n\n3:45 pm–5:00 pm\nOpen Poster Session\n\n\n\n\n\n  \nJune 8\, 2022 \n\n\n\n\n9:15 am–10:15 am\nKeynote Speaker: Nuria Oliver\, Data-Pop Alliance\nTitle: Data Science against COVID-19 \nAbstract: In my talk\, I will describe the work that I have been doing since March 2020\, leading a multi-disciplinary team of 20+ volunteer scientists working very closely with the Presidency of the Valencian Government in Spain on 4 large areas: (1) human mobility modeling; (2) computational epidemiological models (both metapopulation\, individual and LSTM-based models); (3) predictive models; and (4) citizen surveys via the COVID19impactsurvey with over 600\,000 answers worldwide. \nI will describe the results that we have produced in each of these areas\, including winning the 500K XPRIZE Pandemic Response Challenge and best paper award at ECML-PKDD 2021. I will share the lessons learned in this very special initiative of collaboration between the civil society at large (through the survey)\, the scientific community (through the Expert Group) and a public administration (through the Commissioner at the Presidency level). WIRED magazine just published an article describing our story.\n\n\n10:15 am–10:45 am\nCoffee Break\n\n\n\n10:45 am–12:15 pm\nPaper Session 5\nSession Chair: Kunal Talwar\n\n\n\nShengyuan Hu\, Carnegie Mellon University\nTitle: Private Multi-Task Learning: Formulation and Applications to Federated Learning \nAbstract: Many problems in machine learning rely on multi-task learning (MTL)\, in which the goal is to solve multiple related machine learning tasks simultaneously. MTL is particularly relevant for privacy-sensitive applications in areas such as healthcare\, finance\, and IoT computing\, where sensitive data from multiple\, varied sources are shared for the purpose of learning. In this work\, we formalize notions of task-level privacy for MTL via joint differential privacy (JDP)\, a relaxation of differential privacy for mechanism design and distributed optimization. We then propose an algorithm for mean-regularized MTL\, an objective commonly used for applications in personalized federated learning\, subject to JDP. We analyze our objective and solver\, providing certifiable guarantees on both privacy and utility. Empirically\, our method allows for improved privacy/utility trade-offs relative to global baselines across common federated learning benchmarks\n\n\n\nChristina Yu\,\nCornell University\nTitle: Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve \nAbstract: We consider the problem of dividing limited resources to individuals arriving over T rounds with a goal of achieving fairness across individuals. In general there may be multiple resources and multiple types of individuals with different utilities. A standard definition of `fairness’ requires an allocation to simultaneously satisfy envy-freeness and Pareto efficiency. However\, in the online sequential setting\, the social planner must decide on a current allocation before the downstream demand is realized\, such that no policy can guarantee these desiderata simultaneously with probability 1\, requiring a modified metric of measuring fairness for online policies. We show that in the online setting\, the two desired properties (envy-freeness and efficiency) are in direct contention\, in that any algorithm achieving additive counterfactual envy-freeness up to L_T necessarily suffers an efficiency loss of at least 1 / L_T. We complement this uncertainty principle with a simple algorithm\, HopeGuardrail\, which allocates resources based on an adaptive threshold policy and is able to achieve any fairness-efficiency point on this frontier. Our result is the first to provide guarantees for fair online resource allocation with high probability for multiple resource and multiple type settings. In simulation results\, our algorithm provides allocations close to the optimal fair solution in hindsight\, motivating its use in practical applications as the algorithm is able to adapt to any desired fairness efficiency trade-off.\n\n\n\nHedyeh Beyhaghi\, Carnegie Mellon University\nTitle: On classification of strategic agents who can both game and improve \nAbstract: In this work\, we consider classification of agents who can both game and improve. For example\, people wishing to get a loan may be able to take some actions that increase their perceived credit-worthiness and others that also increase their true credit-worthiness. A decision-maker would like to define a classification rule with few false-positives (does not give out many bad loans) while yielding many true positives (giving out many good loans)\, which includes encouraging agents to improve to become true positives if possible. We consider two models for this problem\, a general discrete model and a linear model\, and prove algorithmic\, learning\, and hardness results for each. For the general discrete model\, we give an efficient algorithm for the problem of maximizing the number of true positives subject to no false positives\, and show how to extend this to a partial-information learning setting. We also show hardness for the problem of maximizing the number of true positives subject to a nonzero bound on the number of false positives\, and that this hardness holds even for a finite-point version of our linear model. We also show that maximizing the number of true positives subject to no false positive is NP-hard in our full linear model. We additionally provide an algorithm that determines whether there exists a linear classifier that classifies all agents accurately and causes all improvable agents to become qualified\, and give additional results for low-dimensional data.\n\n\n\nKeegan Harris\, Carnegie Mellon University\nTitle: Bayesian Persuasion for Algorithmic Recourse \nAbstract: When subjected to automated decision-making\, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a favorable decision. In many practical situations\, the underlying assessment rule is deliberately kept secret to avoid gaming and maintain competitive advantage. The resulting opacity forces the decision subjects to rely on incomplete information when making strategic feature modifications. We capture such settings as a game of Bayesian persuasion\, in which the decision maker offers a form of recourse to the decision subject by providing them with an action recommendation (or signal) to incentivize them to modify their features in desirable ways. We show that when using persuasion\, both the decision maker and decision subject are never worse off in expectation\, while the decision maker can be significantly better off. While the decision maker’s problem of finding the optimal Bayesian incentive-compatible (BIC) signaling policy takes the form of optimization over infinitely-many variables\, we show that this optimization can be cast as a linear program over finitely-many regions of the space of possible assessment rules. While this reformulation simplifies the problem dramatically\, solving the linear program requires reasoning about exponentially-many variables\, even under relatively simple settings. Motivated by this observation\, we provide a polynomial-time approximation scheme that recovers a near-optimal signaling policy. Finally\, our numerical simulations on semi-synthetic data empirically illustrate the benefits of using persuasion in the algorithmic recourse setting.\n\n\n12:15 pm–1:45 pm\nLunch Break\n\n\n\n1:45–3:15 pm\nPaper Session 6\nSession Chair: Elisa Celis\n\n\n\nMark Bun\, Boston University\nTitle: Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling \nAbstract: Sampling schemes are fundamental tools in statistics\, survey design\, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism run on a simple random sample of a population provides stronger privacy guarantees than the same algorithm run on the entire population. However\, in practice\, sampling designs are often more complex than the simple\, data-independent sampling schemes that are addressed in prior work. In this work\, we extend the study of privacy amplification results to more complex\, data-dependent sampling schemes. We find that not only do these sampling schemes often fail to amplify privacy\, they can actually result in privacy degradation. We analyze the privacy implications of the pervasive cluster sampling and stratified sampling paradigms\, as well as provide some insight into the study of more general sampling designs.\n\n\n\nSamson Zhou\, Carnegie Mellon University\nTitle: Private Data Stream Analysis for Universal Symmetric Norm Estimation \nAbstract: We study how to release summary statistics on a data stream subject to the constraint of differential privacy. In particular\, we focus on releasing the family of symmetric norms\, which are invariant under sign-flips and coordinate-wise permutations on an input data stream and include L_p norms\, k-support norms\, top-k norms\, and the box norm as special cases. Although it may be possible to design and analyze a separate mechanism for each symmetric norm\, we propose a general parametrizable framework that differentially privately releases a number of sufficient statistics from which the approximation of all symmetric norms can be simultaneously computed. Our framework partitions the coordinates of the underlying frequency vector into different levels based on their magnitude and releases approximate frequencies for the “heavy” coordinates in important levels and releases approximate level sizes for the “light” coordinates in important levels. Surprisingly\, our mechanism allows for the release of an arbitrary number of symmetric norm approximations without any overhead or additional loss in privacy. Moreover\, our mechanism permits (1+\alpha)-approximation to each of the symmetric norms and can be implemented using sublinear space in the streaming model for many regimes of the accuracy and privacy parameters.\n\n\n\nAloni Cohen\, University of Chicago\nTitle: Attacks on Deidentification’s Defenses \nAbstract: Quasi-identifier-based deidentification techniques (QI-deidentification) are widely used in practice\, including k-anonymity\, ?-diversity\, and t-closeness. We present three new attacks on QI-deidentification: two theoretical attacks and one practical attack on a real dataset. In contrast to prior work\, our theoretical attacks work even if every attribute is a quasi-identifier. Hence\, they apply to k-anonymity\, ?-diversity\, t-closeness\, and most other QI-deidentification techniques.\nFirst\, we introduce a new class of privacy attacks called downcoding attacks\, and prove that every QI-deidentification scheme is vulnerable to downcoding attacks if it is minimal and hierarchical. Second\, we convert the downcoding attacks into powerful predicate singling-out (PSO) attacks\, which were recently proposed as a way to demonstrate that a privacy mechanism fails to legally anonymize under Europe’s General Data Protection Regulation. Third\, we use LinkedIn.com to reidentify 3 students in a k-anonymized dataset published by EdX (and show thousands are potentially vulnerable)\, undermining EdX’s claimed compliance with the Family Educational Rights and Privacy Act. \nThe significance of this work is both scientific and political. Our theoretical attacks demonstrate that QI-deidentification may offer no protection even if every attribute is treated as a quasi-identifier. Our practical attack demonstrates that even deidentification experts acting in accordance with strict privacy regulations fail to prevent real-world reidentification. Together\, they rebut a foundational tenet of QI-deidentification and challenge the actual arguments made to justify the continued use of k-anonymity and other QI-deidentification techniques.\n\n\n\nSteven Wu\,\nCarnegie Mellon University\nTitle: Fully Adaptive Composition in Differential Privacy \nAbstract: Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However\, these results require that the privacy parameters of all algorithms be fixed before interacting with the data. To address this\, Rogers et al. introduced fully adaptive composition\, wherein both algorithms and their privacy parameters can be selected adaptively. The authors introduce two probabilistic objects to measure privacy in adaptive composition: privacy filters\, which provide differential privacy guarantees for composed interactions\, and privacy odometers\, time-uniform bounds on privacy loss. There are substantial gaps between advanced composition and existing filters and odometers. First\, existing filters place stronger assumptions on the algorithms being composed. Second\, these odometers and filters suffer from large constants\, making them impractical. We construct filters that match the tightness of advanced composition\, including constants\, despite allowing for adaptively chosen privacy parameters. We also construct several general families of odometers. These odometers can match the tightness of advanced composition at an arbitrary\, preselected point in time\, or at all points in time simultaneously\, up to a doubly-logarithmic factor. We obtain our results by leveraging recent advances in time-uniform martingale concentration. In sum\, we show that fully adaptive privacy is obtainable at almost no loss\, and conjecture that our results are essentially not improvable (even in constants) in general.\n\n\n3:15 pm–3:45 pm\nFORC Reception\n\n\n\n3:45 pm–5:00 pm\nSocial Hour
URL:https://cmsa.fas.harvard.edu/event/symposium-on-foundations-of-responsible-computing-forc/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/FORC22_poster.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220517T090000
DTEND;TZID=America/New_York:20220517T180000
DTSTAMP:20260504T060850
CREATED:20230706T181958Z
LAST-MODIFIED:20240229T102937Z
UID:10000146-1652778000-1652810400@cmsa.fas.harvard.edu
SUMMARY:SMaSH: Symposium for Mathematical Sciences at Harvard
DESCRIPTION:SMaSH: Symposium for Mathematical Sciences at Harvard\nOn Tuesday\, May 17\, 2022\, from 9:00 am – 5:30 pm\, the Harvard John A Paulson School of Engineering and Applied Sciences (SEAS) and the Harvard Center of Mathematical Sciences and Applications (CMSA) held a Symposium for Mathematical Sciences for the mathematical sciences community at Harvard. \nOrganizing Committee \n\nMichael Brenner\, Applied Mathematics (SEAS)\nMichael Desai\, Organismic and Evolutionary Biology (FAS)\nSam Gershman\, Psychology (FAS)\nMichael Hopkins\, Mathematics (FAS)\nGary King\, Government (FAS)\nPeter Koellner\, Philosophy (FAS)\nScott Kominers\, Economics (FAS) & Entrepreneurial Management (HBS)\nXihong Lin\, Biostatistics (HSPH) & Statistics (FAS)\nYue Lu\, Electrical Engineering (SEAS)\nSusan Murphy\, Statistics (FAS) & Computer Science (SEAS)\nLisa Randall\, Physics (SEAS)\nEugene Shakhnovich\, Chemistry (FAS)\nSalil Vadhan\, Computer Science (SEAS)\nHorng-Tzer Yau\, Mathematics (FAS)\n\n\nThis event was held in-person at the Science and Engineering Complex (SEC) at 150 Western Ave\, Allston\, MA 02134\, and streamed on Zoom. \nHarvard graduate students and postdocs presented Poster Sessions. \n\nVenue: Science and Engineering Complex (SEC) \n\nSpeakers\n\nAnurag Anshu\, Computer Science (SEAS)\nMorgane Austern\, Statistics (FAS)\nDemba Ba\, Electrical Engineering & Bioengineering (SEAS)\nMichael Brenner\, Applied Mathematics (SEAS)\nRui Duan\, Biostatistics (HSPH)\nYannai A. Gonczarowski\, Economics (FAS) & Computer Science (SEAS)\nKosuke Imai\, Government & Statistics (FAS)\nSham M. Kakade\, Computer Science (SEAS) & Statistics (FAS)\nSeth Neel\, Technology & Operations Management (HBS)\nMelanie Matchett Wood\, Mathematics (FAS)\n\nSchedule PDF \nSchedule\n\n\n\n\n9:00–9:30 am\nCoffee and Breakfast\nWest Atrium (ground floor of the SEC)\n\n\n9:30–10:30 am\nFaculty Talks\nWinokur Family Hall Classroom (Room 1.321) located just off of the West AtriumKosuke Imai\, Government & Statistics (FAS): Use of Simulation Algorithms for Legislative Redistricting Analysis and EvaluationYannai A. Gonczarowski\, Economics (FAS) & Computer Science (SEAS): The Sample Complexity of Up-to-ε Multi-Dimensional Revenue Maximization\n\n\n10:30–11:00 am\nCoffee Break\nWest Atrium (ground floor of the SEC)\n\n\n11:00–12:00 pm\nFaculty Talks\nWinokur Family Hall Classroom (Room 1.321) located just off of the West AtriumSeth Neel\, Technology & Operations Management (HBS): “Machine (Un)Learning” or Why Your Deployed Model Might Violate Existing Privacy LawDemba Ba\, Electrical Engineering & Bioengineering (SEAS): Geometry\, AI\, and the Brain\n\n\n12:00–1:00 pm\nLunch Break\nEngineering Yard Tent\n\n\n1:00–2:30 pm\nFaculty Talks\nWinokur Family Hall Classroom (Room 1.321) located just off of the West AtriumMelanie Matchett Wood\, Mathematics (FAS): Understanding distributions of algebraic structures through their momentsMorgane Austern\, Statistics (FAS): Limit theorems for structured random objectsAnurag Anshu\, Computer Science (SEAS): Operator-valued polynomial approximations and their use.\n\n\n2:30–3:00 pm\nCoffee Break\nWest Atrium (ground floor of the SEC)\n\n\n3:00–4:30 pm\nFaculty Talks\nWinokur Family Hall Classroom (Room 1.321) located just off of the West AtriumMichael Brenner\, Applied Mathematics (SEAS): Towards living synthetic materialsRui Duan\, Biostatistics (HSPH): Federated and transfer learning for healthcare data integrationSham M. Kakade\, Computer Science (SEAS) & Statistics (FAS): What is the Statistical Complexity of Reinforcement Learning?\n\n\n4:30–5:30 pm\nReception with Jazz musicians\n& Poster Session\nEngineering Yard Tent\n\n\n\n\n\nFaculty Talks\n\n\n\n\nSpeaker\nTitle / Abstract / Bio\n\n\nAnurag Anshu\, Computer Science (SEAS)\nTitle: Operator-valued polynomial approximations and their use. \nAbstract: Approximation of complicated functions with low degree polynomials is an indispensable tool in mathematics. This becomes particularly relevant in computer science\, where the complexity of interesting functions is often captured by the degree of the approximating polynomials. This talk concerns the approximation of operator-valued functions (such as the exponential of a hermitian matrix\, or the intersection of two projectors) with low-degree operator-valued polynomials. We will highlight the challenges that arise in achieving similarly good approximations as real-valued functions\, as well as recent methods to overcome them. We will discuss applications to the ground states in physics and quantum complexity theory: correlation lengths\, area laws and concentration bounds. \nBio: Anurag Anshu is an Assistant Professor of computer science at Harvard University. He spends a lot of time exploring the rich structure of quantum many-body systems from the viewpoint of quantum complexity theory\, quantum learning theory and quantum information theory. He held postdoctoral positions at University of California\, Berkeley and University of Waterloo and received his PhD from National University of Singapore\, focusing on quantum communication complexity.\n\n\nMorgane Austern\, Statistics (FAS)\nTitle: Limit theorems for structured random objects \nAbstract: Statistical inference relies on numerous tools from probability theory to study the properties of estimators. Some of the most central ones are the central limit theorem and the free central limit theorem. However\, these same tools are often inadequate to study modern machine problems that frequently involve structured data (e.g networks) or complicated dependence structures (e.g dependent random matrices). In this talk\, we extend universal limit theorems beyond the classical setting. We consider distributionally “structured’ and dependent random object i.e random objects whose distribution is invariant under the action of an amenable group. We show\, under mild moment and mixing conditions\, a series of universal second and third order limit theorems: central-limit theorems\, concentration inequalities\, Wigner semi-circular law and Berry-Esseen bounds. The utility of these will be illustrated by a series of examples in machine learning\, network and information theory. \nBio: Morgane Austern is an assistant professor in the Statistics Department of Harvard University. Broadly\, she is interested in developing probability tools for modern machine learning and in establishing the properties of learning algorithms in structured and dependent data contexts. She graduated with a PhD in statistics from Columbia University in 2019 where she worked in collaboration with Peter Orbanz and Arian Maleki on limit theorems for dependent and structured data. She was a postdoctoral researcher at Microsoft Research New England from 2019 to 2021.\n\n\nDemba Ba\, Electrical Engineering & Bioengineering (SEAS)\nTitle: Geometry\, AI\, and the Brain \nAbstract: A large body of experiments suggests that neural computations reflect\, in some sense\, the geometry of “the world”. How do artificial and neural systems learn representations of “the world” that reflect its geometry? How\, for instance\, do we\, as humans\, learn representations of objects\, e.g. fruits\, that reflect the geometry of object space? Developing artificial systems that can capture/understand the geometry of the data they process may enable them to learn representations useful in many different contexts and tasks. My talk will describe an artificial neural-network architecture that\, starting from a simple union-of-manifold model of data comprising objects from different categories\, mimics some aspects of how primates learn\, organize\, and retrieve concepts\, in a manner that respects the geometry of object space. \nBio: Demba Ba serves as an Associate Professor of electrical engineering and bioengineering in Harvard University’s School of Engineering and Applied Sciences\, where he directs the CRISP group. Recently\, he has taken a keen interest in the connection between artificial neural networks and sparse signal processing. His group leverages this connection to solve data-driven unsupervised learning problems in neuroscience\, to understand the principles of hierarchical representations of sensory signals in the brain\, and to develop explainable AI. In 2016\, he received a Research Fellowship in Neuroscience from the Alfred P. Sloan Foundation. In 2021\, Harvard’s Faculty of Arts and Sciences awarded him the Roslyn Abramson award for outstanding undergraduate teaching.\n\n\nMichael Brenner\, Applied Mathematics (SEAS)\nTitle: Towards living synthetic materials \nAbstract: Biological materials are much more complicated and functional than synthetic ones. Over the past several years we have been trying to figure out why. A sensible hypothesis is that biological materials are programmable. But we are very far from being able to program materials we create with this level of sophistication.  I will discuss our (largely unsuccessful) efforts to bridge this gap\, though as of today I’m somewhat optimistic that we are arriving at a set of theoretical models that is rich enough to produce relevant emergent behavior. \nBio: I’ve been at Harvard for a long time. My favorite part of Harvard is the students.\n\n\nRui Duan\, Biostatistics (HSPH)\nTitle: Federated and transfer learning for healthcare data integration \nAbstract: The growth of availability and variety of healthcare data sources has provided unique opportunities for data integration and evidence synthesis\, which can potentially accelerate knowledge discovery and improve clinical decision-making. However\, many practical and technical challenges\, such as data privacy\, high dimensionality\, and heterogeneity across different datasets\, remain to be addressed. In this talk\, I will introduce several methods for the effective and efficient integration of multiple healthcare datasets in order to train statistical or machine learning models with improved generalizability and transferability. Specifically\, we develop communication-efficient federated learning algorithms for jointly analyzing multiple datasets without the need of sharing patient-level data\, as well as transfer learning approaches that leverage shared knowledge learned across multiple datasets to improve the performance of statistical models in target populations of interest. We will discuss both the theoretical properties and examples of implementation of our methods in real-world research networks and data consortia. \nBio: Rui Duan is an Assistant Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She received her Ph.D. in Biostatistics in May 2020 from the University of Pennsylvania. Her research interests focus on developing statistical\, machine learning\, and informatics tools for (1) efficient data integration in biomedical research\, (2) understanding and accounting for the heterogeneity of biomedical data\, and improving the generalizability and transferability of models across populations (3) advancing precision medicine research on rare diseases and underrepresented populations.\n\n\nYannai A. Gonczarowski\, Economics (FAS) & Computer Science (SEAS)\nTitle: The Sample Complexity of Up-to-ε Multi-Dimensional Revenue Maximization \nAbstract: We consider the sample complexity of revenue maximization for multiple bidders in unrestricted multi-dimensional settings. Specifically\, we study the standard model of n additive bidders whose values for m heterogeneous items are drawn independently. For any such instance and any ε > 0\, we show that it is possible to learn an ε-Bayesian Incentive Compatible auction whose expected revenue is within ε of the optimal ε-BIC auction from only polynomially many samples. \nOur fully nonparametric approach is based on ideas that hold quite generally\, and completely sidestep the difficulty of characterizing optimal (or near-optimal) auctions for these settings. Therefore\, our results easily extend to general multi-dimensional settings\, including valuations that are not necessarily even subadditive\, and arbitrary allocation constraints. For the cases of a single bidder and many goods\, or a single parameter (good) and many bidders\, our analysis yields exact incentive compatibility (and for the latter also computational efficiency). Although the single-parameter case is already well-understood\, our corollary for this case extends slightly the state-of-the-art. \nJoint work with S. Matthew Weinberg \nBio: Yannai A. Gonczarowski is an Assistant Professor of Economics and of Computer Science at Harvard University—the first faculty member at Harvard to have been appointed to both of these departments. Interested in both economic theory and theoretical computer science\, Yannai explores computer-science-inspired economics: he harnesses approaches\, aesthetics\, and techniques traditionally originating in computer science to derive economically meaningful insights. Yannai received his PhD from the Departments of Math and CS\, and the Center for the Study of Rationality\, at the Hebrew University of Jerusalem\, where he was advised by Sergiu Hart and Noam Nisan. Yannai is also a professionally-trained opera singer\, having acquired a bachelor’s degree and a master’s degree in Classical Singing at the Jerusalem Academy of Music and Dance. Yannai’s doctoral dissertation was recognized with several awards\, including the 2018 Michael B. Maschler Prize of the Israeli Chapter of the Game Theory Society\, and the ACM SIGecom Doctoral Dissertation Award for 2018. For the design and implementation of the National Matching System for Gap-Year Programs in Israel\, he was awarded the Best Paper Award at MATCH-UP’19 and the inaugural INFORMS AMD Michael H. Rothkopf Junior Researcher Paper Prize (first place) for 2020. Yannai is also the recipient of the inaugural ACM SIGecom Award for Best Presentation by a Student or Postdoctoral Researcher at EC’18. His first textbook\, “Mathematical Logic through Python” (Gonczarowski and Nisan)\, which introduces a new approach to teaching the material of a basic Logic course to Computer Science students\, tailored to the unique intuitions and strengths of this cohort of students\, is forthcoming in Cambridge University Press.\n\n\nKosuke Imai\, Government & Statistics (FAS)\nTitle: Use of Simulation Algorithms for Legislative Redistricting Analysis and Evaluation \nAbstract: After the 2020 Census\, many states have been redrawing the boundaries of Congressional and state legislative districts. To evaluate the partisan and racial bias of redistricting plans\, scholars have developed Monte Carlo simulation algorithms. The idea is to generate a representative sample of redistricting plans under a specified set of criteria and conduct a statistical hypothesis test by comparing a proposed plan with these simulated plans. I will give a brief overview of these redistricting simulation algorithms and discuss how they are used in real-world court cases. \nBio: Kosuke Imai is Professor in the Department of Government and Department of Statistics at Harvard University. Before moving to Harvard in 2018\, Imai taught at Princeton University for 15 years where he was the founding director of the Program in Statistics and Machine Learning. Imai specializes in the development of statistical methods and machine learning algorithms and their applications to social science research. His areas of expertise include causal inference\, computational social science\, program evaluation\, and survey methodology.\n\n\nSham M. Kakade\, Computer Science (SEAS) & Statistics (FAS)\nTitle: What is the Statistical Complexity of Reinforcement Learning? \nAbstract: This talk will highlight much of the recent progress on the following fundamental question in the theory of reinforcement learning: what (representational or structural) conditions govern our ability to generalize and avoid the curse of dimensionality?  With regards to supervised learning\, these questions are reasonably well understood\, both practically and theoretically: practically\, we have overwhelming evidence on the value of representational learning (say through modern deep networks) as a means for sample efficient learning\, and\, theoretically\, there are well-known complexity measures (e.g. the VC dimension and Rademacher complexity) that govern the statistical complexity of learning.  Providing an analogous theory for reinforcement learning is far more challenging\, where even characterizing structural conditions which support sample efficient generalization has been far less well understood\, until recently. \nThis talk will survey recent advances towards characterizing when generalization is possible in RL\, focusing on both necessary and sufficient conditions. In particular\, we will introduce a new complexity measure\, the Decision-Estimation Coefficient\, that is proven to be necessary (and\, essentially\, sufficient) for sample-efficient interactive learning. \nBio: Sham Kakade is a professor at Harvard University and a co-director of the Kempner Institute for the Study of Artificial and Natural Intelligence.  He works on the mathematical foundations of machine learning and AI. Sham’s thesis helped lay the statistical foundations of reinforcement learning. With his collaborators\, his additional contributions include foundational results on: policy gradient methods in reinforcement learning; regret bounds for linear bandit and Gaussian process bandit models; the tensor and spectral methods for latent variable models; and a number of convergence analyses for convex and non-convex algorithms.  He is the recipient of the ICML Test of Time Award\, the IBM Pat Goldberg best paper award\, and INFORMS Revenue Management and Pricing Prize. He has been program chair for COLT 2011. \nSham was an undergraduate at Caltech\, where he studied physics and worked under the guidance of John Preskill in quantum computing. He completed his Ph.D. with Peter Dayan in computational neuroscience at the Gatsby Computational Neuroscience Unit. He was a postdoc with Michael Kearns at the University of Pennsylvania.\n\n\nSeth Neel\, Technology & Operations Management (HBS)\nTitle: “Machine (Un)Learning” or Why Your Deployed Model Might Violate Existing Privacy Law \nAbstract:  Businesses like Facebook and Google depend on training sophisticated models on user data. Increasingly—in part because of regulations like the European Union’s General Data Protection Act and the California Consumer Privacy Act—these organizations are receiving requests to delete the data of particular users. But what should that mean? It is straightforward to delete a customer’s data from a database and stop using it to train future models. But what about models that have already been trained using an individual’s data? These are not necessarily safe; it is known that individual training data can be exfiltrated from models trained in standard ways via model inversion attacks. In a series of papers we help formalize a rigorous notion of data-deletion and propose algorithms to efficiently delete user data from trained models with provable guarantees in both convex and non-convex settings. \nBio: Seth Neel is a first-year Assistant Professor in the TOM Unit at Harvard Business School\, and Co-PI of the SAFR ML Lab in the D3 Institute\, which develops methodology to incorporate privacy and fairness guarantees into techniques for machine learning and data analysis\, while balancing other critical considerations like accuracy\, efficiency\, and interpretability. He obtained his Ph.D. from the University of Pennsylvania in 2020 where he was an NSF graduate fellow. His work has focused primarily on differential privacy\, notions of fairness in a variety of machine learning settings\, and adaptive data analysis.\n\n\nMelanie Matchett Wood\, Mathematics (FAS)\nTitle: Understanding distributions of algebraic structures through their moments \nAbstract: A classical tool of probability and analysis is to use the moments (mean\, variance\, etc.) of a distribution to recognize an unknown distribution of real numbers.  In recent work\, we are interested in distributions of algebraic structures that can’t be captured in a single number.  We will explain one example\, the fundamental group\, that captures something about the shapes of possibly complicated or high dimensional spaces.  We are developing a new theory of the moment problem for random algebraic structures which helps to to identify distributions of such\, such as fundamental groups of random three dimensional spaces.  This talk is based partly on joint work with Will Sawin. \nBio: Melanie Matchett Wood is a professor of mathematics at Harvard University and a Radcliffe Alumnae Professor at the Radcliffe Institute for Advanced Study.  Her work spans number theory\, algebraic geometry\, algebraic topology\, additive combinatorics\, and probability. Wood has been awarded a CAREER grant\, a Sloan Research Fellowship\, a Packard Fellowship for Science and Engineering\, and the AWM-Microsoft Research Prize in Algebra and Number Theory\, and she is a Fellow of the American Mathematical Society. In 2021\, Wood received the National Science Foundation’s Alan T. Waterman Award\, the nation’s highest honor for early-career scientists and engineers.
URL:https://cmsa.fas.harvard.edu/event/smash-symposium-for-mathematical-sciences-at-harvard/
LOCATION:Science and Engineering Complex (SEC)\, 150 Western Ave\, Allston\, MA 02134\, MA
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/SMaSH_2022-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220509T090000
DTEND;TZID=America/New_York:20220512T123000
DTSTAMP:20260504T060850
CREATED:20230706T181710Z
LAST-MODIFIED:20231227T082643Z
UID:10000107-1652086800-1652358600@cmsa.fas.harvard.edu
SUMMARY:Conference in Memory of Professor Masatake Kuranishi
DESCRIPTION:On May 9–12\, 2022\, the CMSA hosted the conference Deformations of structures and moduli in geometry and analysis: A Memorial in honor of Professor Masatake Kuranishi. \nOrganizers:  Tristan Collins (MIT) and Shing-Tung Yau (Harvard and Tsinghua) \nVideos are available on the conference playlist. \n  \nSpeakers: \nCharles Fefferman (Princeton University) \nTeng Fei (Rutgers University) \nRobert Friedman (Columbia University) \nKenji Fukaya (Simons Center\, Stony Brook) \nAkito Futaki (Tsinghua University) \nVictor Guillemin (Massachusetts Institute of Technology) \nNigel Hitchin (Oxford University) \nBlaine Lawson (Stony Brook University) \nYu-Shen Lin (Boston University) \nMelissa C.C. Liu (Columbia University) \nTakeo Ohsawa (Nagoya University) \nDuong H. Phong (Columbia University) \nSebastien Picard (University of British Columbia) \nPaul Seidel (Massachusetts Institute of Technology) \nGabor Szekelyhidi (University of Notre Dame) \nClaire Voisin (Institut de Mathematiques\, Jussieu\, France) \nShing-Tung Yau (Harvard University) \n  \n\n\n\nSchedule (download pdf) \n\nMonday\, May 9\, 2022 \n\n\n\n8:15 am\nLight breakfast & coffee/tea\n\n\n8:45–9:00 am\nOpening Remarks\n\n\n9:00–10:00 am\nKenji Fukaya\nTitle: Gromov Hausdorff convergence of filtered A infinity category \nAbstract: In mirror symmetry a mirror to a symplectic manifold is actually believed to be a family of complex manifold parametrized by a disk (of radius 0). The coordinate ring of the parameter space is a kind of formal power series ring the Novikov ring. Novikov ring is a coefficient ring of Floer homology. Most of the works on homological Mirror symmetry so far studies A infinity category over Novikov field\, which corresponds to the study of generic fiber. The study of A infinity category over Novikov ring is related to several interesting phenomenon of Hamiltonian dynamics. In this talk I will explain a notion which I believe is useful to study mirror symmetry. \nVideo\n\n\n10:15–11:15 am\nNigel Hitchin (Zoom)\nTitle: Deformations: A personal perspective \nAbstract: The talk\, largely historical\, will focus on different deformation complexes I have encountered in my work\, starting with instantons on 4-manifolds\, but also monopoles\, Higgs bundles and generalized complex structures. I will also discuss some speculative ideas related to surfaces of negative curvature. \nVideo\n\n\n11:30–12:30 pm\nH. Blaine Lawson\nTitle: Projective Hulls\, Projective Linking\, and Boundaries of Varieties \nAbstract: In 1958 John Wermer proved that the polynomial hull of a compact real analytic curve γ ⊂ Cn was a 1-dim’l complex subvariety of Cn − γ. This result engendered much subsequent activity\, and was related to Gelfand’s spectrum of a Banach algebra. In the early 2000’s Reese Harvey and I found a projective analogue of these concepts and wondered whether Wermer’s Theorem could be generalized to the projective setting. This question turned out to be more subtle and quite intriguing\, with unexpected consequences. We now know a great deal\, a highpoint of which s a result with Harvey and Wermer. It led to conjectures (for Cω-curves in P2C) which imply several results. One says\, roughly\, that a (2p − 1)-cycle Γ in Pn bounds a positive holomorphic p-chain of mass ≤ Λ ⇐⇒ its normalized linking number with all positive (n − p)-cycles in Pn − |Γ| is ≥ −Λ. Another says that a class τ ∈ H2p(Pn\,|Γ|;Z) with ∂τ = Γ contains a positive holomorphic p-chain ⇐⇒ τ•[Z]≥0 for all positive holomorphic (n−p)-cycles Z in Pn−|Γ| \nVideo\n\n\n12:30–2:30 pm\nLunch Break\n\n\n\n2:30–3:30 pm\nGabor Szekelyhidi\nTitle: Singularities along the Lagrangian mean curvature flow. \nAbstract: We study singularity formation along the Lagrangian mean curvature flow of surfaces. On the one hand we show that if a tangent flow at a singularity is the special Lagrangian union of two transverse planes\, then the flow undergoes a “neck pinch”\, and can be continued past the flow. This can be related to the Thomas-Yau conjecture on stability conditions along the Lagrangian mean curvature flow. In a different direction we show that ancient solutions of the flow\, whose blow-down is given by two planes meeting along a line\, must be translators. These are joint works with Jason Lotay and Felix Schulze. \nVideo\n\n\n3:30–4:00 pm\nCoffee Break\n \n\n\n4:00–5:00 pm\nTakeo Ohsawa\nTitle: Glimpses of embeddings and deformations of CR manifolds \nAbstract: Basic results on the embeddings and the deformations of CR manifolds will be reviewed with emphasis on the reminiscences of impressive moments with Kuranishi since his visit to Kyoto in 1975. \nVideo\n\n\n\n  \n  \n  \nTuesday\, May 10\, 2022 \n  \n\n\n\n8:15 am\nLight breakfast & coffee/tea\n\n\n9:00–10:00 am\nCharles Fefferman (Zoom)\nTitle: Interpolation of Data by Smooth Functions \nAbstract: Let X be your favorite Banach space of continuous functions on R^n. Given an (arbitrary) set E in R^n and an arbitrary function f:E->R\, we ask: How can we tell whether f extends to a function F \in X? If such an F exists\, then how small can we take its norm? What can we say about its derivatives (assuming functions in X have derivatives)? Can we take F to depend linearly on f? Suppose E is finite. Can we compute an F as above with norm nearly as small as possible? How many computer operations does it take? What if F is required to agree only approximately with f on E? What if we are allowed to discard a few data points (x\, f(x)) as “outliers”? Which points should we discard? \nThe results were obtained jointly with A. Israel\, B. Klartag\, G.K. Luli and P. Shvartsman over many years. \nVideo\n\n\n10:15–11:15 am\nClaire Voisin\nTitle: Deformations of K-trivial manifolds and applications to hyper-Kähler geometry \nSummary: I will explain the Ran approach via the T^1-lifting principle to the BTT theorem stating that deformations of K-trivial compact Kähler manifolds are unobstructed. I will explain a similar unobstructedness result for Lagrangian submanifolds of hyper-Kähler manifolds and I will describe important consequences on the topology and geometry of hyper-Kähler manifolds. \nVideo\n\n\n11:30– 2:30 pm\nVictor Guillemin\nTitle: Semi-Classical Functions of Isotropic Type \nAbstract: The world of semiclassical analysis is populated by objects of “Lagrangian type.” The topic of this talk however will be objects in semi-classical analysis that live instead on isotropic submanifolds. I will describe in my talk a lot of interesting examples of such objects. \nVideo\n\n\n12:30–2:30 pm\nLunch Break\n\n\n\n2:30–3:30 pm\nTeng Fei\nTitle: Symplectic deformations and the Type IIA flow \nAbstract: The equations of flux compactification of Type IIA superstrings were written down by Tomasiello and Tseng-Yau. To study these equations\, we introduce a natural geometric flow known as the Type IIA flow on symplectic Calabi-Yau 6-manifolds. We prove the wellposedness of this flow and establish the basic estimates. We show that the Type IIA flow can be applied to find optimal almost complex structures on certain symplectic manifolds. We prove the dynamical stability of the Type IIA flow\, which leads to a proof of stability of Kahler property for Calabi-Yau 3-folds under symplectic deformations. This is based on joint work with Phong\, Picard and Zhang. \nVideo\n\n\nSpeakers Banquet\n\n\n\n\n\n  \n  \n  \nWednesday\, May 11\, 2022 \n  \n\n\n\n8:15 am\nLight breakfast & coffee/tea\n\n\n9:00–10:00 am\nShing-Tung Yau (Zoom)\nTitle: Canonical metrics and stability in mirror symmetry \nAbstract: I will discuss the deformed Hermitian-Yang-Mills equation\, its role in mirror symmetry and its connections to notions of stability.  I will review what is known\, and pose some questions for the future. \nVideo\n\n\n10:15–11:15 am\nDuong H. Phong\nTitle: $L^\infty$ estimates for the Monge-Ampere and other fully non-linear equations in complex geometry \nAbstract: A priori estimates are essential for the understanding of partial differential equations\, and of these\, $L^\infty$ estimates are particularly important as they are also needed for other estimates. The key $L^\infty$ estimates were obtained by S.T. Yau in 1976 for the Monge-Ampere equation for the Calabi conjecture\, and sharp estimates obtained later in 1998 by Kolodziej using pluripotential theory. It had been a long-standing question whether a PDE proof of these estimates was possible. We provide a positive answer to this question\, and derive as a consequence sharp estimates for general classes of fully non-linear equations. This is joint work with B. Guo and F. Tong. \nVideo\n\n\n11:30–2:30 pm\nPaul Seidel\nTitle: The quantum connection: familiar yet puzzling \nAbstract: The small quantum connection on a Fano variety is possibly the most basic piece of enumerative geometry. In spite of being really easy to write down\, it is the subject of far-reaching conjectures (Dubrovin\, Galkin\, Iritani)\, which challenge our understanding of mirror symmetry. I will give a gentle introduction to the simplest of these questions. \nVideo\n\n\n12:30–2:30 pm\nLunch Break\n\n\n\n2:30–3:30 pm\nMelissa C.C. Liu\nTitle: Higgs-Coulumb correspondence for abelian gauged linear sigma models \nAbstract: The underlying geometry of a gauged linear sigma model (GLSM) consists of a GIT quotient of a complex vector space by the linear action of a reductive algebraic group G (the gauge group) and a polynomial function (the superpotential) on the GIT quotient. The Higgs-Coulomb correspondence relates (1) GLSM invariants which are virtual counts of curves in the critical locus of the superpotential (Higgs branch)\, and (2) Mellin-Barnes type integrals on the Lie algebra of G (Coulomb branch). In this talk\, I will describe the correspondence when G is an algebraic torus\, and explain how to use the correspondence to study dependence of GLSM invariants on the stability condition. This is based on joint work with Konstantin Aleshkin. \nVideo\n\n\n3:30–4:00 pm\nCoffee Break\n \n\n\n4:00–5:00 pm\nSebastien Picard\nTitle: Topological Transitions of Calabi-Yau Threefolds \nAbstract: Conifold transitions were proposed in the works of Clemens\, Reid and Friedman as a way to travel in the parameter space of Calabi-Yau threefolds with different Hodge numbers. This process may deform a Kahler Calabi-Yau threefold into a non-Kahler complex manifold with trivial canonical bundle. We will discuss the propagation of differential geometric structures such as balanced hermitian metrics\, Yang-Mills connections\, and special submanifolds through conifold transitions. This is joint work with T. Collins\, S. Gukov and S.-T. Yau. \nVideo\n\n\n\n  \n  \n  \nThursday\, May 12\, 2022 \n  \n\n\n\n8:15 am\nLight breakfast & coffee/tea\n\n\n9:00 am–10:00 am\nAkito Futaki (Zoom)\nTitle: Transverse coupled Kähler-Einstein metrics and volume minimization\n\nAbstract: We show that transverse coupled Kähler-Einstein metrics on toric Sasaki manifolds arise as a critical point of a volume functional. As a preparation for the proof\, we re-visit the transverse moment polytopes and contact moment polytopes under the change of Reeb vector fields. Then we apply it to a coupled version of the volume minimization by Martelli-Sparks-Yau. This is done assuming the Calabi-Yau condition of the Kählercone\, and the non-coupled case leads to a known existence result of a transverse Kähler-Einstein metric and a Sasaki-Einstein metric\, but the coupled case requires an assumption related to Minkowski sum to obtain transverse coupled Kähler-Einstein metrics.Video\n\n\n10:15 am–11:15 am\nYu-Shen Lin\nTitle: SYZ Mirror Symmetry of Log Calabi-Yau Surfaces \nAbstract: Strominger-Yau-Zaslow conjecture predicts Calabi-Yau manifolds admits special Lagrangian fibrations. The conjecture serves as one of the guiding principles in mirror symmetry. In this talk\, I will explain the existence of the special Lagrangian fibrations in some log Calabi-Yau surfaces and their dual fibrations in their expected mirrors. The journey leads us to the study of the moduli space of Ricci-flat metrics with certain asymptotics on these geometries and the discovery of new semi-flat metrics. If time permits\, I will explain the application to the Torelli theorem of ALH^* gravitational instantons. The talk is based on joint works with T. Collins and A. Jacob. \nVideo\n\n\n11:30 am – 12:30 pm\nRobert Friedman\nTitle: Deformations of singular Fano and Calabi-Yau varieties \nAbstract: This talk will describe recent joint work with Radu Laza on deformations of generalized Fano and Calabi-Yau varieties\, i.e. compact analytic spaces whose dualizing sheaves are either duals of ample line bundles or are trivial. Under the assumption of isolated hypersurface canonical singularities\, we extend results of Namikawa and Steenbrink in dimension three and discuss various generalizations to higher dimensions. \nVideo\n\n\n12:30 pm\nConcluding Remarks\n\n\n\n 
URL:https://cmsa.fas.harvard.edu/event/conference-in-memory-of-professor-masatake-kuranishi/
LOCATION:Science and Engineering Complex (SEC)\, 150 Western Ave\, Allston\, MA 02134\, MA
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/Kuranishi_Harvard_10x12-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220404T093000
DTEND;TZID=America/New_York:20220408T170000
DTSTAMP:20260504T060850
CREATED:20230705T082708Z
LAST-MODIFIED:20250305T172217Z
UID:10000087-1649064600-1649437200@cmsa.fas.harvard.edu
SUMMARY:General Relativity Conference
DESCRIPTION:Schedule | April 4–8\, 2022\nMonday\, April 4\, 2022 \n\n\n\n\nTime (ET)\nSpeaker\nTitle/Abstract\n\n\n9:30 am–10:30 am\nPieter Blue\, University of Edinburgh\, UK\n(virtual)\nTitle: Linear stability of the Kerr spacetime in the outgoing radiation gauge \nAbstract: This talk will discuss a new gauge condition (i.e. coordinate condition) for the Einstein equation\, the linearisation of the Einstein equation in this gauge\, and the decay of solutions to the linearised Einstein equation around Kerr black holes in this gauge. The stability of the family of Kerr black holes under the evolution generated by the Einstein equation is a long-standing problem in mathematical relativity. In 1972\, Teukolsky discovered equations governing certain components of the linearised curvature that are invariant under linearised gague transformations. In 1975\, Chrzanowski introduced the “outgoing radiation gauge”\, a condition on the linearised metric that allows for the construction of the linearised metric from the linearised curvature. In 2019\, we proved decay for the metric constructed using Chrzanowski’s outgoing radiation gauge. Recently\, using a flow along null geodesics\, we have constructed a new gauge such that\, in this gauge\, the Einstein equation is well posed and such that the linearisation is Chrzanowski’s outgoing radiation gauge. \nThis is joint work with Lars Andersson\, Thomas Backdahl\, and Siyuan Ma.\n\n\n10:30 am–11:30 am\nPeter Hintz\, ETH Zürich\n(virtual)\nTitle: Mode stability and shallow quasinormal modes of Kerr-de Sitter\nblack holesAbstract: The Kerr-de Sitter metric describes a rotating black hole with mass $m$ and specific angular momentum $a$ in a universe\, such as our own\, with cosmological constant $\Lambda>0$. I will explain a proof of mode stability for the scalar wave equation on Kerr-de Sitter spacetimes in the following setting: fixing $\Lambda$ and the ratio $|a/m|<1$ (related to the subextremality of the black hole in question)\, mode stability holds for sufficiently small black hole mass $m$. We also obtain estimates for the location of quasinormal modes (resonances) $\sigma$ in any fixed half space $\Im\sigma>-C$. Our results imply that solutions of the wave equation decay exponentially in time to constants\, with an explicit exponential rate. The proof is based on careful uniform estimates for the spectral family in the singular limit $m\to 0$ in which\, depending on the scaling\, the Kerr-de Sitter spacetime limits to a Kerr or the de Sitter spacetime.\n\n\n11:30 am–12:30 pm\nLars Andersson\, Albert Einstein Institute\, Germany\n(virtual)\nTitle: Gravitational instantons and special geometry \nAbstract: Gravitational instantons are Ricci flat complete Riemannian 4-manifolds with at least quadratic curvature decay. In this talk\, I will introduce some notions of special geometry\, discuss known examples\, and mention some open questions. The Chen-Teo gravitational instanton is an asymptotically flat\, toric\, Ricci flat family of metrics on $\mathrm{CP}^2 \setminus \mathrm{S}^1$\, that provides a counterexample to the classical Euclidean Black Hole Uniqueness conjecture. I will sketch a proof that the Chen-Teo Instanton is Hermitian and non-Kähler. Thus\, all known examples of gravitational instantons are Hermitian. This talks is based on joint work with Steffen Aksteiner\, cf. https://arxiv.org/abs/2112.11863.\n\n\n12:30 pm–1:30 pm\nbreak\n\n\n\n1:30 pm–2:30 pm\nMartin Taylor\, Imperial College London\n(virtual)\nTitle: The nonlinear stability of the Schwarzschild family of black holes \nAbstract: I will present a theorem on the full finite codimension nonlinear asymptotic stability of the Schwarzschild family of black holes.  The proof employs a double null gauge\, is expressed entirely in physical space\, and utilises the analysis of Dafermos–Holzegel–Rodnianski on the linear stability of the Schwarzschild family.  This is joint work with M. Dafermos\, G. Holzegel and I. Rodnianski.\n\n\n2:30 pm–3:30 pm\nPo-Ning Chen\, University of California\, Riverside\n(virtual)\nTitle: Angular momentum in general relativity\n\nAbstract: The definition of angular momentum in general relativity has been a subtle issue since the 1960s\, due to the ‘supertranslation ambiguity’. In this talk\, we will discuss how the mathematical theory of quasilocal mass and angular momentum leads to a new definition of angular momentum at null infinity that is free of any supertranslation ambiguity.This is based on joint work with Jordan Keller\, Mu-Tao Wang\, Ye-Kai Wang\, and Shing-Tung Yau.\n\n\n3:30 pm–4:00 pm\nbreak\n\n\n\n4:00 pm–5:00 pm\nDan Lee\, Queens College (CUNY)\n(hybrid: in person & virtual)\nTitle: Stability of the positive mass theorem \nAbstract: We will discuss the problem of stability for the rigidity part of the Riemannian positive mass theorem\, focusing on recent work with Kazaras and Khuri\, in which we proved that if one assumes a lower Ricci curvature bound\, then stability holds with respect to pointed Gromov-Hausdorff convergence.\n\n\n\n\n  \nTuesday\, April 5\, 2022 \n\n\n\n\nTime (ET)\nSpeaker\nTitle/Abstract\n\n\n9:30 am–10:30 am\nXinliang An\, National University of Singapore\n(virtual)\nTitle: Anisotropic dynamical horizons arising in gravitational collapse \nAbstract: Black holes are predicted by Einstein’s theory of general relativity\, and now we have ample observational evidence for their existence. However theoretically there are many unanswered questions about how black holes come into being and about the structures of their inner spacetime singularities. In this talk\, we will present several results in these directions. First\, in a joint work with Qing Han\, with tools from scale-critical hyperbolic method and non-perturbative elliptic techniques\, with anisotropic characteristic initial data we prove that: in the process of gravitational collapse\, a smooth and spacelike apparent horizon (dynamical horizon) emerges from general (both isotropic and anisotropic) initial data. This result extends the 2008 Christodoulou’s monumental work and it connects to black hole thermodynamics along the apparent horizon. Second\, in joint works with Dejan Gajic and Ruixiang Zhang\, for the spherically symmetric Einstein-scalar field system\, we derive precise blow-up rates for various geometric quantities along the inner spacelike singularities. These rates obey polynomial blow-up upper bounds; and when it is close to timelike infinity\, these rates are not limited to discrete finite choices and they are related to the Price’s law along the event horizon. This indicates a new blow-up phenomenon\, driven by a PDE mechanism\, rather than an ODE mechanism. If time permits\, some results on fluid dynamics will also be addressed.\n\n\n10:30 am–11:30 am\nSergiu Klainerman\, Princeton\n(virtual)\nTitle: Nonlinear stability of slowly rotating Kerr solutions \nAbstract: I will talk about the status of the stability of Kerr conjecture in General Relativity based on recent results obtained in collaboration with Jeremie Szeftel and Elena Giorgi.\n\n\n11:30 am–12:30 pm\nSiyuan Ma\, Sorbonne University\n(virtual)\nTitle: Sharp decay for Teukolsky master equation \nAbstract: I will talk about joint work with L. Zhang on deriving the late time dynamics of the spin $s$ components that satisfy the Teukolsky master equation in Kerr spacetimes.\n\n\n12:30 pm–1:30 pm\nBreak\n\n\n\n1:30 pm–2:30 pm\nJonathan Luk\, Stanford\n(virtual)\nTitle: A tale of two tails \nAbstract: Motivated by the strong cosmic censorship conjecture\, we introduce a general method for understanding the late-time tail for solutions to wave equations on asymptotically flat spacetimes in odd spatial dimensions. A particular consequence of the method is a re-proof of Price’s law-type results\, which concern the sharp decay rate of the late-time tails on stationary spacetimes. Moreover\, we show that the late-time tails are in general different from the stationary case in the presence of dynamical and/or nonlinear perturbations. This is a joint work with Sung-Jin Oh (Berkeley).\n\n\n2:30 pm–3:30 pm\nGary Horowitz\, University of California Santa Barbara\n(virtual)\nTitle: A new type of extremal black hole \nAbstract: I describe a family of four-dimensional\, asymptotically flat\, charged black holes that develop (charged) scalar hair as one increases their charge at fixed mass. Surprisingly\, the maximum charge for given mass is a nonsingular hairy black hole with a nondegenerate event horizon. Since the surface gravity is nonzero\, if quantum matter is added\, Hawking radiation does not appear to stop when this new extremal limit is reached. This raises the question of whether Hawking radiation will cause the black hole to turn into a naked singularity. I will argue that does not occur.\n\n\n3:30 pm–4:00 pm\nBreak\n\n\n\n4:00 pm–5:00 pm\nLydia Bieri\, University of Michigan\n(virtual)\nTitle: Gravitational radiation in general spacetimes \nAbstract: Studies of gravitational waves have been devoted mostly to sources such as binary black hole mergers or neutron star mergers\, or generally sources that are stationary outside of a compact set. These systems are described by asymptotically-flat manifolds solving the Einstein equations with sufficiently fast decay of the gravitational field towards Minkowski spacetime far away from the source. Waves from such sources have been recorded by the LIGO/VIRGO collaboration since 2015. In this talk\, I will present new results on gravitational radiation for sources that are not stationary outside of a compact set\, but whose gravitational fields decay more slowly towards infinity. A panorama of new gravitational effects opens up when delving deeper into these more general spacetimes. In particular\, whereas the former sources produce memory effects that are finite and of purely electric parity\, the latter in addition generate memory of magnetic type\, and both types grow. These new effects emerge naturally from the Einstein equations both in the Einstein vacuum case and for neutrino radiation. The latter results are important for sources with extended neutrino halos.\n\n\n\n\n  \nWednesday\, April 6\, 2022 \n\n\n\n\nTime (ET)\nSpeaker\nTitle/Abstract\n\n\n9:30 am–10:30 am\nGerhard Huisken\, Mathematisches Forschungsinstitut Oberwolfach\n(virtual)\nTitle: Space-time versions of inverse mean curvature flow \nAbstract: In order to extend the Penrose inequality from a time-symmetric setting to general asymptotically flat initial data sets several anisotropic generalisations of inverse mean curvature flow have been suggested that take the full space-time geometry into account. The lecture describes the properties of such flows and reports on recent joint work with Markus Wolff on inverse flow along the space-time mean curvature.\n\n\n10:30 am–11:30 am\nCarla Cederbaum\, Universität Tübingen\, Germany\n(virtual)\nTitle: Coordinates are messy \nAbstract: Asymptotically Euclidean initial data sets $(M\,g\,K)$ are characterized by the existence of asymptotic coordinates in which the Riemannian metric $g$ and second fundamental form $K$ decay to the Euclidean metric $\delta$ and to $0$ suitably fast\, respectively. Provided their matter densities satisfy suitable integrability conditions\, they have well-defined (ADM-)energy\, (ADM-)linear momentum\, and (ADM-)mass. This was proven by Bartnik using harmonic coordinates. To study their (ADM-)angular momentum and (BORT-)center of mass\, one usually assumes the existence of Regge—Teitelboim coordinates on the initial data set $(M\,g\,K)$ in question. We will give examples of asymptotically Euclidean initial data sets which do not possess any Regge—Teitelboim coordinates We will also show that harmonic coordinates can be used as a tool in checking whether a given asymptotically Euclidean initial data set possesses Regge—Teitelboim coordinates. This is joint work with Melanie Graf and Jan Metzger. We will also explain the consequences these findings have for the definition of the center of mass\, relying on joint work with Nerz and with Sakovich.\n\n\n11:30 am–12:30 pm\nStefanos Aretakis\, University of Toronto\n(virtual)\nTitle: Observational signatures for extremal black holes \nAbstract: We will present results regarding the asymptotics of scalar perturbations on black hole backgrounds. We will then derive observational signatures for extremal black holes that are based on global or localized measurements on null infinity. This is based on joint work with Gajic-Angelopoulos and ongoing work with Khanna-Sabharwal.\n\n\n12:30 pm–1:30 pm\nBreak\n\n\n\n1:30 pm–2:30 pm\nJared Speck\, Vanderbilt University\n(virtual)\nTitle: The mathematical theory of shock waves in multi-dimensional relativistic and non-relativistic compressible Euler flow \nAbstract: In the last two decades\, there have been dramatic advances in the rigorous mathematical theory of shock waves in solutions to the relativistic Euler equations and their non-relativistic analog\, the compressible Euler equations. A lot of the progress has relied on techniques that were developed to study Einstein’s equations. In this talk\, I will provide an overview of the field and highlight some recent progress on problems without symmetry or irrotationality assumptions. I will focus on results that reveal various aspects of the structure of the maximal development of the data and the corresponding implications for the shock development problem\, which is the problem of continuing the solution weakly after a shock. I will also describe various open problems\, some of which are tied to the Einstein–Euler equations. Various aspects of this program are joint with L. Abbrescia\, M. Disconzi\, and J. Luk.\n\n\n2:30 pm–3:30 pm\nLan-Hsuan Huang\, University of Connecticut\n(virtual)\nTitle: Null perfect fluids\, improvability of dominant energy scalar\, and Bartnik mass minimizers \nAbstract: We introduce the concept of improvability of the dominant energy scalar\, and we derive strong consequences of non-improvability. In particular\, we prove that a non-improvable initial data set without local symmetries must sit inside a null perfect fluid spacetime carrying a global Killing vector field. We also show that the dominant energy scalar is always almost improvable in a precise sense. Using these main results\, we provide a characterization of Bartnik mass minimizing initial data sets which makes substantial progress toward Bartnik’s stationary conjecture. \nAlong the way we observe that in dimensions greater than eight there exist pp-wave counterexamples (without the optimal decay rate for asymptotically flatness) to the equality case of the spacetime positive mass theorem. As a consequence\, we find counterexamples to Bartnik’s stationary and strict positivity conjectures in those dimensions. This talk is based on joint work with Dan A. Lee.\n\n\n3:30 pm–4:00 pm\nBreak\n\n\n\n4:00 pm–5:00 pm\nDemetre Kazaras\, Duke University\n(virtual)\nTitle: Comparison geometry for scalar curvature and spacetime harmonic functions \nAbstract: Comparison theorems are the basis for our geometric understanding of Riemannian manifolds satisfying a given curvature condition. A remarkable example is the Gromov-Lawson toric band inequality\, which bounds the distance between the two sides of a Riemannian torus-cross-interval with positive scalar curvature by a sharp constant inversely proportional to the scalar curvature’s minimum. We will give a new qualitative version of this and similar band-type inequalities in dimension 3 using the notion of spacetime harmonic functions\, which recently played the lead role in our recent proof of the positive mass theorem. This is joint work with Sven Hirsch\, Marcus Khuri\, and Yiyue Zhang.\n\n\n\n\n  \nThursday\, April 7\, 2022 \n\n\n\n\nTime (ET)\nSpeaker\nTitle/Abstract\n\n\n9:30 am–10:30 am\nPiotr Chrusciel\, Universitat Wien\n(virtual)\nTitle: Maskit gluing and hyperbolic mass \nAbstract: “Maskit gluing” is a gluing construction for asymptotically locally hyperbolic (ALH) manifolds with negative cosmological constant. I will present a formula for the mass of Maskit-glued ALH manifolds and describe how it can be used to construct general relativistic initial data with negative mass.\n\n\n10:30 am–11:30 am\nGreg Galloway\, University of Miami (virtual)\nTitle:  Initial data rigidity and applications \nAbstract:  We present a result from our work with Michael Eichmair and Abraão Mendes concerning initial data rigidity results (CMP\, 2021)\, and look at some consequences.  In a note with Piotr Chruściel (CQG 2021)\, we showed how to use this result\, together with arguments from Chruściel and Delay’s proof of the their hyperbolic PMT result\, to obtain a hyperbolic PMT result with boundary.  This will also be discussed.\n\n\n11:30 am–12:30 pm\nPengzi Miao\, University of Miami\n(virtual)\nTitle: Some remarks on mass and quasi-local mass \nAbstract: In the first part of this talk\, I will describe how to detect the mass of asymptotically flat and asymptotically hyperbolic manifolds via large Riemannian polyhedra. In the second part\, I will discuss an estimate of the Bartnik quasi-local mass and its geometric implications. This talk is based on several joint works with A. Piubello\, and with H.C. Jang.\n\n\n12:30 pm–1:30 pm\nBreak\n\n\n\n1:30 pm–2:30 pm\nYakov Shlapentokh Rothman\, Princeton\n(hybrid: in person & virtual)\nTitle: Self-Similarity and Naked Singularities for the Einstein Vacuum Equations \nAbstract: We will start with an introduction to the problem of constructing naked singularities for the Einstein vacuum equations\, and then explain our discovery of a fundamentally new type of self-similarity and show how this allows us to construct solutions corresponding to a naked singularity. This is joint work with Igor Rodnianski.\n\n\n2:30 pm–3:30 pm\nMarcelo Disconzi\, Vanderbilt University\n(virtual)\nTitle: General-relativistic viscous fluids. \nAbstract: The discovery of the quark-gluon plasma that forms in heavy-ion collision experiments provides a unique opportunity to study the properties of matter under extreme conditions\, as the quark-gluon plasma is the hottest\, smallest\, and densest fluid known to humanity. Studying the quark-gluon plasma also provides a window into the earliest moments of the universe\, since microseconds after the Big Bang the universe was filled with matter in the form of the quark-gluon plasma. For more than two decades\, the community has intensely studied the quark-gluon plasma with the help of a rich interaction between experiments\, theory\, phenomenology\, and numerical simulations. From these investigations\, a coherent picture has emerged\, indicating that the quark-gluon plasma behaves essentially like a relativistic liquid with viscosity. More recently\, state-of-the-art numerical simulations strongly suggested that viscous and dissipative effects can also have non-negligible effects on gravitational waves produced by binary neutron star mergers. But despite the importance of viscous effects for the study of such systems\, a robust and mathematically sound theory of relativistic fluids with viscosity is still lacking. This is due\, in part\, to difficulties to preserve causality upon the inclusion of viscous and dissipative effects into theories of relativistic fluids. In this talk\, we will survey the history of the problem and report on a new approach to relativistic viscous fluids that addresses these issues.\n\n\n3:30 pm–4:00 pm\nBreak\n\n\n\n4:00 pm–5:00 pm\nMaxime van de Moortel\, Princeton\n(hybrid: in person & virtual)\nTitle: Black holes: the inside story of gravitational collapse \nAbstract: What is inside a dynamical black hole? While the local region near time-like infinity is understood for various models\, the global structure of the black hole interior has largely remained unexplored.\nThese questions are deeply connected to the nature of singularities in General Relativity and celebrated problems such as Penrose’s Strong Cosmic Censorship Conjecture.\nI will present my recent resolution of these problems in spherical gravitational collapse\, based on the discovery of a novel phenomenon: the breakdown of weak singularities and the dynamical formation of a strong singularity.\n\n\n\n\n  \nFriday\, April 8\, 2022 \n\n\n\n\nTime (ET)\nSpeaker\nTitle/Abstract\n\n\n9:30 am–10:30 am\nYe-Kai Wang\, National Cheng Kun University\, Taiwan\n(virtual)\nTitle: Supertranslation invariance of angular momentum at null infinity in double null gauge \nAbstract: This talk accompanies Po-Ning Chen’s talk on Monday with the results described in the double null gauge rather than Bondi-Sachs coordinates. Besides discussing\nhow Chen-Wang-Yau angular momentum resolves the supertranslation ambiguity\, we also review the definition of angular momentum defined by A. Rizzi. The talk is based on the joint work with Po-Ning Chen\, Jordan Keller\, Mu-Tao Wang\, and Shing-Tung Yau.\n\n\n10:30 am–11:30 am\nZoe Wyatt\, King’s College London\n(virtual)\nTitle: Global Stability of Spacetimes with Supersymmetric Compactifications \nAbstract: Spacetimes with compact directions which have special holonomy\, such as Calabi-Yau spaces\, play an important role in\nsupergravity and string theory. In this talk I will discuss a recent work with Lars Andersson\, Pieter Blue and Shing-Tung Yau\, where we show the global\, nonlinear stability a spacetime which is a cartesian product of a high dimensional Minkowski space with a compact Ricci flat internal space with special holonomy. This stability result is related to a conjecture of Penrose concerning the validity of string theory. Our proof uses the intersection of methods for quasilinear wave and Klein-Gordon equations\, and so towards the end of the talk I will also comment more generally on coupled wave–Klein-Gordon equations.\n\n\n11:30 am–12:30 pm\nElena Giorgi\, Columbia University\n(hybrid: in person & virtual)\nTitle: The stability of charged black holes \nAbstract: Black hole solutions in General Relativity are parametrized by their mass\, spin and charge. In this talk\, I will motivate why the charge of black holes adds interesting dynamics to solutions of the Einstein equation thanks to the interaction between gravitational and electromagnetic radiation. Such radiations are solutions of a system of coupled wave equations with a symmetric structure which allows to define a combined energy-momentum tensor for the system. Finally\, I will show how this physical-space approach is resolutive in the most general case of Kerr-Newman black hole\, where the interaction between the radiations prevents the separability in modes.\n\n\n12:30 pm–1:30 pm\nBreak\n\n\n\n1:30 pm–2:30 pm\nMarcus Khuri\, Stony Brook University\n(virtual)\nTitle: The mass-angular momentum inequality for multiple black holes\n\nAbstract: Consider a complete 3-dimensional initial data set for the Einstein equations which has multiple asymptotically flat or asymptotically cylindrical ends. If it is simply connected\, axisymmetric\, maximal\, and satisfies the appropriate energy condition then the ADM mass of any of the asymptotically flat ends is bounded below by the square root of the total angular momentum. This generalizes previous work of Dain\, Chrusciel-Li-Weinstein\, and Schoen-Zhou which treated either the single black hole case or the multiple black hole case without an explicit lower bound. The proof relies on an analysis of the asymptotics of singular harmonic maps from\nR^3 \ \Gamma –>H^2   where \Gamma is a coordinate axis. This is joint work with Q. Han\, G. Weinstein\, and J. Xiong.\n\n\n2:30 pm–3:30 pm\nMartin Lesourd\, Harvard\n(hybrid: in person & virtual)\nTitle:  A Snippet on Mass and the Topology and Geometry of Positive Scalar Curvature \nAbstract:  I will talk about a small corner of the study of Positive Scalar Curvature (PSC) and questions which are most closely related to the Positive Mass Theorem. The classic questions are ”which topologies allow for PSC?” and ”what is the geometry of manifolds with PSC?”. This is based on joint work with Prof. S-T. Yau\, Prof. D. A. Lee\, and R. Unger.\n\n\n3:30 pm–4:00 pm\nBreak\n\n\n\n4:00 pm–5:00 pm\nGeorgios Moschidis\, Princeton\n(virtual)\nTitle: Weak turbulence for the Einstein–scalar field system. \nAbstract: In the presence of confinement\, the Einstein field equations are expected to exhibit turbulent dynamics. In the presence of a negative cosmological constant\, the AdS instability conjecture claims the existence of arbitrarily small perturbations to the initial data of Anti-de Sitter spacetime which\, under evolution by the vacuum Einstein equations with reflecting boundary conditions at conformal infinity\, lead to the formation of black holes after sufficiently long time.\nIn this talk\, I will present a rigorous proof of the AdS instability conjecture in the setting of the spherically symmetric Einstein-scalar field system. The construction of the unstable initial data will require carefully designing a family of initial configurations of localized matter beams and estimating the exchange of energy taking place between interacting beams over long periods of time\, as well as estimating the decoherence rate of those beams.
URL:https://cmsa.fas.harvard.edu/event/general-relativity-conference/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/GR-Conference.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210824
DTEND;VALUE=DATE:20210825
DTSTAMP:20260504T060850
CREATED:20230705T081718Z
LAST-MODIFIED:20250328T145235Z
UID:10000070-1629763200-1629849599@cmsa.fas.harvard.edu
SUMMARY:Big Data Conference 2021
DESCRIPTION:On August 24\, 2021\, the CMSA hosted our seventh annual Conference on Big Data. The Conference features many speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. \nThe 2021 Big Data Conference took place virtually on Zoom. \nOrganizers:  \n\nShing-Tung Yau\, William Caspar Graustein Professor of Mathematics\, Harvard University\nScott Duke Kominers\, MBA Class of 1960 Associate Professor\, Harvard Business\nHorng-Tzer Yau\, Professor of Mathematics\, Harvard University\nSergiy Verstyuk\, CMSA\, Harvard University\n\nSpeakers: \n\nAndrew Blumberg\, University of Texas at Austin\nMoran Koren\, Harvard CMSA\nHima Lakkaraju\, Harvard University\nKatrina Ligett\, The Hebrew University of Jerusalem\n\n\n\n\n\nTime (ET; Boston time)\nSpeaker\nTitle/Abstract\n\n\n9:00AM\nConference Organizers\nIntroduction and Welcome\n\n\n9:10AM – 9:55AM\nAndrew Blumberg\, University of Texas at Austin\nTitle: Robustness and stability for multidimensional persistent homology \nAbstract: A basic principle in topological data analysis is to study the shape of data by looking at multiscale homological invariants. The idea is to filter the data using a scale parameter that reflects feature size. However\, for many data sets\, it is very natural to consider multiple filtrations\, for example coming from feature scale and density. A key question that arises is how such invariants behave with respect to noise and outliers. This talk will describe a framework for understanding those questions and explore open problems in the area.\n\n\n10:00AM – 10:45AM\nKatrina Ligett\, The Hebrew University of Jerusalem\nTitle: Privacy as Stability\, for Generalization \nAbstract: Many data analysis pipelines are adaptive: the choice of which analysis to run next depends on the outcome of previous analyses. Common examples include variable selection for regression problems and hyper-parameter optimization in large-scale machine learning problems: in both cases\, common practice involves repeatedly evaluating a series of models on the same dataset. Unfortunately\, this kind of adaptive re-use of data invalidates many traditional methods of avoiding overfitting and false discovery\, and has been blamed in part for the recent flood of non-reproducible findings in the empirical sciences. An exciting line of work beginning with Dwork et al. in 2015 establishes the first formal model and first algorithmic results providing a general approach to mitigating the harms of adaptivity\, via a connection to the notion of differential privacy. In this talk\, we’ll explore the notion of differential privacy and gain some understanding of how and why it provides protection against adaptivity-driven overfitting. Many interesting questions in this space remain open. \nJoint work with: Christopher Jung (UPenn)\, Seth Neel (Harvard)\, Aaron Roth (UPenn)\, Saeed Sharifi-Malvajerdi (UPenn)\, and Moshe Shenfeld (HUJI). This talk will draw on work that appeared at NeurIPS 2019 and ITCS 2020\n\n\n10:50AM – 11:35AM\nHima Lakkaraju\, Harvard University\nTitle: Towards Reliable and Robust Model Explanations \nAbstract: As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice\, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this talk\, I will present some of our recent research that sheds light on the vulnerabilities of popular post hoc explanation techniques such as LIME and SHAP\, and also introduce novel methods to address some of these vulnerabilities. More specifically\, I will first demonstrate that these methods are brittle\, unstable\, and are vulnerable to a variety of adversarial attacks. Then\, I will discuss two solutions to address some of the vulnerabilities of these methods – (i) a framework based on adversarial training that is designed to make post hoc explanations more stable and robust to shifts in the underlying data; (ii) a Bayesian framework that captures the uncertainty associated with post hoc explanations and in turn allows us to generate explanations with user specified levels of confidences. I will conclude the talk by discussing results from real world datasets to both demonstrate the vulnerabilities in post hoc explanation techniques as well as the efficacy of our aforementioned solutions.\n\n\n11:40AM – 12:25PM\nMoran Koren\, Harvard CMSA\nTitle: A Gatekeeper’s Conundrum \nAbstract: Many selection processes contain a “gatekeeper”. The gatekeeper’s goal is to examine an applicant’s suitability to a proposed position before both parties endure substantial costs. Intuitively\, the introduction of a gatekeeper should reduce selection costs as unlikely applicants are sifted out. However\, we show that this is not always the case as the gatekeeper’s introduction inadvertently reduces the applicant’s expected costs and thus interferes with her self-selection. We study the conditions under which the gatekeeper’s presence improves the system’s efficiency and those conditions under which the gatekeeper’s presence induces inefficiency. Additionally\, we show that the gatekeeper can sometimes improve selection correctness by behaving strategically (i.e.\, ignore her private information with some probability).\n\n\n12:25PM\nConference Organizers\nClosing Remarks
URL:https://cmsa.fas.harvard.edu/event/big-data-conference-2021/
LOCATION:Virtual
CATEGORIES:Big Data Conference,Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/BD_21-Poster.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200824T100000
DTEND;TZID=America/New_York:20200825T140500
DTSTAMP:20260504T060850
CREATED:20230707T104105Z
LAST-MODIFIED:20250305T185337Z
UID:10000137-1598263200-1598364300@cmsa.fas.harvard.edu
SUMMARY:2020 Big Data Conference (Virtual)
DESCRIPTION:On August 24-25\, 2020 the CMSA hosted our sixth annual Conference on Big Data. The Conference featured many speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. The 2020 Big Data Conference took place virtually. \n\nVideos of the talks are available in this youtube playlist.\n  \nOrganizers:  \n\nShing-Tung Yau\, William Caspar Graustein Professor of Mathematics\, Harvard University\nScott Duke Kominers\, MBA Class of 1960 Associate Professor\, Harvard Business\nHorng-Tzer Yau\, Professor of Mathematics\, Harvard University\nSergiy Verstyuk\, CMSA\, Harvard University\n\nSpeakers:\n \n\nSanjeev Arora\, Princeton University\nJuan Camilo Castillo\, University of Pennsylvania\nJoseph Dexter\, Dartmouth College\nNicole Immorlica\, Microsoft\nAmin Saberi\, Stanford University\nVira Semenova\, University of California\, Berkeley\nVarda Shalev\, Tel Aviv University
URL:https://cmsa.fas.harvard.edu/event/2020-big-data-conference-virtual/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Big Data Conference,Conference,Event
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/Big-Data-2020-pdf.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191101T090000
DTEND;TZID=America/New_York:20191101T170000
DTSTAMP:20260504T060850
CREATED:20230715T072841Z
LAST-MODIFIED:20250305T211543Z
UID:10000122-1572598800-1572627600@cmsa.fas.harvard.edu
SUMMARY:Learning from health data in the million genome era
DESCRIPTION:On November 1\, 2019 the CMSA will be hosting a conference organized by Seven Bridges Genomics. The workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA. \nProjects currently underway around the world are collecting detailed health and genomic data from millions of volunteers. In parallel\, numerous healthcare systems have announced commitments to integrate genomic data into the standard of care for select patients. These data have the potential to reveal transformative insights into health and disease. However\, to realize this promise\, novel approaches are required across the full life cycle of data analysis. This symposium will include discussion of advanced statistical and algorithmic approaches to draw insights from petabyte scale genomic and health data; success stories to date; and a view towards the future of clinical integration of genomics in the learning health system. \nSpeakers:  \n\nHeidi Rehm\, Ph.D.\nChief Genomics Officer\, MGH; Professor of Pathology\, MGH\, BWH & Harvard Medical School; Medical Director\, Broad Institute Clinical Research Sequencing Platform.\nSaiju Pyarajan\, Ph.D.\nDirector\, Centre for Data and Computational Sciences\,VABHS\, and Department of Medicine\, BWH and HMS\nTianxi Cai\, Sci.D\nJohn Rock Professor of Population and Translational Data Sciences\, Department of Biostatistics\, Harvard School of Public Health\nSusan Redline\, M.D.\, M.P.H\nFarrell Professor of Sleep MedicineHarvard Medical School\, Brigham and Women’s Hospital and Beth Israel Deaconess Medical Center\nAvinash Sahu\, Ph.D.\nPostdoctoral Research Fellow\, Dana Farber Cancer Institute\, Harvard School of Public Health\nPeter J. Park\, Ph.D.\nProfessor of Biomedical Informatics\, Department of Biomedical Informatics\, Harvard Medical School\nDavid Roberson\nCommunity Engagement Manager\, Seven Bridges
URL:https://cmsa.fas.harvard.edu/event/learning-from-health-data-in-the-million-genome-era/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/SEVENB0051-POSTER-Harvard-Seminar-REV1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190819T083000
DTEND;TZID=America/New_York:20190820T164000
DTSTAMP:20260504T060850
CREATED:20230707T174003Z
LAST-MODIFIED:20250328T145128Z
UID:10000116-1566203400-1566319200@cmsa.fas.harvard.edu
SUMMARY:2019 Big Data Conference
DESCRIPTION:On August 19-20\, 2019 the CMSA hosted the fifth annual Conference on Big Data. The Conference will featured many speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. \nThe talks will take place in Science Center Hall D\, 1 Oxford Street. \nVideos can be found in the Youtube playlist.
URL:https://cmsa.fas.harvard.edu/event/2019-big-data-conference/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Big Data Conference,Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Big-Data-2019-Poster-5-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190502T090000
DTEND;TZID=America/New_York:20190505T170000
DTSTAMP:20260504T060850
CREATED:20230715T175235Z
LAST-MODIFIED:20250328T145104Z
UID:10000115-1556787600-1557075600@cmsa.fas.harvard.edu
SUMMARY:Conference on Differential Geometry\, Calabi-Yau theory and General Relativity: A conference in honor of the 70th Birthday of Shing-Tung Yau
DESCRIPTION:On May 2-5\, 2019 the Harvard Mathematics Department hosted a Conference on Differential Geometry\, Calabi-Yau Theory and General Relativity: A conference in honor of the 70th Birthday of Shing-Tung Yau. The conference was held in the  Science Center\, Lecture Hall C.  \nOrganizers:\n\nHorng-Tzer Yau (Harvard)\nWilfried Schmid (Harvard)\nClifford Taubes (Harvard)\nCumrun Vafa (Harvard)\n\nSpeakers:\n\nLydia Bieri\, University of Michigan\nTristan Collins\, MIT\nSimon Donaldson\, Imperial College\nFan Chung Graham\, UC San Diego\nNigel Hitchin\, Oxford University\nJun Li\, Stanford University\nKefeng Liu\, UCLA\nChiu-Chu Melissa Liu\, Columbia University\nAlina Marian\, Northeastern University\nXenia de la Ossa\, Oxford University\nDuong H. Phong\, Columbia University\nRichard Schoen\, UC Irvine\nAndrew Strominger\, Harvard University\nNike Sun\, MIT\nClifford Taubes\, Harvard University\nChuu-Lian Terng\, UC Irvine\nValentino Tosatti\, Northwestern University\nKaren Uhlenbeck\, University of Texas\nCumrun Vafa\, Harvard University\nMu Tao Wang\, Columbia University\nEdward Witten\, IAS\nStephen Yau\, Tsinghua University\, P.R. China
URL:https://cmsa.fas.harvard.edu/event/conference-on-differential-geometry-calabi-yau-theory-and-general-relativity-a-conference-in-honor-of-the-70th-birthday-of-shing-tung-yau/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Yau-2-2-791x1024-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190429T090000
DTEND;TZID=America/New_York:20190501T170000
DTSTAMP:20260504T060850
CREATED:20230715T174721Z
LAST-MODIFIED:20250304T214254Z
UID:10000114-1556528400-1556730000@cmsa.fas.harvard.edu
SUMMARY:Conference on Algebraic Geometry\, Representation theory and Mathematical Physics
DESCRIPTION:From April 29 to May 1\, 2019 the CMSA will be hosting a Conference on Algebraic Geometry\, Representation theory and Mathematical Physics. This workshop is organized by Bong Lian (Brandeis) and Artan Sheshmani (CMSA) . The workshop will be held in room G10 of the CMSA\, located at 20 Garden Street\, Cambridge\, MA.   \nVideos\nSpeakers: \n\nDan Abramovich\, Brown\nRoman Bezrukavnikov\, MIT\nFedor Bogomolov\, NYU\nQile Chen\, Boston College\nDawei Chen\, Boston College\nAlexander Efimov\, Moscow\nPavel Etingof\, MIT\nMaksym Fedorchuk\, Boston College\nDennis Gaitsgory\, Harvard\nAmin Gholampour\, Maryland\nBrendan Hassett\, Brown\nLudmil Katzarkov\, Miami & Moscow\nSi Li\, Tsinghua\nAndrei Negut\, MIT\nYuri Tschinkel\, NYU\nWei Zhang\, MIT\n\n  \nMonday\, April 29 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n8:30 – 9:00am\nBreakfast\n\n\n\n9:00 – 10:00am\nWei Zhang\, MIT\nTitle: The arithmetic fundamental lemma for diagonal cycles \nAbstract: I’ll recall the Gross–Zagier theorem and a high dimensional generalization\, the arithmetic Gan-Gross-Prasad conjecture\, which relates the height pairing of arithmetic diagonal cycles on certain shimura varieties to the first order derivative of certain L-functions.  The arithmetic fundamental lemma conjecture arises from the relative trace formula approach to this conjecture. I will recall the statement of the arithmetic fundamental lemma and outline a proof.\n\n\n10:00 – 10:30am\nBreak\n\n\n\n10:30 – 11:30am\nYuri Tschinkel\, NYU\nTitle: Equivariant birational geometry and modular symbols \nAbstract: We introduce new invariants in equivariant birational geometry and study their relation to modular symbols and cohomology of arithmetic groups (joint with M. Kontsevich and V. Pestun).\n\n\n11:30 – 1:30pm\nLunch\n\n\n\n1:30 – 2:30pm\nAlexander Efimov\, Moscow\nTitle: Torsionness for regulators of canonical extensions \nAbstract: I will sketch a generalization of the results of Iyer and Simpson arXiv:0707.0372 to the general case of a normal-crossings divisor at infinity.\n\n\n2:30 – 3:00pm\nBreak\n\n\n\n3:00 – 4:00pm\nAmin Gholampour\, Maryland\nTitle: Euler Characteristics of punctual quot schemes on threefolds \nAbstract: Let F be a homological dimension 1 torsion free sheaf on a nonsingular quasi-projective threefold. The first cohomology of the derived dual of F is a 1-dimension sheaf G supported on the singular locus of F. We prove a wall-crossing formula relating the generating series of the Euler characteristics of Quot(F\, n) and Quot(G\,n)\, where Quot(-\,n) denotes the quot scheme of length n quotients. We will use this relation in studying the Euler characteristics of the moduli spaces of stable torsion free sheaves on nonsingular projective threefolds. This is a joint work with Martijn Kool.\n\n\n4:00 – 4:30pm\nBreak\n\n\n\n4:30 – 5:30pm\nMaksym Fedorchuck\, BC\nTitle:  Stability of one-parameter families of weighted hypersurfaces \nAbstract:  We define a notion of stability for fibrations over a curve with generic fibers being weighted hypersurfaces (in some weighted projective space) generalizing Kollár’s stability for families of hypersurfaces in a projective space.  The stability depends on a choice of an effective line bundle on the parameter space of weighted hypersurfaces and different choices pick out different birational model of the total space of the fibration. I will describe enumerative geometry that goes into understanding these stability conditions\, and\, if time permits\, examples where this machinery can be used to produce birational models with good properties.  Joint work with Hamid Ahmadinezhad and Igor Krylov.\n\n\n\n  \nTuesday\, April 30 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n8:30 – 9:00am\nBreakfast\n\n\n\n9:00 – 10:00am\nBrendan Hassett\, Brown\nTitle: Rationality for geometrically rational threefolds \nAbstract: We consider rationality questions for varieties over non-closed fields that become rational over an algebraic closure\, like smooth complete intersections of two quadrics.  (joint with Tschinkel)\n\n\n10:00 – 10:30am\nBreak\n\n\n\n10:30 – 11:30am\nDennis Gaitsgory\, Harvard\nTitle: The Fundamental Local Equivalence in quantum geometric Langlands \nAbstract: The Fundamental Local Equivalence is statement that relates the q-twisted  Whittaker category of the affine Grassmannian for the group G and the category of modules over the Langlands dual “big” quantum group. The non-triviaiity of the statement lies is the fact that the relationship between the group and its  dual is combinatorial\, so to prove the FLE one needs to express both sides in combinatorial terms. In the talk we will indicate the proof of a related statement for the “small” quantum group. The combinatorial link is provided by the category of factorization modules over a certain factorization algebra\, which in itself is a geometric device that concisely encodes the root data.\n\n\n11:30 – 1:00pm\nLunch\n\n\n\n1:00- 2:00pm\nAndrei Negut\, MIT\nTitle: AGT relations in geometric representation theory \nAbstract: I will survey a program that seeks to translate the Alday-Gaiotto-Tachikawa correspondence (between gauge theory on R^4 and conformal field theory) into the language of algebraic geometry. The objects of study become moduli spaces of sheaves on surfaces\, and the goal is to connect them with the W-algebra of type gl_n.\n\n\n2:00 – 2:15pm\nBreak\n\n\n\n2:15 – 3:15pm\nDan Abramovich\, Brown\nTitle: Resolution in characteristic 0 using weighted blowing up \nAbstract: Given a variety $X$\, one wants to blow up the worst singular locus\, show that it gets better\, and iterate until the singularities are resolved. \nExamples such as the whitney umbrella show that this iterative process cannot be done by blowing up smooth loci – it goes into a loop. \nWe show that there is a functorial way to resolve varieties using \emph{weighted} blowings up\, in the stack-theoretic sense. To an embedded variety $X \subset Y$ one functorially assigns an invariant $(a_1\,\ldots\,a_k)$\, and a center locally of the form $(x_1^{a_1} \, \ldots \, x_k^{a_k})$\, whose stack-theoretic weighted blowing up has strictly smaller invariant under the lexicographic order. \nThis is joint work with Michael Tëmkin (Jerusalem) and Jaroslaw Wlodarczyk (Purdue)\, a side product of our work on functorial semistable reduction. A similar result was discovered by G. Marzo and M. McQuillan.\n\n\n3:15 – 3:30pm\nBreak\n\n\n\n3:30 – 4:30pm\nFedor Bogomolov\, NYU\nTitle: On the base of a Lagrangian fibration for a compact hyperkahler manifold. \nAbstract: In my talk I will discuss our proof with N. Kurnosov that the base of such fibration for complex projective manifold hyperkahler manifold of dimension $4$ is always a projective plane $P^2$. In fact we show that the base of such fibration can not have a singular point of type $E_8$. It was by the theorem of Matsushita and others that only quotient singularities can occur and if the base is smooth then the it is isomorphic to $P^2$. The absence of other singularities apart from $E_8$ has been already known and we show that $E-8$ can not occur either. Our method can be applied to other types of singularities for the study of  Lagrangian fibrations in higher dimensions More recently similar result was obtained by Huybrechts and Xu.\n\n\n4:30 – 4:45pm\nBreak\n\n\n\n4:45 – 5:45pm\nDawei Chen\, BC\nTitle: Volumes and intersection theory on moduli spaces of Abelian differentials \nAbstract: Computing volumes of moduli spaces has significance in many fields. For instance\, Witten’s conjecture regarding intersection numbers on moduli spaces of Riemann surfaces has a fascinating connection to the Weil-Petersson volume\, which motivated Mirzakhani to give a proof via Teichmueller theory\, hyperbolic geometry\, and symplectic geometry. In this talk I will introduce an analogue of Witten’s intersection numbers on moduli spaces of Abelian differentials to compute the Masur-Veech volumes induced by the flat metric associated with Abelian differentials. This is joint work with Moeller\, Sauvaget\, and Zagier (arXiv:1901.01785).\n\n\n\n  \nWednesday\, May 1 \n\n\n\nTime\nSpeaker\nTitle/Abstract\n\n\n8:30 – 9:00am\nBreakfast\n\n\n\n9:00 – 10:00am\nPavel Etingof\, MIT\nTitle: Short star-products for filtered quantizations \nThis is joint work with Eric Rains and Douglas Stryker.\n\n\n10:00 – 10:30am\nBreak\n\n\n\n10:30 – 11:30am\nRoman Bezrukavnikov\, MIT\nTitle: Stability conditions and representation theory \nAbstract: I will recall the concept of real variation of stabilities (introduced in my work with Anno and Mirkovic)\nand its relation to modular Lie algebra representations. I will also address a potential generalization of that picture\nto modular representations of affine Lie algebras related to the classical limit of geometric Langlands duality and its local counterpart.\n\n\n11:30 – 11:45am\nBreak\n\n\n\n11:45 – 12:45pm\nQile Chen\, BC\nTitle: Counting curves in critical locus via logarithmic compactification \nAbstract: An R-map consists of a pre-stable map to possibly non-GIT quotient together with sections of certain spin bundles. The moduli of R-maps are in general non-compact. When the target of R-maps is equipped with a super-potential W with compact critical locus\, using Kiem-Li cosection localization it has been proved by many authors in various settings that the virtual cycle of R-maps can be represented by the cosection localized virtual cycle which is supported on the proper locus consisting of R-maps in the critical locus of W. Though the moduli of R-maps is equipped with a natural torus action by scaling of the spin bundles\, the non-compactness of the R-maps moduli makes such powerful torus action useless. \nIn this talk\, I will introduce a logarithmic compactification of the moduli of R-maps using certain modifications of stable logarithmic maps. The logarithmic moduli space carries a canonical virtual cycle from the logarithmic deformation theory. In the presence of a super-potential with compact critical locus\, it further carries a reduced virtual cycle. We prove that (1) the reduced virtual cycle of the compactification can be represented by the cosection localized virtual cycle; and (2) the difference of the canonical and reduced virtual cycles is another reduced virtual cycle supported along the logarithmic boundary. As an application\, one recovers the Gromov-Witten invariants of the critical locus as the invariants of logarithmic R-maps of its ambient space in an explicit form. The latter can be calculated using the spin torus action. \nThis is a joint work with Felix Janda and Yongbin Ruan.\n\n\n12:45 – 2:30pm\nLunch\n\n\n\n2:30 – 3:30pm\nSi Li\, Tsinghua\nTitle: Semi-infinite Hodge structure: from BCOV theory to Seiberg-Witten geometry \nAbstract: I will explain how the semi-infinite Hodge theory extends Kodaira-Spencer gravity (Bershadsky-Cecotti-Ooguri-Vafa theory of B-twisted closed topological string field theory) into a full solution of Batalin-Vilkovisky master equation. This allows us to formulate quantum B-model via a rigorous BV quantization method and construct integrable hierarchies arising naturally from the background symmetry. In the second part of the talk\, I will explain the recent discovery of the connection between K.Saito’s primitive form and 4d N=2 Seiberg-Witten geometry arising from singularity theory.\n\n\n3:30 – 4:00pm\nBreak\n\n\n\n4:00 – 5:00pm\nLudmil Katzarkov\, Moscow\nTitle: PDE’s non commutative  motives and HMS. \nAbstract: In this talk we will discuss the theory of central manifolds and the new structures in geometry it produces. Application to Bir.  Geometry will be discussed.\n\n\n\n 
URL:https://cmsa.fas.harvard.edu/event/conference-on-algebraic-geometry-representation-theory-and-mathematical-physics/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/algebraic-geo-conference-final-795x1024-1-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181116T080000
DTEND;TZID=America/New_York:20181117T170000
DTSTAMP:20260504T060850
CREATED:20230715T085736Z
LAST-MODIFIED:20241212T191652Z
UID:10000102-1542355200-1542474000@cmsa.fas.harvard.edu
SUMMARY:Current Developments In Mathematics 2018
DESCRIPTION:Current Developments in Mathematics 2018 Conference. \nFriday\, Nov. 16\, 2018 2:15 pm – 6:00 pm \nSaturday\, Nov. 17\, 2018  9:00 am – 5:00 pm \nHarvard University Science Center\, Hall B \nYoutube Playlist
URL:https://cmsa.fas.harvard.edu/event/current-developments-in-mathematics-2018/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/cdm-2018-poster.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180823T083000
DTEND;TZID=America/New_York:20180824T163000
DTSTAMP:20260504T060850
CREATED:20230715T083801Z
LAST-MODIFIED:20250415T154139Z
UID:10000086-1535013000-1535128200@cmsa.fas.harvard.edu
SUMMARY:Big Data Conference 2018
DESCRIPTION:On August 23-24\, 2018 the CMSA hosted the fourth annual Conference on Big Data. The Conference featured speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. \nThe talks were held in Science Center Hall B\, 1 Oxford Street. \nSpeakers:  \n\nMohammad Akbarpour\, Stanford\nEmily Breza\, Harvard\nFrancesca Dominici\, Harvard\nChiara Farronato\, Harvard\nKobi Gal\, Ben Gurion\nJonah Kallenbach\, Reverie Labs\nSamuel Kou\, Harvard\nLaura Kreidberg\, Harvard\nDanielle Li\, MIT\nLibby Mishkin\, Uber\nJosh Speagle\, Harvard\nWilliam Stein\, University of Washington\nAlex Teyltelboym\, University of Oxford\nSergiy Verstyuk\, CMSA/Harvard\n\nOrganizers:  \n\nShing-Tung Yau\, William Caspar Graustein Professor of Mathematics\, Harvard University\nScott Duke Kominers\, MBA Class of 1960 Associate Professor\, Harvard Business\nRichard Freeman\, Herbert Ascherman Professor of Economics\, Harvard University\nJun Liu\, Professor of Statistics\, Harvard University\nHorng-Tzer Yau\, Professor of Mathematics\, Harvard University
URL:https://cmsa.fas.harvard.edu/event/2018-big-data-conference-2/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Big Data Conference,Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Big-Data-2018-4.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180818T083000
DTEND;TZID=America/New_York:20180820T172000
DTSTAMP:20260504T060850
CREATED:20230715T083526Z
LAST-MODIFIED:20250304T213419Z
UID:10000084-1534581000-1534785600@cmsa.fas.harvard.edu
SUMMARY:From Algebraic Geometry to Vision and AI: A Symposium Celebrating the Mathematical Work of David Mumford
DESCRIPTION:On August 18 and 20\, 2018\, the Center of Mathematic Sciences and Applications and the Harvard University Mathematics Department hosted a conference on From Algebraic Geometry to Vision and AI: A Symposium Celebrating the Mathematical Work of David Mumford. The talks took place in Science Center\, Hall B. \nSaturday\, August 18th:  A day of talks on Vision\, AI and brain sciences \nMonday\, August 20th: a day of talks on Math \nSpeakers: \n\nStuart Geman\, Brown\nJanos Kollar\, Princeton\nTai Sing Lee\, CMU\nEmanuele Macri\, Northeastern\nJitendra Malik\, Berkeley / FAIR\nPeter Michor\, University of Vienna\nMichael Miller\, Johns Hopkins\nAaron Pixton\, MIT\nJayant Shah\, Northeastern\nJosh Tenenbaum\, MIT\nBurt Totaro\, UCLA\nAvi Wigderson\, IAS\nYing Nian Wu\, UCLA\nLaurent Younes\, Johns Hopkins\nSong-Chun Zhu\, UCLA\n\nOrganizers:\n\nChing-Li Chai\, University of Pennsylvania\nDavid Gu\, Stony Brook University\nAmnon Neeman\, Australian National University\nMark Nitzberg\, University of California at Berkeley\nYang Wang\, Hong Kong University of Science and Technology\nShing-Tung Yau\, Harvard University\nSong-Chun Zhu\, University of California\, Los Angeles\n\nPublication: \nPure and Applied Mathematics Quarterly\nSpecial Issue: In Honor of David Mumford\nGuest Editors: Ching-Li Chai\, Amnon Neeman \n 
URL:https://cmsa.fas.harvard.edu/event/from-algebraic-geometry-to-vision-and-ai-a-symposium-celebrating-the-mathematical-work-of-david-mumford/
LOCATION:Common Room\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Mumford-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180124T090000
DTEND;TZID=America/New_York:20180125T170000
DTSTAMP:20260504T060850
CREATED:20230717T173945Z
LAST-MODIFIED:20250305T214037Z
UID:10000042-1516784400-1516899600@cmsa.fas.harvard.edu
SUMMARY:Blockchain Conference
DESCRIPTION:On January 24-25\, 2019 the Center of Mathematical Sciences will be hosting a conference on distributed-ledger (blockchain) technology. The conference is intended to cover a broad range of topics\, from abstract mathematical aspects (cryptography\, game theory\, graph theory\, theoretical computer science) to concrete applications (in accounting\, government\, economics\, finance\, management\, medicine). The talks will take place in Science Center\, Hall D. \nhttps://youtu.be/FyKCCutxMYo \nPhotos\n \nSpeakers: \n\nJoseph Abadi\, Princeton University\nBenedikt Bunz\, Stanford University\nJake Cacciapaglia\, Nebula Genomics/Harvard Medical School\nEduardo Castello\, Massachusetts Institute of Technology\nAlisa DiCaprio\, R3\nZhiguo He\, University of Chicago\nSteven Kou\, Boston University\nAnne Lafarre\, Tilburg University\nJacob Leshno\, University of Chicago\nBruce Schneier\, Harvard Kennedy School\nDavid Schwartz\, Ripple\nElaine Shi\, Cornell University/Thunder Research\nHong Wan\, NCSU
URL:https://cmsa.fas.harvard.edu/event/blockchain-conference/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/Blockchain-Final-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170818T154700
DTEND;TZID=America/New_York:20170819T154700
DTSTAMP:20260504T060850
CREATED:20230717T172600Z
LAST-MODIFIED:20250328T144515Z
UID:10000034-1503071220-1503157620@cmsa.fas.harvard.edu
SUMMARY:2017 Big Data Conference
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a conference on Big Data from August 18 – 19\, 2017\, in Hall D of the Science Center at Harvard University.\nThe Big Data Conference features many speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. This is the third conference on Big Data the Center will host as part of our annual events\, and is co-organized by Richard Freeman\, Scott Kominers\, Jun Liu\, Horng-Tzer Yau and Shing-Tung Yau. \nConfirmed Speakers: \n\nMohammad Akbarpour\, Stanford University\nAlbert-László Barabási\, Northeastern University\nNoureddine El Karoui\, University of California\, Berkeley\nRavi Jagadeesan\, Harvard University\nLucas Janson\, Harvard University\nTracy Ke\, University of Chicago\nTze Leung Lai\, Stanford University\nAnnie Liang\, University of Pennsylvania\nMarena Lin\, Harvard University\nNikhil Naik\, Harvard University\nAlex Peysakhovich\, Facebook\nNatesh Pillai\, Harvard University\nJann Spiess\, Harvard University\nBradly Stadie\, Open AI\, University of California\, Berkeley\nZak Stone\, Google\nHau-Tieng Wu\, University of Toronto\nSifan Zhou\, Xiamen University\n\n  \nFollowing the conference\, there will be a two-day workshop from August 20-21. The workshop is organized by Scott Kominers\, and will feature: \n\nJörn Boehnke\, Harvard University\nNikhil Naik\, Harvard University\nBradly Stadie\, Open AI\, University of California\, Berkeley\n\n  \nConference Schedule \nA PDF version of the schedule below can also be downloaded here. \nAugust 18\, Friday (Full day)\n\n\n\nTime\nSpeaker\nTopic\n\n\n8:30 am – 9:00 am\n\nBreakfast\n\n\n9:00 am – 9:40 am\nMohammad Akbarpour \nVideo\nTitle: Information aggregation in overlapping generations and the emergence of experts \nAbstract: We study a model of social learning with “overlapping generations”\, where agents meet others and share data about an underlying state over time. We examine under what conditions the society will produce individuals with precise knowledge about the state of the world. There are two information sharing regimes in our model: Under the full information sharing technology\, individuals exchange the information about their point estimates of an underlying state\, as well as their sources (or the precision of their signals) and update their beliefs by taking a weighted average. Under the limited information sharing technology\, agents only observe the information about the point estimates of those they meet\, and update their beliefs by taking a weighted average\, where weights can depend on the sequence of meetings\, as well as the labels. Our main result shows that\, unlike most social learning settings\, using such linear learning rules do not guide the society (or even a fraction of its members) to learn the truth\, and having access to\, and exploiting knowledge of the precision of a source signal are essential for efficient social learning (joint with Amin Saberi & Ali Shameli).\n\n\n9:40 am – 10:20 am\nLucas Janson \nVideo\nTitle: Model-Free Knockoffs For High-Dimensional Controlled Variable Selection \nAbstract: Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a nonlinear fashion\, such as when the response is binary. Although this modeling problem has been extensively studied\, it remains unclear how to effectively control the fraction of false discoveries even in high-dimensional logistic regression\, not to mention general high-dimensional nonlinear models. To address such a practical problem\, we propose a new framework of model-free knockoffs\, which reads from a different perspective the knockoff procedure (Barber and Candès\, 2015) originally designed for controlling the false discovery rate in linear models. The key innovation of our method is to construct knockoff variables probabilistically instead of geometrically. This enables model-free knockoffs to deal with arbitrary (and unknown) conditional models and any dimensions\, including when the dimensionality p exceeds the sample size n\, while the original knockoffs procedure is constrained to homoscedastic linear models with n greater than or equal to p. Our approach requires the design matrix be random (independent and identically distributed rows) with a covariate distribution that is known\, although we show our procedure to be robust to unknown/estimated distributions. As we require no knowledge/assumptions about the conditional distribution of the response\, we effectively shift the burden of knowledge from the response to the covariates\, in contrast to the canonical model-based approach which assumes a parametric model for the response but very little about the covariates. To our knowledge\, no other procedure solves the controlled variable selection problem in such generality\, but in the restricted settings where competitors exist\, we demonstrate the superior power of knockoffs through simulations. Finally\, we apply our procedure to data from a case-control study of Crohn’s disease in the United Kingdom\, making twice as many discoveries as the original analysis of the same data. \nSlides\n\n\n10:20 am – 10:50 am\n\nBreak\n\n\n10:50 pm – 11:30 pm\nNoureddine El Karoui \nVideo\nTitle: Random matrices and high-dimensional statistics: beyond covariance matrices \nAbstract: Random matrices have played a central role in understanding very important statistical methods linked to covariance matrices (such as Principal Components Analysis\, Canonical Correlation Analysis etc…) for several decades. In this talk\, I’ll show that one can adopt a random-matrix-inspired point of view to understand the performance of other widely used tools in statistics\, such as M-estimators\, and very common methods such as the bootstrap. I will focus on the high-dimensional case\, which captures well the situation of “moderately” difficult statistical problems\, arguably one of the most relevant in practice. In this setting\, I will show that random matrix ideas help upend conventional theoretical thinking (for instance about maximum likelihood methods) and highlight very serious practical problems with resampling methods.\n\n\n11:30 am – 12:10 pm\nNikhil Naik \nVideo\nTitle: Understanding Urban Change with Computer Vision and Street-level Imagery \nAbstract: Which neighborhoods experience physical improvements? In this work\, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First\, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements. Second\, neighborhoods with better initial appearances experience\, on average\, larger positive improvements. Third\, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods. Together\, our results illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities. \n(Joint work with Edward L. Glaeser\, Cesar A. Hidalgo\, Scott Duke Kominers\, and Ramesh Raskar.)\n\n\n12:10 pm – 12:25 pm\nVideo #1 \nVideo #2\nData Science Lightning Talks\n\n\n12:25 pm – 1:30 pm\n\nLunch\n\n\n1:30 pm – 2:10 pm\nTracy Ke \nVideo\nTitle: A new SVD approach to optimal topic estimation \nAbstract: In the probabilistic topic models\, the quantity of interest—a low-rank matrix consisting of topic vectors—is hidden in the text corpus matrix\, masked by noise\, and Singular Value Decomposition (SVD) is a potentially useful tool for learning such a low-rank matrix. However\, the connection between this low-rank matrix and the singular vectors of the text corpus matrix are usually complicated and hard to spell out\, so how to use SVD for learning topic models faces challenges. \nWe overcome the challenge by revealing a surprising insight: there is a low-dimensional simplex structure which can be viewed as a bridge between the low-rank matrix of interest and the SVD of the text corpus matrix\, and which allows us to conveniently reconstruct the former using the latter. Such an insight motivates a new SVD-based approach to learning topic models. \nFor asymptotic analysis\, we show that under a popular topic model (Hofmann\, 1999)\, the convergence rate of the l1-error of our method matches that of the minimax lower bound\, up to a multi-logarithmic term. In showing these results\, we have derived new element-wise bounds on the singular vectors and several large deviation bounds for weakly dependent multinomial data. Our results on the convergence rate and asymptotical minimaxity are new. We have applied our method to two data sets\, Associated Process (AP) and Statistics Literature Abstract (SLA)\, with encouraging results. In particular\, there is a clear simplex structure associated with the SVD of the data matrices\, which largely validates our discovery.\n\n\n2:10 pm – 2:50 pm\nAlbert-László Barabási \nVideo\nTitle: Taming Complexity: From Network Science to Controlling Networks \nAbstract: The ultimate proof of our understanding of biological or technological systems is reflected in our ability to control them. While control theory offers mathematical tools to steer engineered and natural systems towards a desired state\, we lack a framework to control complex self-organized systems. Here we explore the controllability of an arbitrary complex network\, identifying the set of driver nodes whose time-dependent control can guide the system’s entire dynamics. We apply these tools to several real networks\, unveiling how the network topology determines its controllability. Virtually all technological and biological networks must be able to control their internal processes. Given that\, issues related to control deeply shape the topology and the vulnerability of real systems. Consequently unveiling the control principles of real networks\, the goal of our research\, forces us to address series of fundamental questions pertaining to our understanding of complex systems. \n \n\n\n2:50 pm – 3:20 pm\n\nBreak\n\n\n3:20 pm – 4:00 pm\nMarena Lin \nVideo\nTitle: Optimizing climate variables for human impact studies \nAbstract: Estimates of the relationship between climate variability and socio-economic outcomes are often limited by the spatial resolution of the data. As studies aim to generalize the connection between climate and socio-economic outcomes across countries\, the best available socio-economic data is at the national level (e.g. food production quantities\, the incidence of warfare\, averages of crime incidence\, gender birth ratios). While these statistics may be trusted from government censuses\, the appropriate metric for the corresponding climate or weather for a given year in a country is less obvious. For example\, how do we estimate the temperatures in a country relevant to national food production and therefore food security? We demonstrate that high-resolution spatiotemporal satellite data for vegetation can be used to estimate the weather variables that may be most relevant to food security and related socio-economic outcomes. In particular\, satellite proxies for vegetation over the African continent reflect the seasonal movement of the Intertropical Convergence Zone\, a band of intense convection and rainfall. We also show that agricultural sensitivity to climate variability differs significantly between countries. This work is an example of the ways in which in-situ and satellite-based observations are invaluable to both estimates of future climate variability and to continued monitoring of the earth-human system. We discuss the current state of these records and potential challenges to their continuity.\n\n\n4:00 pm – 4:40 pm\nAlex Peysakhovich\n Title: Building a cooperator \nAbstract: A major goal of modern AI is to construct agents that can perform complex tasks. Much of this work deals with single agent decision problems. However\, agents are rarely alone in the world. In this talk I will discuss how to combine ideas from deep reinforcement learning and game theory to construct artificial agents that can communicate\, collaborate and cooperate in productive positive sum interactions.\n\n\n4:40 pm – 5:20 pm\nTze Leung Lai \nVideo\nTitle: Gradient boosting: Its role in big data analytics\, underlying mathematical theory\, and recent refinements \nAbstract: We begin with a review of the history of gradient boosting\, dating back to the LMS algorithm of Widrow and Hoff in 1960 and culminating in Freund and Schapire’s AdaBoost and Friedman’s gradient boosting and stochastic gradient boosting algorithms in the period 1999-2002 that heralded the big data era. The role played by gradient boosting in big data analytics\, particularly with respect to deep learning\, is then discussed. We also present some recent work on the mathematical theory of gradient boosting\, which has led to some refinements that greatly improves the convergence properties and prediction performance of the methodology.\n\n\n\nAugust 19\, Saturday (Full day)\n\n\n\nTime\nSpeaker\nTopic\n\n\n8:30 am – 9:00 am\n\nBreakfast\n\n\n9:00 am – 9:40 am\nNatesh Pillai \nVideo\nTitle: Accelerating MCMC algorithms for Computationally Intensive Models via Local Approximations \nAbstract: We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood\, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis–Hastings kernel\, borrowing ideas from deterministic approximation theory\, optimization\, and experimental design. Previous efforts at integrating approximate models into inference typically sacrifice either the sampler’s exactness or efficiency; our work seeks to address these limitations by exploiting useful convergence characteristics of local approximations. We prove the ergodicity of our approximate Markov chain\, showing that it samples asymptotically from the exact posterior distribution of interest. We describe variations of the algorithm that employ either local polynomial approximations or local Gaussian process regressors. Our theoretical results reinforce the key observation underlying this article: when the likelihood has some local regularity\, the number of model evaluations per Markov chain Monte Carlo (MCMC) step can be greatly reduced without biasing the Monte Carlo average. Numerical experiments demonstrate multiple order-of-magnitude reductions in the number of forward model evaluations used in representative ordinary differential equation (ODE) and partial differential equation (PDE) inference problems\, with both synthetic and real data.\n\n\n9:40 am – 10:20 am\nRavi Jagadeesan \nVideo\nTitle: Designs for estimating the treatment effect in networks with interference \nAbstract: In this paper we introduce new\, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies\, we introduce the notion of considering a treatment assignment as a quasi-coloring” on a graph. Our idea of a perfect quasi-coloring strives to match every treated unit on a given network with a distinct control unit that has identical number of treated and control neighbors. For a wide range of interference functions encountered in applications\, we show both by theory and simulations that the classical Neymanian estimator for the direct effect has desirable properties for our designs. This further extends to settings where homophily is present in addition to interference.\n\n\n10:20 am – 10:50 am\n\nBreak\n\n\n10:50 am – 11:30 am\nAnnie Liang \nVideo\nTitle: The Theory is Predictive\, but is it Complete? An Application to Human Generation of Randomness \nAbstract: When we test a theory using data\, it is common to focus on correctness: do the predictions of the theory match what we see in the data? But we also care about completeness: how much of the predictable variation in the data is captured by the theory? This question is difficult to answer\, because in general we do not know how much “predictable variation” there is in the problem. In this paper\, we consider approaches motivated by machine learning algorithms as a means of constructing a benchmark for the best attainable level of prediction.  We illustrate our methods on the task of predicting human-generated random sequences. Relative to a theoretical machine learning algorithm benchmark\, we find that existing behavioral models explain roughly 15 percent of the predictable variation in this problem. This fraction is robust across several variations on the problem. We also consider a version of this approach for analyzing field data from domains in which human perception and generation of randomness has been used as a conceptual framework; these include sequential decision-making and repeated zero-sum games. In these domains\, our framework for testing the completeness of theories provides a way of assessing their effectiveness over different contexts; we find that despite some differences\, the existing theories are fairly stable across our field domains in their performance relative to the benchmark. Overall\, our results indicate that (i) there is a significant amount of structure in this problem that existing models have yet to capture and (ii) there are rich domains in which machine learning may provide a viable approach to testing completeness (joint with Jon Kleinberg and Sendhil Mullainathan).\n\n\n11:30 am – 12:10 pm\nZak Stone \nVideo\nTitle: TensorFlow: Machine Learning for Everyone \nAbstract: We’ve witnessed extraordinary breakthroughs in machine learning over the past several years. What kinds of things are possible now that weren’t possible before? How are open-source platforms like TensorFlow and hardware platforms like GPUs and Cloud TPUs accelerating machine learning progress? If these tools are new to you\, how should you get started? In this session\, you’ll hear about all of this and more from Zak Stone\, the Product Manager for TensorFlow on the Google Brain team.\n\n\n12:10 pm – 1:30 pm\n\nLunch\n\n\n1:30 pm – 2:10 pm\nJann Spiess \nVideo\nTitle: (Machine) Learning to Control in Experiments \nAbstract: Machine learning focuses on high-quality prediction rather than on (unbiased) parameter estimation\, limiting its direct use in typical program evaluation applications. Still\, many estimation tasks have implicit prediction components. In this talk\, I discuss accounting for controls in treatment effect estimation as a prediction problem. In a canonical linear regression framework with high-dimensional controls\, I argue that OLS is dominated by a natural shrinkage estimator even for unbiased estimation when treatment is random; suggest a generalization that relaxes some parametric assumptions; and contrast my results with that for another implicit prediction problem\, namely the first stage of an instrumental variables regression.\n\n\n2:10 pm – 2:50 pm\nBradly Stadie\nTitle: Learning to Learn Quickly: One-Shot Imitation and Meta Learning \nAbstract: Many reinforcement learning algorithms are bottlenecked by data collection costs and the brittleness of their solutions when faced with novel scenarios.\nWe will discuss two techniques for overcoming these shortcomings. In one-shot imitation\, we train a module that encodes a single demonstration of a desired behavior into a vector containing the essence of the demo. This vector can subsequently be utilized to recover the demonstrated behavior. In meta-learning\, we optimize a policy under the objective of learning to learn new tasks quickly. We show meta-learning methods can be accelerated with the use of auxiliary objectives. Results are presented on grid worlds\, robotics tasks\, and video game playing tasks.\n\n\n2:50 pm – 3:20 pm\n\nBreak\n\n\n3:20 pm – 4:00 pm\nHau-Tieng Wu \nVideo\nTitle: When Medical Challenges Meet Modern Data Science \nAbstract: Adaptive acquisition of correct features from massive datasets is at the core of modern data analysis. One particular interest in medicine is the extraction of hidden dynamics from a single observed time series composed of multiple oscillatory signals\, which could be viewed as a single-channel blind source separation problem. The mathematical and statistical problems are made challenging by the structure of the signal which consists of non-sinusoidal oscillations with time varying amplitude/frequency\, and by the heteroscedastic nature of the noise. In this talk\, I will discuss recent progress in solving this kind of problem by combining the cepstrum-based nonlinear time-frequency analysis and manifold learning technique. A particular solution will be given along with its theoretical properties. I will also discuss the application of this method to two medical problems – (1) the extraction of a fetal ECG signal from a single lead maternal abdominal ECG signal; (2) the simultaneous extraction of the instantaneous heart/respiratory rate from a PPG signal during exercise; (3) (optional depending on time) an application to atrial fibrillation signals. If time permits\, the clinical trial results will be discussed.\n\n\n4:00 pm – 4:40 pm\nSifan Zhou \nVideo\nTitle: Citing People Like Me: Homophily\, Knowledge Spillovers\, and Continuing a Career in Science \nAbstract: Forward citation is widely used to measure the scientific merits of articles. This research studies millions of journal article citation records in life sciences from MEDLINE and finds that authors of the same gender\, the same ethnicity\, sharing common collaborators\, working in the same institution\, or being geographically close are more likely (and quickly) to cite each other than predicted by their proportion among authors working on the same research topics. This phenomenon reveals how social and geographic distances influence the quantity and speed of knowledge spillovers. Given the importance of forward citations in academic evaluation system\, citation homophily potentially put authors from minority group at a disadvantage. I then show how it influences scientists’ chances to survive in the academia and continue publishing. Based on joint work with Richard Freeman.\n\n\n\n  \nTo view photos and video interviews from the conference\, please visit the CMSA blog. \n\n \n\n  \n\n\n\nBig Data\,CMSA\,Harvard\,Math\nEvents\,Past Events
URL:https://cmsa.fas.harvard.edu/event/2017-big-data-conference-aug-18-19/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Big Data Conference,Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Big-Data-2017_2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170605T090000
DTEND;TZID=America/New_York:20170606T170000
DTSTAMP:20260504T060850
CREATED:20230717T175551Z
LAST-MODIFIED:20250305T182141Z
UID:10000032-1496653200-1496768400@cmsa.fas.harvard.edu
SUMMARY:A Celebration of Symplectic Geometry: 15 Years of JSG
DESCRIPTION:In celebration of the Journal of Symplectic Geometry’s 15th anniversary\, the Center of Mathematical Sciences and Applications will be hosting A Celebration of Symplectic Geometry: 15 Years of JSG on June 5-6\, 2017. \nConfirmed speakers: \n\nRoger Casals\, MIT\nChen He\, Northeastern University\nYael Karshon\, University of Toronto\nAilsa Keating\, Institute of Advanced Study\nEckhard Meinrenken\, University of Toronto\nAna Rita Pires\, Fordham University\nSobhan Seyfaddini\, Institute of Advanced Study\nAlejandro Uribe\, University of Michigan\nJonathan Weitsman\, Northeastern University\n\nThe conference is co-organized by Denis Auroux and Victor Guillemin. Additional information on the conference will be announced closer to the event. \nSchedule:\nJune 5\, Monday (Full day)\n\n\n\nTime\nSpeaker\nTopic\n\n\n8:30am – 9:0am\n\nBreakfast\n\n\n9:00am – 10:00am\nJonathan Weitsman\nTitle: On the geometric quantization of (some) Poisson manifolds\n\n\n10:30am – 11:30am\nEckhard Meinrenken\nTitle: On Hamiltonian loop group spaces \nAbstract: Let G be a compact Lie group. We explain a construction of an LG-equivariant spinor module over any Hamiltonian loop group space with proper moment map. It may be regarded as its `canonical spin-c structure’. We show how to reduce to finite dimensions\, resulting in actual spin-s structure on transversals\, as well as twisted spin-c structures for the associated quasi-hamiltonian space. This is based on joint work with Yiannis Loizides and Yanli Song.\n\n\n\n11:30am – 1:30pm\n\nBreak\n\n\n1:30pm – 2:30pm\nAna Rita Pires\nTitle: Infinite staircases in symplectic embedding problems \nAbstract: McDuff and Schlenk studied an embedding capacity function\, which describes when a 4-dimensional ellipsoid can symplectically embed into a 4-ball. The graph of this function includes an infinite staircase related to the odd index Fibonacci numbers. Infinite staircases have been shown to exist also in the graphs of the embedding capacity functions when the target manifold is a polydisk or the ellipsoid E(2\,3). I will describe how we use ECH capacities\, lattice point counts and Ehrhart theory to show that infinite staircases exist for these and a few other target manifolds\, as well as to conjecture that these are the only such target manifolds. This is a joint work with Cristofaro-Gardiner\, Holm and Mandini. \nVideo\n\n\n3:00pm – 4:00pm\nSobhan Seyfaddini\nTitle: Rigidity of conjugacy classes in groups of area-preserving homeomorphisms \nAbstract: Motivated by understanding the algebraic structure of groups of area-preserving homeomorphims F. Beguin\, S. Crvoisier\, and F. Le Roux were lead to the following question: Can the conjugacy class of a Hamiltonian homeomorphism be dense? We will show that one can rule out existence of dense conjugacy classes by simply counting fixed points. This is joint work with Le Roux and Viterbo.\n\n\n4:30pm – 5:30pm\nRoger Casals\nTitle: Differential Algebra of Cubic Graphs\nAbstract: In this talk we will associate a combinatorial dg-algebra to a cubic planar graph. This algebra is defined by counting binary sequences\, which we introduce\, and we shall provide explicit computations and examples. From there we study the Legendrian surfaces behind these constructions\, including Legendrian surgeries\, the count of Morse flow trees involved in contact homology\, and the relation to microlocal sheaves. Time permitting\, I will explain a connection to spectral networks.Video\n\n\n\nJune 6\, Tuesday (Full day) \n\n\n\nTime\nSpeaker\nTopic\n\n\n8:30am – 9:00am\n\nBreakfast\n\n\n9:00am – 10:00am\nAlejandro Uribe\nTitle: Semi-classical wave functions associated with isotropic submanifolds of phase space \nAbstract: After reviewing fundamental ideas on the quantum-classical correspondence\, I will describe how to associate spaces of semi-classical wave functions to isotropic submanifolds of phase space satisfying a Bohr-Sommerfeld condition. Such functions have symbols that are symplectic spinors\, and they satisfy a symbol calculus under the action of quantum observables. This is the semi-classical version of the Hermite distributions of Boutet the Monvel and Guillemin\, and it is joint work with Victor Guillemin and Zuoqin Wang. I will inlcude applications and open questions. \nVideo\n\n\n10:30am – 11:30am\nAlisa Keating\nTitle: Symplectomorphisms of exotic discs \nAbstract: It is a theorem of Gromov that the group of compactly supported symplectomorphisms of R^4\, equipped with the standard symplectic form\, is contractible. While nothing is known in higher dimensions for the standard symplectic form\, we show that for some exotic symplectic forms on R^{4n}\, for all but finitely n\, there exist compactly supported symplectomorphisms that are smoothly non-trivial. The principal ingredients are constructions of Milnor and Munkres\, a symplectic and contact version of the Gromoll filtration\, and Borman\, Eliashberg and Murphy’s work on existence of over-twisted contact structures. Joint work with Roger Casals and Ivan Smith. \nVideo\n\n\n11:30am – 1:30pm\n\nBreak\n\n\n1:30pm – 2:30pm\nChen He\nTitle: Morse theory on b-symplectic manifolds \nAbstract: b-symplectic (or log-symplectic) manifolds are Poisson manifolds equipped with symplectic forms of logarithmic singularity. Following Guillemin\, Miranda\, Pires and Scott’s introduction of Hamiltonian group actions on b-symplectic manifolds\, we will survey those classical results of Hamiltonian geometry to the b-symplectic case. \nVideo\n\n\n3:00pm – 4:00pm\nYael Karshon\nTitle: Geometric quantization with metaplectic-c structures \nAbstract: I will present a variant of the Kostant-Souriau geometric quantization procedure that uses metaplectic-c structures to incorporate the “half form correction” into the prequantization stage. This goes back to the late 1970s but it is not widely known and it has the potential to generalize and improve upon recent works on geometric quantization. \nVideo\n\n\n\n 
URL:https://cmsa.fas.harvard.edu/event/a-celebration-of-symplectic-geometry-15-years-of-jsg-june-5-6-2017/
LOCATION:20 Garden Street\, Cambridge\, MA 02138\, MA\, MA\, 02138\, United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Shlomo_orange.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170501T090000
DTEND;TZID=America/New_York:20170502T170000
DTSTAMP:20260504T060850
CREATED:20230717T175324Z
LAST-MODIFIED:20240209T152357Z
UID:10000031-1493629200-1493744400@cmsa.fas.harvard.edu
SUMMARY:Working Conference on Covariance Analysis in Biology\, May 1-4\, 2017
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a working Conference on Covariance Analysis in Biology\, May 1-4\, 2017.  The conference will be hosted in Room G10 of the CMSA Building located at 20 Garden Street\, Cambridge\, MA 02138. \nThis event is open and free.  If you would like to attend\, please register here to help us keep a headcount. A list of lodging options convenient to the Center can also be found on our recommended lodgings page. \nSpeakers: \nOrr Ashenberg\, Fred Hutchinson Cancer Research Center \nJohn Barton\, Massachusetts Institute of Technology \nSimona Cocco\, Laboratoire de Physique Statistique de l’ENS \nSean Eddy\, Harvard University \nEfthimios Kaxiras\, Harvard University \n\n\n\nMichael Laub\, Massachusetts Institute of Technology \nDebora S. Marks\, Harvard University \n\n\n\nGovind Menon\, Brown University \nRémi Monasson\, Laboratoire de Physique Théorique de l’ENS \nAndrew Murray\, Harvard University \nIlya Nemenman\, Emory College \n\n\n\nChris Sander\, Dana-Farber Cancer Institute\, Harvard Medical School \n\n\n\nDave Thirumalai\, University of Texas at Austin \nMartin Weigt\, IBPS\, Université Pierre et Marie Curie \nMatthieu Wyart\, EPFL \nMore speakers will be confirmed soon. \n  \n\n\n\nSchedule:\n(Please click here for a downloadable version of the schedule.)\nPlease note that the schedule for both days is currently tentative and is subject to change.\nMay 1\, Monday \n\n\n\n\n\nTime\nSpeaker\nTopic\n\n\n9:00-10:00am\nSean Eddy\nTBA\n\n\n10:00-11:00am\nMike Laub\nTBA\n\n\n11:00am-12:00pm\nIlya Nemenman\nTBA\n\n\n\n\nMay 2\, Tuesday\n\n\n\n\n\nTime\nSpeaker\nTopic\n\n\n9:00-10:00am\nOrr Ashenberg\nTBA\n\n\n10:00-11:00am\nDebora Marks\nTBA\n\n\n11:00am-12:00pm\nMartin Weigt\nTBA\n\n\n4:30pm-5:30pm\nSimona Cocco\nCMSA Colloquia\n\n\n\n  \n\nMay 3\, Wednesday\n\n\n\n\n\nTime\nSpeaker\nTopic\n\n\n9:00-10:00am\nAndrew Murray\nTBA\n\n\n10:00-11:00am\nMatthieu Wyart\nTBA\n\n\n11:00am-12:00pm\nRémi Monasson\nTBA\n\n\n\n  \n\nMay 4\, Thursday\n\n\n\n\nTime\nSpeaker\nTopic\n\n\n9:00-10:00am\nDavid Thirumalai\nTBA\n\n\n10:00-11:00am\nChris Sander\nTBA\n\n\n11:00am-12:00pm\nJohn Barton\nTBA\n\n\n\n  \n\n\nOrganizers: \n\n\n\nMichael Brenner\, Lucy Colwell\, Elena Rivas\, Eugene Shakhnovich \n\n\n\n* This event is sponsored by CMSA Harvard University. \n\n\n\n\nPast Events
URL:https://cmsa.fas.harvard.edu/event/working-conference-on-covariance-analysis-in-biology-may-1-4-2017/
LOCATION:20 Garden Street\, Cambridge\, MA 02138\, MA\, MA\, 02138\, United States
CATEGORIES:Conference,Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170428T090000
DTEND;TZID=America/New_York:20170502T170000
DTSTAMP:20260504T060850
CREATED:20230717T175015Z
LAST-MODIFIED:20250305T215930Z
UID:10000030-1493370000-1493744400@cmsa.fas.harvard.edu
SUMMARY:JDG 2017 Conference
DESCRIPTION:In celebration of the Journal of Differential Geometry’s 50th anniversary\, the Harvard Math Department will be hosting the Tenth Conference on Geometry and Topology (JDG 2017) from April 28 – May 2\, 2017. \nConfirmed Speakers \n\nMina Aganagic\, UC Berkeley\nDenis Auroux\, UC Berkeley\nCaucher Birkar\, University of Cambridge\nHuai-Dong Cao\, Lehigh University\nTristan Collins\, Harvard University\nCamillo De Lellis\, ETH Zurich\nJean-Pierre Demailly\, Grenoble Alpes University\nSimon Donaldson\, Stony Brook University\nDan Freed\, University of Texas at Austin\nKenji Fukaya\, Stony Brook University\nDavid Gabai\, Princeton University\nLarry Guth\, Massachusetts Institute of Technology\nRichard Hamilton\, Columbia University\nYujiro Kawamata\, University of Tokyo\nFrances Kirwan\, Oxford University\nBlaine Lawson\, Stony Brook University\nJun Li\, Stanford University\nSi Li\, Tsinghua University\nBong Lian\, Brandeis University\nChiu-Chu Melissa Liu\, Columbia University\nCiprian Manolescu\, University of California\, Los Angeles\nFernando Marques\, Princeton University\nWilliam Meeks\, University of Massachusetts Amherst\nWilliam Minicozzi\, Massachusetts Institute of Technology\nJohn Pardon\, Princeton University\nDuong Phong\, Columbia University\nAlena Pirutka\, Courant Institute of New York University\nRichard Schoen\, University of California\, Irvine\nArtan Sheshmani\, QGM Aarhus University/Harvard University\nCliff Taubes\, Harvard University\nCumrun Vafa\, Harvard University\nMu-Tao Wang\, Columbia University\nShing-Tung Yau\, Harvard University\nSteve Zelditch\, Northwestern University\n\n* This event is co-sponsored by Lehigh University and partially supported by the National Science Foundation.
URL:https://cmsa.fas.harvard.edu/event/jdg-2017-conference-april-28-may-2-2017/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/JDG-2017-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170327T153400
DTEND;TZID=America/New_York:20170330T153400
DTSTAMP:20260504T060850
CREATED:20240209T021031Z
LAST-MODIFIED:20240307T105730Z
UID:10001796-1490628840-1490888040@cmsa.fas.harvard.edu
SUMMARY:Working Conference on Materials and Data Analysis\, March 27-30\, 2017
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a 5-day working Conference on Materials and Data Analysis and related areas\, March 27-30\, 2017.  The conference will be hosted in Room G10 of the CMSA Building located at 20 Garden Street\, Cambridge\, MA 02138. \nPhotos of the event can be found on CMSA’s Blog. \n Participants:\n\nRyan P. Adams\, Harvard University\nJörg Behler\, University of Göttingen\nKieron Burke\, University of California\, Irvine\nLucy Colwell\, University of Cambridge\nGábor Csányi\, University of Cambridge\nEkin Doğuş Çubuk\, Stanford University\nLeslie Greengard\, Courant Institute of Mathematical Sciences\, New York University\nPetros Koumoutsakos\, Radcliffe Institute for Advanced Study\, Harvard University\nGovind Menon\, Brown University\nEvan Reed\, Stanford University\nPatrick Riley\, Google\nMatthias Rupp\, Fitz Haber Institute of the Max Planck Society\nSadasivan Shankar\, Harvard University\nDennis Sheberla\, Harvard University\n\n\n\nOrganizers: \n\n\n\nMichael Brenner\, Efthimios Kaxiras \n\n\n\n* This event is sponsored by CMSA Harvard University. \n\nSchedule:\n\nMonday\, March 27 \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nKieron Burke\, University of California\, Irvine\nBackground in DFT and electronic structure calculations\n\n\n10:00am – 11:00am\nKieron Burke\, University of California\, Irvine\n\nThe density functionals machines can learn \n\n\n\n11:00am – 12:00pm\nSadasivan Shankar\, Harvard University\nA few key principles for applying Machine Learning to Materials (or Complex Systems) — Scientific and Engineering Perspectives\n\n\n\nTuesday\, March 28 \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nRyan Adams\, Harvard\nTBA\n\n\n10:00am – 11:00am\nGábor Csányi\, University of Cambridge\n\nInteratomic potentials using machine learning: accuracy\, transferability and chemical diversity \n\n\n\n11:00am – 1:00pm\nLunch Break\n\n\n1:00pm – 2:00pm\nEvan Reed\, Stanford University\nTBA\n\n\n\n Wednesday\, March 29  \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nPatrick Riley\, Google\nThe Message Passing Neural Network framework and its application to molecular property prediction\n\n\n10:00am – 11:00am\nJörg Behler\, University of Göttingen\nTBA\n\n\n11:00am – 12:00pm\nEkin Doğuş Çubuk\, Stanford Univers\nTBA\n\n\n4:00pm\nLeslie Greengard\, Courant Institute\nInverse problems in acoustic scattering and cryo-electron microscopy \nCMSA Colloquium\n\n\n\nThursday\, March 30 \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nMatthias Rupp\, Fitz Haber Institute of the Max Planck Society\nTBA\n\n\n10:00am – 11:00am\nPetros Koumoutsakos\, Radcliffe Institute for Advanced Study\, Harvard\nTBA\n\n\n11:00am – 1:00pm\nLunch Break\n\n\n1:00pm – 2:00pm\nDennis Sheberla\, Harvard University\nRapid discovery of functional molecules by a high-throughput virtual screening\n\n\n\n\n\n\n\nEvents\, Past Events
URL:https://cmsa.fas.harvard.edu/event/working-conference-on-materials-and-data-analysis-march-27-30-2017/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170109T090000
DTEND;TZID=America/New_York:20170113T170000
DTSTAMP:20260504T060850
CREATED:20250305T194842Z
LAST-MODIFIED:20250305T194842Z
UID:10003717-1483952400-1484326800@cmsa.fas.harvard.edu
SUMMARY:Working Conference on Applications of Random Matrix Theory to Data Analysis\, January 9-13\, 2017
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a working Conference on Applications of Random Matrix Theory to Data Analysis\, January 9-13\, 2017.  The conference will be hosted in Room G10 of the CMSA Building located at 20 Garden Street\, Cambridge\, MA 02138. \nParticipants:\nGerard Ben Arous\, Courant Institute of Mathematical Sciences \nAlex Bloemendal\, Broad Institute \nArup Chakraburty\, MIT \n\n\n\nZhou Fan\, Stanford University \nAlpha Lee\, Harvard University \nMatthew R. McKay\, Hong Kong University of Science and Technology (HKUST) \nDavid R. Nelson\, Harvard University \nNick Patterson\, Broad Institute \nMarc Potters\, Capital Fund management \n\n\n\nYasser Roudi\, IAS \nTom Trogdon\, UC Irvine \nOrganizers: \n\n\n\nMichael Brenner\, Lucy Colwell\, Govind Menon\, Horng-Tzer Yau \nPlease click Program for a downloadable schedule with talk abstracts.\n\nSchedule: \n\n\n\nJanuary 9 – Day 1\n\n\n9:30am – 10:00am\nBreakfast & Opening remarks\n\n\n10:00am – 11:00am\nMarc Potters\, “Eigenvector overlaps and the estimation of large noisy matrices”\n\n\n11:00am – 12:00pm\nYasser Roudi\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm\nAfternoon Discussion\n\n\nJanuary 10 – Day 2\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nArup Chakraburty\, “The mathematical analyses and biophysical reasons underlying why the prevalence of HIV strains and their relative fitness are simply correlated\, and pose the challenge of building a general theory that encompasses other viruses where this is not true.”\n\n\n10:00am – 11:00am\nTom Trogdon\, “On the average behavior of numerical algorithms”\n\n\n11:00am – 12:00pm\nDavid R. Nelson\, “Non-Hermitian Localization in Neural Networks”\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm\nAfternoon Discussion\n\n\nJanuary 11 – Day 3\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nNick Patterson\n\n\n10:00am – 11:00am\nLucy Colwell\n\n\n11:00am – 12:00pm\nAlpha Lee\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm-4:00pm\nAfternoon Discussion\n\n\n4:00pm\nGerard Ben Arous (Public Talk)\, “Complexity of random functions of many variables: from geometry to statistical physics and deep learning algorithms“\n\n\nJanuary 12 – Day 4\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nGovind Menon\n\n\n10:00am – 11:00am\nAlex Bloemendal\n\n\n11:00am – 12:00pm\nZhou Fan\, “Free probability\, random matrices\, and statistics”\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm\nAfternoon Discussion\n\n\nJanuary 13 – Day 5\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 12:00pm\nFree for Working\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm\nFree for Working\n\n\n\n\n* This event is sponsored by CMSA Harvard University.
URL:https://cmsa.fas.harvard.edu/event/working-conference-on-applications-of-random-matrix-theory-to-data-analysis-january-9-13-2017/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Conference,Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20161203T090000
DTEND;TZID=America/New_York:20161204T170000
DTSTAMP:20260504T060850
CREATED:20230717T172404Z
LAST-MODIFIED:20250305T201523Z
UID:10000018-1480755600-1480870800@cmsa.fas.harvard.edu
SUMMARY:Mini-school on Nonlinear Equations\, December 3-4\, 2016
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a Mini-school on Nonlinear Equations on December 3-4\, 2016. The conference will have speakers and will be hosted at Harvard CMSA Building: Room G10 20 Garden Street\, Cambridge\, MA 02138. \nSpeakers:\n\nCliff Taubes (Harvard University)\nValentino Tosatti (Northwestern University)\nPengfei Guan (McGill University)\nJared Speck (MIT)\n\nSchedule:\n\n\n\nDecember 3rd – Day 1\n\n\n9:00am – 10:30am\nCliff Taubes\, “Compactness theorems in gauge theories”\n\n\n10:45am – 12:15pm\nValentino Tosatti\, “Complex Monge-Ampère Equations”\n\n\n\n\n\n12:15pm – 1:45pm\nLUNCH\n\n\n\n\n\n\n1:45pm – 3:15pm\nPengfei Guan\, “Monge-Ampère type equations and related geometric problems”\n\n\n3:30pm – 5:00pm\nJared Speck\, “Finite-time degeneration of hyperbolicity without blowup for solutions to quasilinear wave equations”\n\n\n\n\n\n\n\n\nDecember 4th – Day 2\n\n\n9:00am – 10:30am\nCliff Taubes\, “Compactness theorems in gauge theories”\n\n\n10:45am – 12:15pm\nValentino Tosatti\, “Complex Monge-Ampère Equations”\n\n\n\n\n\n12:15pm – 1:45pm\nLUNCH\n\n\n\n\n\n\n1:45pm – 3:15pm\nPengfei Guan\, “Monge-Ampère type equations and related geometric problems”\n\n\n3:30pm – 5:00pm\nJared Speck\, “Finite-time degeneration of hyperbolicity without blowup for solutions to quasilinear wave equations”\n\n\n\n\n  \n* This event is sponsored by National Science Foundation (NSF) and CMSA Harvard University.
URL:https://cmsa.fas.harvard.edu/event/mini-school-on-nonlinear-equations-december-3-4-2016/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event,Workshop
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/minischool.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20160822T090000
DTEND;TZID=America/New_York:20160823T163000
DTSTAMP:20260504T060850
CREATED:20230717T171959Z
LAST-MODIFIED:20250328T144123Z
UID:10000017-1471856400-1471969800@cmsa.fas.harvard.edu
SUMMARY:2016 Big Data Conference & Workshop
DESCRIPTION:! LOCATION CHANGE: The conference will be in Science Center Hall C on Tuesday\, Aug.23\, 2016.\nThe Center of Mathematical Sciences and Applications will be hosting a workshop on Big Data from August 12 – 21\, 2016 followed by a two-day conference on Big Data from August 22 – 23\, 2016. \nBig Data Conference features many speakers from the Harvard Community as well as many scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. This is the second conference on Big Data the Center will host as part of our annual events. The 2015 conference was a huge success. \nThe conference will be hosted at Harvard Science Center Hall A (Monday\, Aug.22) & Hall C (Tuesday\, Aug.23): 1 Oxford Street\, Cambridge\, MA 02138. \nThe 2016 Big Data conference is sponsored by the Center of Mathematical Sciences and Applications at Harvard University and the Alfred P. Sloan Foundation. \nConference Speakers:\n\nJörn Boehnke\, Harvard CMSA\nJoan Bruna\, UC Berkeley [Video]\nTamara Broderick\, MIT [Video]\nJustin Chen\, MIT [Video]\nYiling Chen\, Harvard University [Video]\nAmir Farbin\, UT Arlington [Video]\nDoug Finkbeiner\, Harvard University [Video]\nAndrew Gelman\, Columbia University [Video]\nNina Holden\, MIT [Video]\nElchanan Mossel\, MIT\nAlex Peysakhovich\, Facebook\nAlexander Rakhlin\, University of Pennsylvania [Video]\nNeal Wadhwa\, MIT [Video]\nJun Yin\, University of Wisconsin\nHarry Zhou\, Yale University [Video]\n\nPlease click Conference Program for a downloadable schedule with talk abstracts.\nConference Schedule:\n\n\n\nAugust 22 – Day 1\n\n\n8:30am\nBreakfast\n\n\n8:55am\nOpening remarks\n\n\n9:00am – 9:50am\nYiling Chen\, “Machine Learning with Strategic Data Sources” [Video]\n\n\n9:50am – 10:40am\nAndrew Gelman\, “Taking Bayesian Inference Seriously” [Video]\n\n\n10:40am – 11:10am\nBreak\n\n\n11:10am – 12:00pm\nHarrison Zhou\, “A General Framework for Bayes Structured Linear Models” [Video]\n\n\n12:00pm – 1:30pm\nLunch\n\n\n1:30pm – 2:20pm\nDouglas Finkbeiner\, “Mapping the Milky Way in 3D with star colors” [Video]\n\n\n2:20pm – 3:10pm\nNina Holden\, “Sparse exchangeable graphs and their limits” [Video]\n\n\n3:10pm – 3:40pm\nBreak\n\n\n3:40pm – 4:30pm\nAlex Peysakhovich\, “How social science methods inform personalization on Facebook News Feed” [Video]\n\n\n4:30pm – 5:20pm\nAmir Farbin\, “Deep Learning in High Energy Physics” [Video]\n\n\n\n\n\nAugust 23 – Day 2\n\n\n8:45am\nBreakfast\n\n\n9:00am – 9:50am\nJoan Bruna Estrach\, “Addressing Computational and Statistical Gaps with Deep Networks” [Video]\n\n\n9:50am – 10:40am\nJustin Chen & Neal Wadhwa\, “Smaller Than the Eye Can See: Big Engineering from Tiny Motions in Video” [Video]\n\n\n10:40am – 11:10am\nBreak\n\n\n11:10am – 12:00pm\nAlexander Rakhlin\, “How to Predict When Estimation is Hard: Algorithms for Learning on Graphs” [Video]\n\n\n12:00pm – 1:30pm\nLunch\n\n\n1:30pm – 2:20pm\nTamara Broderick\, “Fast Quantification of Uncertainty and Robustness with Variational Bayes” [Video]\n\n\n2:20pm – 3:10pm\nElchanan Mossel\, “Phylogenetic Reconstruction – a Rigorous Model of Deep Learning”\n\n\n3:10pm – 3:40pm\nBreak\n\n\n3:40pm – 4:30pm\nJörn Boehnke\, “Amazon’s Price and Sales-rank Data: What can one billion prices on 150 thousand products tell us about the economy?”\n\n\n\nWorkshop Participants:\nRichard Freeman’s Group: \n\nSen Chai\, ESSEC\nBrock Mendel\, Harvard University\nRaviv Muriciano-Goroff\, Stanford University\nSifan Zhou\, CMSA\n\nScott Kominer’s Group: \n\nBradly Stadie\, UC Berkeley\nNeal Wadhwa\, MIT [Video]\nJustin Chen\n\nChristopher Rogan’s Group: \n\nAmir Farbin\, UT Arlington [Video]\nPaul Jackson\, University of Adelaide\n\nFor more information about the workshops\, please reach out directly to the individual group leaders. \n* This event is sponsored by CMSA Harvard University and the Alfred P. Sloan Foundation. \n 
URL:https://cmsa.fas.harvard.edu/event/2016-big-data-conference-workshop/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Big Data Conference,Conference,Event,Workshop
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Big-Data_2016_2-1-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20160408T083000
DTEND;TZID=America/New_York:20160410T180000
DTSTAMP:20260504T060850
CREATED:20230717T180554Z
LAST-MODIFIED:20240209T151732Z
UID:10000016-1460104200-1460311200@cmsa.fas.harvard.edu
SUMMARY:Concluding Conference of the Special Program on Nonlinear Equations\, April 8 – 10\, 2016
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a concluding conference on April 8-10\, 2016 to accompany the year-long program on nonlinear equations. The conference will have 15 speakers and will be hosted at Harvard CMSA Building: Room G10 20 Garden Street\, Cambridge\, MA 02138 \nSpeakers:\n\nLydia Bieri (University of Michigan)\nLuis Caffarelli (University of Texas at Austin)\nMihalis Dafermos (Princeton University)\nCamillo De Lellis (Universität Zürich)\nPengfei Guan (McGill University)\nSlawomir Kolodziej (Jagiellonian University)\nMelissa Liu (Columbia University)\nDuong H. Phong (Columbia University)\nRichard Schoen (UC Irvine)\nCliff Taubes (Harvard University)\nBlake Temple (UC Davis)\nValentino Tosatti (Northwestern University)\nTai-Peng Tsai (University of British Columbia)\nMu-Tao Wang (Columbia University)\nXu-jia Wang (Australian National University)\n\nPlease click NLE Conference Schedule with Abstracts for a downloadable schedule with talk abstracts.\nPlease note that lunch will not be provided during the conference\, but a map of Harvard Square with a list of local restaurants can be found by clicking Map & Resturants.\nSchedule:\n\n\n\nApril 8 – Day 1\n\n\n8:30am\nBreakfast\n\n\n8:45am\nOpening remarks\n\n\n9:00am – 10:00am\nCamillo De Lellis\, “A Nash Kuiper theorem for $C^{1\,1:5}$ isometric immersions of disks“\n\n\n10:00am – 10:15am\nBreak\n\n\n10:15am – 11:15am\nXu-Jia Wang\, “Monge’s mass transport problem“\n\n\n11:15am – 11:30am\nBreak\n\n\n11:30am – 12:30pm\nPeng-Fei Guan\, “The Weyl isometric embedding problem in general $3$ d Riemannian manifolds“\n\n\n12:30pm – 2:00pm\nLunch\n\n\n2:00pm – 3:00pm\nBlake Temple\, “An instability in the Standard Model of Cosmology“\n\n\n3:00pm – 3:15pm\nBreak\n\n\n3:15pm – 4:15pm\nLydia Bieri\, “The Einstein Equations and Gravitational Radiation“\n\n\n4:15pm – 4:30pm\nBreak\n\n\n4:30pm – 5:30pm\nValentino Tosatti\, “Adiabatic limits of Ricci flat Kahler metrics“\n\n\n\n\n\nApril 9 – Day 2\n\n\n8:45am\nBreakfast\n\n\n9:00am – 10:00am\nD.H. Phong\, “On Strominger systems and Fu-Yau equations”\n\n\n10:00am – 10:15am\nBreak\n\n\n10:15am – 11:15am\nSlawomir Kolodziej\, “Stability of weak solutions of the complex Monge-Ampère equation on compact Hermitian manifolds”\n\n\n11:15am – 11:30am\nBreak\n\n\n11:30am – 12:30pm\nLuis Caffarelli\, “Non local minimal surfaces and their interactions”\n\n\n12:30pm – 2:00pm\nLunch\n\n\n2:00pm – 3:00pm\nMihalis Dafermos\, “The interior of dynamical vacuum black holes and the strong cosmic censorship conjecture in general relativity”\n\n\n3:00pm – 3:15pm\nBreak\n\n\n3:15pm – 4:15pm\nMu-Tao Wang\, “The stability of Lagrangian curvature flows”\n\n\n4:15pm – 4:30pm\nBreak\n\n\n4:30pm – 5:30pm\nMelissa Liu\, “Counting curves in a quintic threefold”\n\n\n\n\n\nApril 10 – Day 3\n\n\n8:45am\nBreakfast\n\n\n9:00am – 10:00am\nRick Schoen\, “Metrics of fixed area on high genus surfaces with largest first eigenvalue”\n\n\n10:00am – 10:15am\nBreak\n\n\n10:15am – 11:15am\nCliff Taubes\, “The zero loci of Z/2 harmonic spinors in dimensions 2\, 3 and 4”\n\n\n11:15am – 11:30am\nBreak\n\n\n11:30am – 12:30pm\nTai-Peng Tsai\, “Forward Self-Similar and Discretely Self-Similar Solutions of the 3D incompressible Navier-Stokes Equations”\n\n\n\n* This event is sponsored by National Science Foundation (NSF) and CMSA Harvard University.
URL:https://cmsa.fas.harvard.edu/event/concluding-conference-of-the-special-program-on-nonlinear-equations-april-8-10-2016-2/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Conference,Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20151029T090000
DTEND;TZID=America/New_York:20151030T170000
DTSTAMP:20260504T060850
CREATED:20230717T180326Z
LAST-MODIFIED:20250304T180538Z
UID:10000014-1446109200-1446224400@cmsa.fas.harvard.edu
SUMMARY:Second Annual STAR Lab Conference
DESCRIPTION:The second annual STAR Lab conference is running 10/29/-10/30/2015 at the Harvard Business School.  This event is co-sponsored by the Center of Mathematical Sciences and Applications. \nFor more information\, please consult the event’s website.
URL:https://cmsa.fas.harvard.edu/event/second-annual-star-lab-conference-2/
LOCATION:20 Garden Street\, Cambridge\, MA 02138\, MA\, MA\, 02138\, United States
CATEGORIES:Conference,Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20150824T084500
DTEND;TZID=America/New_York:20150826T160000
DTSTAMP:20260504T060850
CREATED:20230717T180044Z
LAST-MODIFIED:20250304T180628Z
UID:10000013-1440405900-1440604800@cmsa.fas.harvard.edu
SUMMARY:2015 Conference on Big Data
DESCRIPTION:The Center of Mathematical Sciences and Applications will be having a conference on Big Data August 24-26\, 2015\, in Science Center Hall B at Harvard University.  This conference will feature many speakers from the Harvard Community as well as many scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics.\n\n \nMonday\, August 24 \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:45am\nMeet and Greet\n\n\n\n9:00am\nSendhil Mullainathan\nPrediction Problems in Social Science: Applications of Machine Learning to Policy and Behavioral Economics\n\n\n9:45am\nMike Luca\nDesigning Disclosure for the Digital Age\n\n\n10:30\nBreak\n\n\n\n10:45\nJianqing Fan\nBig Data Big Assumption: Spurious discoveries and endogeneity\n\n\n11:30am\nDaniel Goroff\nPrivacy and Reproducibility in Data Science\n\n\n12:15pm\nBreak for Lunch\n\n\n\n2:00pm\nRyan Adams\nExact Markov Chain Monte Carlo with Large Data\n\n\n2:45pm\nDavid Dunson\nScalable Bayes: Simple algorithms with guarantees\n\n\n3:30pm\nBreak\n\n\n\n3:45pm\nMichael Jordan\nComputational thinking\, inferential thinking and Big Data\n\n\n4:30pm\nJoel Tropp\nApplied Random Matrix Theory\n\n\n5:15pm\nDavid Woodruff\nInput Sparsity and Hardness for Robust Subspace Approximation\n\n\n\nTuesday\, August 25 \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:45am\nMeet and Greet\n\n\n\n9:00am\nGunnar Carlsson\nPersistent homology for qualitative analysis and feature generation\n\n\n9:45am\nAndrea Montanari\nSemidefinite Programming Relaxations for Graph and Matrix Estimation: Algorithms and Phase Transitions\n\n\n10:30am\nBreak\n\n\n\n10:45am\nSusan Athey\nMachine Learning and Causal Inference for Policy Evaluation\n\n\n11:30am\nDenis Nekipelov\nRobust Empirical Evaluation of Large Competitive Markets\n\n\n12:15pm\nBreak for Lunch\n\n\n\n2:00pm\nLucy Colwell\nUsing evolutionary sequence variation to make inferences about protein structure and function: Modeling with Random Matrix Theory\n\n\n2:45pm\nSimona Cocco\nInverse Statistical Physics approaches for the modeling of protein families\n\n\n3:30pm\nBreak\n\n\n\n3:45pm\nRemi Monasson\nInference of top components of correlation matrices with prior informations\n\n\n4:30pm\nSayan Mukherjee\nRandom walks on simplicial complexes and higher order notions of spectral clustering\n\n\n\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \nA Banquet from 7:00 – 8:30pm will follow Tuesday’s talks. This event is by invitation only. \n Wednesday\, August 26  \n\n\n\nTime\nSpeaker\nTitle\n\n\n8:45am\nMeet and Greet\n\n\n\n9:00am\nAnkur Moitra\nBeyond Matrix Completion\n\n\n9:45am\nFlorent Krzakala\nOptimal compressed sensing with spatial coupling and message passing\n\n\n10:30am\nBreak\n\n\n\n10:45am\nPiotr Indyk\nFast Algorithms for Structured Sparsity\n\n\n11:30am\nGuido Imbens\nExact p-values for network inference\n\n\n12:15pm\nBreak for lunch\n\n\n\n2:00pm\nEdo Airoldi\nSome fundamental ideas for causal inference on large networks\n\n\n2:45pm\nRonitt Rubinfeld\nSomething for almost nothing: sublinear time approximation algorithms\n\n\n3:30pm\nBreak\n\n\n\n3:45pm\nLenka Zdeborova\nClustering of sparse networks:  Phase transitions and optimal algorithms\n\n\n4:30pm\nJelani Nelson\nDimensionality reductions via sparse matrices
URL:https://cmsa.fas.harvard.edu/event/conference-on-big-data-august-24-26-2015/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Big Data Conference,Conference,Event
END:VEVENT
END:VCALENDAR