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DTSTART;TZID=America/New_York:20170907T170000
DTEND;TZID=America/New_York:20170907T180000
DTSTAMP:20260701T232137
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SUMMARY:Noga Alon Public Talk
DESCRIPTION:Noga Alon (Tel Aviv University) will be giving a public talk on September 7\, 2017\,as part of the program on combinatorics and complexity hosted by the CMSA during AY17-18.  The talk will be at 5:00pm in Askwith Hall\, 13 Appian Way\, Cambridge\, MA. \nTitle: Graph Coloring: Local and Global \nAbstract: Graph Coloring is arguably the most popular subject in Discrete Mathematics\, and its combinatorial\, algorithmic and computational aspects have been studied intensively. The most basic notion in the area\, the chromatic number of a graph\, is an inherently global property. This is demonstrated by the hardness of computation or approximation of this invariant as well as by the existence of graphs with arbitrarily high chromatic number and no short cycles. The investigation of these graphs had a profound impact on Graph Theory and Combinatorics. It combines combinatorial\, probabilistic\, algebraic and topological techniques with number theoretic tools. I will describe the rich history of the subject focusing on some recent results. \n 
URL:https://cmsa.fas.harvard.edu/event/noga-alon-public-talk-9-7-17/
LOCATION:MA
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DTSTART;TZID=America/New_York:20170818T154700
DTEND;TZID=America/New_York:20170819T154700
DTSTAMP:20260701T232137
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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:20260701T232137
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170523T133200
DTEND;TZID=America/New_York:20170523T133200
DTSTAMP:20260701T232137
CREATED:20230801T174602Z
LAST-MODIFIED:20240209T152416Z
UID:10000033-1495546320-1495546320@cmsa.fas.harvard.edu
SUMMARY:5/23/2017 CMSA Special Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/5-23-2017-cmsa-special-seminar/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170503T132900
DTEND;TZID=America/New_York:20170503T132900
DTSTAMP:20260701T232137
CREATED:20240213T102956Z
LAST-MODIFIED:20240213T102956Z
UID:10002432-1493818140-1493818140@cmsa.fas.harvard.edu
SUMMARY:5-3-2017 Random Matrix & Probability Theory Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/5-3-2017-random-matrix-probability-theory-seminar/
LOCATION:MA
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170502T133000
DTEND;TZID=America/New_York:20170502T133000
DTSTAMP:20260701T232137
CREATED:20240213T102711Z
LAST-MODIFIED:20240213T102711Z
UID:10002427-1493731800-1493731800@cmsa.fas.harvard.edu
SUMMARY:5-2-2017 Social Sciences Application Forum
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/5-2-2017-social-sciences-application-forum/
LOCATION:MA
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170501T090000
DTEND;TZID=America/New_York:20170502T170000
DTSTAMP:20260701T232137
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:20260701T232137
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:20170427T132100
DTEND;TZID=America/New_York:20170427T132100
DTSTAMP:20260701T232137
CREATED:20230801T174502Z
LAST-MODIFIED:20231221T075008Z
UID:10000029-1493299260-1493299260@cmsa.fas.harvard.edu
SUMMARY:4-27-2017 CMSA Special Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-27-2017-cmsa-special-seminar/
LOCATION:MA
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170424T133100
DTEND;TZID=America/New_York:20170424T133100
DTSTAMP:20260701T232137
CREATED:20240213T102519Z
LAST-MODIFIED:20240213T102519Z
UID:10002422-1493040660-1493040660@cmsa.fas.harvard.edu
SUMMARY:4-24-2017 Mathematical Physics Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-24-2017-mathematical-physics-seminar/
LOCATION:MA
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170423T090000
DTEND;TZID=America/New_York:20170424T170000
DTSTAMP:20260701T232137
CREATED:20230717T174601Z
LAST-MODIFIED:20250304T181313Z
UID:10000028-1492938000-1493053200@cmsa.fas.harvard.edu
SUMMARY:Workshop on Quantum Information
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a workshop on Quantum Information on April 23-24\, 2018. In the days leading up to the conference\, the American Mathematical Society will also be hosting a sectional meeting on quantum information on April 21-22. You can find more information here. \nThe following speakers are confirmed: \n\nFernando G.S.L Brandão (CalTech)\nJacob Biamonte (Skoltech)\nIsaac Chuang (MIT)\nIris Cong (Harvard)\nAram Harrow (MIT)\nKe Li (HIT)\nMikhail D. Lukin (Harvard)\nShunlong Luo (AMSS)\nRenato Renner (ETH Zürich)\nPeter Shor (MIT)
URL:https://cmsa.fas.harvard.edu/event/workshop-on-quantum-information/
LOCATION:20 Garden Street\, Cambridge\, MA 02138\, MA\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170419T132200
DTEND;TZID=America/New_York:20170419T132200
DTSTAMP:20260701T232137
CREATED:20240213T092921Z
LAST-MODIFIED:20240213T092921Z
UID:10002336-1492608120-1492608120@cmsa.fas.harvard.edu
SUMMARY:4-19-2017 Random Matrix & Probability Theory Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-19-2017-random-matrix-probability-theory-seminar/
LOCATION:MA
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170418T110000
DTEND;TZID=America/New_York:20170418T120000
DTSTAMP:20260701T232137
CREATED:20240213T093734Z
LAST-MODIFIED:20240220T144719Z
UID:10002348-1492513200-1492516800@cmsa.fas.harvard.edu
SUMMARY:4-18-2017 Social Science Applications Forum
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-18-2017-social-science-applications-forum/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170417T131400
DTEND;TZID=America/New_York:20170417T131400
DTSTAMP:20260701T232137
CREATED:20240213T094054Z
LAST-MODIFIED:20240213T094054Z
UID:10002355-1492434840-1492434840@cmsa.fas.harvard.edu
SUMMARY:4-17-2017 Mathematical Physics Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-17-2017-mathematical-physics-seminar/
LOCATION:MA
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170414T124600
DTEND;TZID=America/New_York:20170414T124600
DTSTAMP:20260701T232137
CREATED:20240213T100101Z
LAST-MODIFIED:20240213T100101Z
UID:10002378-1492173960-1492173960@cmsa.fas.harvard.edu
SUMMARY:4-14-2017 Special Lecture Series
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-14-2017-special-lecture-series/
LOCATION:MA
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170412T125300
DTEND;TZID=America/New_York:20170412T125300
DTSTAMP:20260701T232137
CREATED:20240213T095445Z
LAST-MODIFIED:20240213T095445Z
UID:10002368-1492001580-1492001580@cmsa.fas.harvard.edu
SUMMARY:4-12-2017 Social Science Applications Forum
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-12-2017-social-science-applications-forum/
LOCATION:MA
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170412T124400
DTEND;TZID=America/New_York:20170412T124400
DTSTAMP:20260701T232137
CREATED:20240213T100333Z
LAST-MODIFIED:20240213T100333Z
UID:10002385-1492001040-1492001040@cmsa.fas.harvard.edu
SUMMARY:4-12-2017 Special Lecture Series
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-12-2017-special-lecture-series/
LOCATION:MA
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170412T115600
DTEND;TZID=America/New_York:20170412T115600
DTSTAMP:20260701T232137
CREATED:20240213T093903Z
LAST-MODIFIED:20240227T113239Z
UID:10002351-1491998160-1491998160@cmsa.fas.harvard.edu
SUMMARY:04-12-2017 Random Matrix & Probability Theory Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/04-12-2017-random-matrix-probability-theory-seminar/
LOCATION:MA
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170411T130000
DTEND;TZID=America/New_York:20170411T130000
DTSTAMP:20260701T232137
CREATED:20240213T094754Z
LAST-MODIFIED:20240213T094754Z
UID:10002365-1491915600-1491915600@cmsa.fas.harvard.edu
SUMMARY:4-11-2017 Social Science Applications Forum
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-11-2017-social-science-applications-forum/
LOCATION:MA
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170410T130700
DTEND;TZID=America/New_York:20170410T130700
DTSTAMP:20260701T232137
CREATED:20240213T094427Z
LAST-MODIFIED:20240213T094427Z
UID:10002360-1491829620-1491829620@cmsa.fas.harvard.edu
SUMMARY:4-10-2017 Mathematical Physics Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-10-2017-mathematical-physics-seminar/
LOCATION:MA
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170407T124300
DTEND;TZID=America/New_York:20170407T124300
DTSTAMP:20260701T232137
CREATED:20240213T100607Z
LAST-MODIFIED:20240213T100607Z
UID:10002391-1491568980-1491568980@cmsa.fas.harvard.edu
SUMMARY:4-7-2017 Special Lecture Series
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-7-2017-special-lecture-series/
LOCATION:MA
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170406T125100
DTEND;TZID=America/New_York:20170406T125100
DTSTAMP:20260701T232137
CREATED:20230801T174359Z
LAST-MODIFIED:20231221T074635Z
UID:10000027-1491483060-1491483060@cmsa.fas.harvard.edu
SUMMARY:4-6-2017 CMSA Special Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-6-2017-cmsa-special-seminar/
LOCATION:MA
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170405T124200
DTEND;TZID=America/New_York:20170405T124200
DTSTAMP:20260701T232137
CREATED:20240213T101247Z
LAST-MODIFIED:20240213T101247Z
UID:10002402-1491396120-1491396120@cmsa.fas.harvard.edu
SUMMARY:4-5-2017 Special Lecture Series
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-5-2017-special-lecture-series/
LOCATION:MA
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170405T123100
DTEND;TZID=America/New_York:20170405T123100
DTSTAMP:20260701T232137
CREATED:20240213T103542Z
LAST-MODIFIED:20240213T103542Z
UID:10002444-1491395460-1491395460@cmsa.fas.harvard.edu
SUMMARY:4-5-2017 Random Matrix & Probability Theory Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-5-2017-random-matrix-probability-theory-seminar/
LOCATION:MA
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170403T125000
DTEND;TZID=America/New_York:20170403T125000
DTSTAMP:20260701T232137
CREATED:20240213T095854Z
LAST-MODIFIED:20240213T095854Z
UID:10002375-1491223800-1491223800@cmsa.fas.harvard.edu
SUMMARY:4-3-2017 Mathematical Physics Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-3-2017-mathematical-physics-seminar/
LOCATION:MA
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170330T123800
DTEND;TZID=America/New_York:20170330T123800
DTSTAMP:20260701T232137
CREATED:20240213T102411Z
LAST-MODIFIED:20240213T102411Z
UID:10002418-1490877480-1490877480@cmsa.fas.harvard.edu
SUMMARY:3-30-2017 CMSA Special Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/3-30-2017-cmsa-special-seminar/
LOCATION:MA
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170329T123500
DTEND;TZID=America/New_York:20170329T123500
DTSTAMP:20260701T232137
CREATED:20240213T103211Z
LAST-MODIFIED:20240213T103211Z
UID:10002437-1490790900-1490790900@cmsa.fas.harvard.edu
SUMMARY:3-29-2017 Random Matrix & Probability Theory Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/3-29-2017-random-matrix-probability-theory-seminar/
LOCATION:MA
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170327T153400
DTEND;TZID=America/New_York:20170330T153400
DTSTAMP:20260701T232137
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:20170327T124000
DTEND;TZID=America/New_York:20170327T124000
DTSTAMP:20260701T232137
CREATED:20240213T102023Z
LAST-MODIFIED:20240213T102023Z
UID:10002414-1490618400-1490618400@cmsa.fas.harvard.edu
SUMMARY:03-27-2017 Mathematical Physics Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/03-27-2017-mathematical-physics-seminar/
LOCATION:MA
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170324T100000
DTEND;TZID=America/New_York:20170324T110000
DTSTAMP:20260701T232137
CREATED:20240213T102725Z
LAST-MODIFIED:20240220T143442Z
UID:10002428-1490349600-1490353200@cmsa.fas.harvard.edu
SUMMARY:3-24-2017 Random Matrix & Probability Theory Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/3-24-2017-random-matrix-probability-theory-seminar/
LOCATION:CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
END:VCALENDAR