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DTSTART;TZID=America/New_York:20220519T090000
DTEND;TZID=America/New_York:20220519T100000
DTSTAMP:20260505T234734
CREATED:20240214T084730Z
LAST-MODIFIED:20240301T102658Z
UID:10002598-1652950800-1652954400@cmsa.fas.harvard.edu
SUMMARY:The geometry of conditional independence models with hidden variables
DESCRIPTION:Abstract: Conditional independence (CI) is an important tool instatistical modeling\, as\, for example\, it gives a statistical interpretation to graphical models. In general\, given a list of dependencies among random variables\, it is difficult to say which constraints are implied by them. Moreover\, it is important to know what constraints on the random variables are caused by hidden variables. On the other hand\, such constraints are corresponding to some determinantal conditions on the tensor of joint probabilities of the observed random variables. Hence\, the inference question in statistics relates to understanding the algebraic and geometric properties of determinantal varieties such as their irreducible decompositions or determining their defining equations. I will explain some recent progress that arises by uncovering the link to point configurations in matroid theory and incidence geometry. This connection\, in particular\, leads to effective computational approaches for (1) giving a decomposition for each CI variety; (2) identifying each component in the decomposition as a matroid variety; (3) determining whether the variety has a real point or equivalently there is a statistical model satisfying a given collection of dependencies. The talk is based on joint works with Oliver Clarke\, Kevin Grace\, and Harshit Motwani. \nThe papers are available on arxiv: https://arxiv.org/pdf/2011.02450\nand https://arxiv.org/pdf/2103.16550.pdf
URL:https://cmsa.fas.harvard.edu/event/5-19-2022-cmsa-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-05.19.22-1583x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220512T153800
DTEND;TZID=America/New_York:20220512T173800
DTSTAMP:20260505T234734
CREATED:20240214T084325Z
LAST-MODIFIED:20240301T102818Z
UID:10002594-1652369880-1652377080@cmsa.fas.harvard.edu
SUMMARY:Geometric Models for Sets of Probability Measures
DESCRIPTION:Abstract: Many statistical and computational tasks boil down to comparing probability measures expressed as density functions\, clouds of data points\, or generative models.  In this setting\, we often are unable to match individual data points but rather need to deduce relationships between entire weighted and unweighted point sets. In this talk\, I will summarize our team’s recent efforts to apply geometric techniques to problems in this space\, using tools from optimal transport and spectral geometry. Motivated by applications in dataset comparison\, time series analysis\, and robust learning\, our work reveals how to apply geometric reasoning to data expressed as probability measures without sacrificing computational efficiency.
URL:https://cmsa.fas.harvard.edu/event/5-12-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-05.12.22-1583x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220505T153600
DTEND;TZID=America/New_York:20220505T173600
DTSTAMP:20260505T234734
CREATED:20240214T084023Z
LAST-MODIFIED:20240301T102954Z
UID:10002592-1651764960-1651772160@cmsa.fas.harvard.edu
SUMMARY:Qianfang: a type-safe and data-driven healthcare system starting from Traditional Chinese Medicine
DESCRIPTION:Abstract: Although everyone talks about AI + healthcare\, many people were unaware of the fact that there are two possible outcomes of the collaboration\, due to the inherent dissimilarity between the two giant subjects. The first possibility is healthcare-leads\, and AI is for building new tools to make steps in healthcare easier\, better\, more effective or more accurate. The other possibility is AI-leads\, and therefore the protocols of healthcare can be redesigned or redefined to make sure that the whole infrastructure and pipelines are ideal for running AI algorithms. \nOur system Qianfang belongs to the second category. We have designed a new kind of clinic for the doctors and patients\, so that it will be able to collect high quality data for AI algorithms. Interestingly\, the clinic is based on Traditional Chinese Medicine (TCM) instead of modern medicine\, because we believe that TCM is more suitable for AI algorithms as the starting point. \nIn this talk\, I will elaborate on how we convert TCM knowledge into a modern type-safe large-scale system\, the mini-language that we have designed for the doctors and patients\, the interpretability of AI decisions\, and our feedback loop for collecting data. \nOur project is still on-going\, not finished yet.Bio: Yang Yuan is now an assistant professor at IIIS\, Tsinghua. He finished his undergraduate study at Peking University in 2012. Afterwards\, he received his PhD at Cornell University in 2018\, advised by Professor Robert Kleinberg. During his PhD\, he was a visiting student at MIT/Microsoft New England (2014-2015) and Princeton University (2016 Fall). Before joining Tsinghua\, he spent one year at MIT Institute for Foundations of Data Science (MIFODS) as a postdoc researcher. He now works on AI+Healthcare\, AI Interpretability and AI system.
URL:https://cmsa.fas.harvard.edu/event/5-5-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-05.05.2022-1583x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220428T153300
DTEND;TZID=America/New_York:20220428T173300
DTSTAMP:20260505T234734
CREATED:20240214T112923Z
LAST-MODIFIED:20240301T103000Z
UID:10002698-1651159980-1651167180@cmsa.fas.harvard.edu
SUMMARY:Intersection number and systole on hyperbolic surfaces
DESCRIPTION:Abstract: Let X be a compact hyperbolic surface. We can see that there is a constant C(X) such that the intersection number of the closed geodesics is  \leq C(X) times the product of their lengths. Consider the optimum constant C(X). In this talk\, we describe its asymptotic behavior in terms of systole\,  length of the shortest closed geodesic on X.
URL:https://cmsa.fas.harvard.edu/event/4-28-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-04.28.22-1583x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220421T090000
DTEND;TZID=America/New_York:20220421T100000
DTSTAMP:20260505T234734
CREATED:20240214T113250Z
LAST-MODIFIED:20240301T103156Z
UID:10002700-1650531600-1650535200@cmsa.fas.harvard.edu
SUMMARY:Secure Multi-Party Computation: from Theory to Practice
DESCRIPTION:Abstract:\nEncryption is the backbone of cybersecurity. While encryption can secure data both in transit and at rest\, in the new era of ubiquitous computing\, modern cryptography also aims to protect data during computation. Secure multi-party computation (MPC) is a powerful technology to tackle this problem\, which enables distrustful parties to jointly perform computation over their private data without revealing their data to each other. Although it is theoretically feasible and provably secure\, the adoption of MPC in real industry is still very much limited as of today\, the biggest obstacle of which boils down to its efficiency. \nMy research goal is to bridge the gap between the theoretical feasibility and practical efficiency of MPC. Towards this goal\, my research spans both theoretical and applied cryptography. In theory\, I develop new techniques for achieving general MPC with the optimal complexity\, bringing theory closer to practice. In practice\, I design tailored MPC to achieve the best concrete efficiency for specific real-world applications. In this talk\, I will discuss the challenges in both directions and how to overcome these challenges using cryptographic approaches. I will also show strong connections between theory and practice. \nBiography:\nPeihan Miao is an assistant professor of computer science at the University of Illinois Chicago (UIC). Before coming to UIC\, she received her Ph.D. from the University of California\, Berkeley in 2019 and had brief stints at Google\, Facebook\, Microsoft Research\, and Visa Research. Her research interests lie broadly in cryptography\, theory\, and security\, with a focus on secure multi-party computation — especially in incorporating her industry experiences into academic research.
URL:https://cmsa.fas.harvard.edu/event/4-21-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-04.21.22-1583x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220414T090000
DTEND;TZID=America/New_York:20220414T100000
DTSTAMP:20260505T234734
CREATED:20240214T113429Z
LAST-MODIFIED:20240301T103314Z
UID:10002701-1649926800-1649930400@cmsa.fas.harvard.edu
SUMMARY:SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation
DESCRIPTION:Abstract: A main challenge in analyzing single-cell RNA sequencing (scRNA-seq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called ‘dropout’)\, the gene expression matrix has a substantial amount of zero read counts. Existing imputation methods treat either each cell or each gene as independently and identically distributed\, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach\, called SIMPLEs (SIngle-cell RNA-seq iMPutation and celL clustErings)\, which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously\, it quantifies the uncertainty of imputation and cell clustering via multiple imputations. In simulations\, SIMPLEs performed significantly better than prevailing scRNA-seq imputation methods according to various metrics. By applying SIMPLEs to several real datasets\, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.
URL:https://cmsa.fas.harvard.edu/event/4-14-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-04.14.22-1583x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220407T152200
DTEND;TZID=America/New_York:20220407T172200
DTSTAMP:20260505T234734
CREATED:20240214T113556Z
LAST-MODIFIED:20240301T103440Z
UID:10002702-1649344920-1649352120@cmsa.fas.harvard.edu
SUMMARY:The space of vector bundles on spheres: algebra\, geometry\, topology
DESCRIPTION:Abstract: Bott periodicity relates vector bundles on a topological space X to vector bundles on X “times a sphere”.   I’m not a topologist\, so I will try to explain an algebraic or geometric incarnation\, in terms of vector bundles on the Riemann sphere.   I will attempt to make the talk introductory\, and (for the most part) accessible to those in all fields\, at the expense of speaking informally and not getting far.   This relates to recent work of Hannah Larson\, as well as joint work with (separately) Larson and Jim Bryan.
URL:https://cmsa.fas.harvard.edu/event/4-7-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-04.07.2022-1583x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220331T152000
DTEND;TZID=America/New_York:20220331T172000
DTSTAMP:20260505T234734
CREATED:20240214T113726Z
LAST-MODIFIED:20240301T103621Z
UID:10002704-1648740000-1648747200@cmsa.fas.harvard.edu
SUMMARY:Compactification of an embedded vector space and its combinatorics
DESCRIPTION:Abstract: Matroids are combinatorial abstractions of vector spaces embedded in a coordinate space.  Many fundamental questions have been open for these classical objects.  We highlight some recent progress that arise from the interaction between matroid theory and algebraic geometry.  Key objects involve compactifications of embedded vector spaces\, and an exceptional Hirzebruch-Riemann-Roch isomorphism between the K-ring of vector bundles and the cohomology ring of stellahedral varieties.
URL:https://cmsa.fas.harvard.edu/event/3-31-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-03.231.2022-1583x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220324T151700
DTEND;TZID=America/New_York:20220324T171700
DTSTAMP:20260505T234734
CREATED:20240215T091039Z
LAST-MODIFIED:20240301T104333Z
UID:10002708-1648135020-1648142220@cmsa.fas.harvard.edu
SUMMARY:An operadic structure on supermoduli spaces
DESCRIPTION:Abstract: The operadic structure on the moduli spaces of algebraic curves  encodes in a combinatorial way how nodal curves in the boundary can be obtained by glueing smooth curves along marked points. In this talk\, I will present a generalization of the operadic structure to moduli spaces of SUSY curves (or super Riemann surfaces). This requires colored graphs and generalized operads in the sense of Borisov-Manin. Based joint work with Yu. I. Manin and Y. Wu. https://arxiv.org/abs/2202.10321
URL:https://cmsa.fas.harvard.edu/event/3-24-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-03.24.2022-1583x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220317T151500
DTEND;TZID=America/New_York:20220317T161500
DTSTAMP:20260505T234734
CREATED:20240215T091301Z
LAST-MODIFIED:20240301T104445Z
UID:10002709-1647530100-1647533700@cmsa.fas.harvard.edu
SUMMARY:On optimization and generalization in deep learning
DESCRIPTION:Abstract: Deep neural networks have achieved significant empirical success in many fields\, including the fields of computer vision and natural language processing. Along with its empirical success\, deep learning has been theoretically shown to be attractive in terms of its expressive power. However\, the theory of expressive power does not ensure that we can efficiently find an optimal solution in terms of optimization and generalization\, during the optimization process. In this talk\, I will discuss some mathematical properties of optimization and generalization for deep neural networks.
URL:https://cmsa.fas.harvard.edu/event/3-17-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-03.17.2022-1583x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220310T151300
DTEND;TZID=America/New_York:20220310T161300
DTSTAMP:20260505T234734
CREATED:20240215T091511Z
LAST-MODIFIED:20240301T104543Z
UID:10002710-1646925180-1646928780@cmsa.fas.harvard.edu
SUMMARY:Virtual Teams in Gig Economy — An End-to-End Data Science Approach
DESCRIPTION:Abstract: The gig economy provides workers with the benefits of autonomy and flexibility\, but it does so at the expense of work identity and co-worker bonds. Among the many reasons why gig workers leave their platforms\, an unexplored aspect is the organization identity. In a series of studies\, we develop a team formation and inter-team contest at a ride-sharing platform. We employ an end-to-end data science approach\, combining methodologies from randomized field experiments\, recommender systems\, and counterfactual machine learning. Together\, our results show that platform designers can leverage team identity and team contests to increase revenue and worker engagement in a gig economy. \nBio: Wei Ai is an Assistant Professor in the College of Information Studies (iSchool) and the Institute for Advanced Computer Studies (UMIACS) at the University of Maryland. His research interest lies in data science for social good\, where the advances of machine learning and data analysis algorithms translate into measurable impacts on society. He combines machine learning\, causal inference\, and field experiments in his research\, and has rich experience in collaborating with industrial partners. He earned his Ph.D. from the School of Information at the University of Michigan. His research has been published in top journals and conferences\, including PNAS\, ACM TOIS\, WWW\, and ICWSM.
URL:https://cmsa.fas.harvard.edu/event/3-10-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-03.10.2022-1583x2048-1-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220303T151100
DTEND;TZID=America/New_York:20220303T161100
DTSTAMP:20260505T234735
CREATED:20240215T091737Z
LAST-MODIFIED:20240301T104734Z
UID:10002711-1646320260-1646323860@cmsa.fas.harvard.edu
SUMMARY:Towards Understanding Training Dynamics for Mildly Overparametrized Models
DESCRIPTION:Abstract: While over-parameterization is widely believed to be crucial for the success of optimization for the neural networks\, most existing theories on over-parameterization do not fully explain the reason — they either work in the Neural Tangent Kernel regime where neurons don’t move much\, or require an enormous number of neurons. In this talk I will describe our recent works towards understanding training dynamics that go beyond kernel regimes with only polynomially many neurons (mildly overparametrized). In particular\, we first give a local convergence result for mildly overparametrized two-layer networks. We then analyze the global training dynamics for a related overparametrized tensor model. For both works\, we rely on a key intuition that neurons in overparametrized models work in groups and it’s important to understand the behavior of an average neuron in the group. Based on two works: https://arxiv.org/abs/2102.02410 and https://arxiv.org/abs/2106.06573. \nBio: Professor Rong Ge is Associate Professor of Computer Science at Duke University. He received his Ph.D. from the Computer Science Department of Princeton University\, supervised by Sanjeev Arora. He was a post-doc at Microsoft Research\, New England. In 2019\, he received both a Faculty Early Career Development Award from the National Science Foundation and the prestigious Sloan Research Fellowship. His research interest focus on theoretical computer science and machine learning. Modern machine learning algorithms such as deep learning try to automatically learn useful hidden representations of the data. He is interested in formalizing hidden structures in the data and designing efficient algorithms to find them. His research aims to answer these questions by studying problems that arise in analyzing text\, images\, and other forms of data\, using techniques such as non-convex optimization and tensor decompositions.
URL:https://cmsa.fas.harvard.edu/event/3-3-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-03.03.2022-1583x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220224T150800
DTEND;TZID=America/New_York:20220224T160800
DTSTAMP:20260505T234735
CREATED:20240215T091941Z
LAST-MODIFIED:20240301T104857Z
UID:10002713-1645715280-1645718880@cmsa.fas.harvard.edu
SUMMARY:Singular Set in Obstacle Problems
DESCRIPTION:Abstract: In this talk we describe a new method to study the singular set in the obstacle problem. This method does not depend on monotonicity formulae and works for fully nonlinear elliptic operators. The result we get matches the best-known result for the case of Laplacian.
URL:https://cmsa.fas.harvard.edu/event/2-24-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-2.24.2022-1583x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220217T150500
DTEND;TZID=America/New_York:20220217T160500
DTSTAMP:20260505T234735
CREATED:20240215T092142Z
LAST-MODIFIED:20240301T105602Z
UID:10002714-1645110300-1645113900@cmsa.fas.harvard.edu
SUMMARY:Sparse Markov Models for High-dimensional Inference
DESCRIPTION:Abstract: Finite order Markov models are theoretically well-studied models for dependent data.  Despite their generality\, application in empirical work when the order is larger than one is quite rare.  Practitioners avoid using higher order Markov models because (1) the number of parameters grow exponentially with the order\, (2) the interpretation is often difficult. Mixture of transition distribution models (MTD)  were introduced to overcome both limitations. MTD represent higher order Markov models as a convex mixture of single step Markov chains\, reducing the number of parameters and increasing the interpretability. Nevertheless\, in practice\, estimation of MTD models with large orders are still limited because of curse of dimensionality and high algorithm complexity. Here\, we prove that if only few lags are relevant we can consistently and efficiently recover the lags and estimate the transition probabilities of high order MTD models. Furthermore\, we show that using the selected lags we can construct non-asymptotic confidence intervals for the transition probabilities of the model. The key innovation is a recursive procedure for the selection of the relevant lags of the model.  Our results are  based on (1) a new structural result of the MTD and (2) an improved martingale concentration inequality. Our theoretical results are illustrated through simulations.
URL:https://cmsa.fas.harvard.edu/event/2-17-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-2.17.2022-1-1583x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220210T150000
DTEND;TZID=America/New_York:20220210T160000
DTSTAMP:20260505T234735
CREATED:20240215T092349Z
LAST-MODIFIED:20240301T105720Z
UID:10002716-1644505200-1644508800@cmsa.fas.harvard.edu
SUMMARY:2/10/2022 – Interdisciplinary Science Seminar
DESCRIPTION:Title: Metric Algebraic Geometry \nAbstract: A real algebraic variety is the set of points in real Euclidean space that satisfy a system of polynomial equations. Metric algebraic geometry is the study of properties of real algebraic varieties that depend on a distance metric. In this talk\, we introduce metric algebraic geometry through a discussion of Voronoi cells\, bottlenecks\, and the reach of an algebraic variety. We also show applications to the computational study of the geometry of data with nonlinear models.
URL:https://cmsa.fas.harvard.edu/event/2-10-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-2.10.2022-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220203T145700
DTEND;TZID=America/New_York:20220203T165700
DTSTAMP:20260505T234735
CREATED:20240215T092602Z
LAST-MODIFIED:20240301T105825Z
UID:10002717-1643900220-1643907420@cmsa.fas.harvard.edu
SUMMARY:2/3/2022 – Interdisciplinary Science Seminar
DESCRIPTION:Title:Quasiperiodic prints from triply periodic blocks \nAbstract: Slice a triply periodic wooden sculpture along an irrational plane. If you ink the cut surface and press it against a page\, the pattern you print will be quasiperiodic. Patterns like these help physicists see how metals conduct electricity in strong magnetic fields. I’ll show you some block prints that imitate the printing process described above\, and I’ll point out the visual features that reveal conductivity properties. \nInteractive slides:https://www.ihes.fr/~fenyes/seeing/slices/
URL:https://cmsa.fas.harvard.edu/event/2-3-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-2.03.2022-1583x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220127T145400
DTEND;TZID=America/New_York:20220127T165400
DTSTAMP:20260505T234735
CREATED:20240215T092855Z
LAST-MODIFIED:20240215T092855Z
UID:10002718-1643295240-1643302440@cmsa.fas.harvard.edu
SUMMARY:1/27/2022 – Interdisciplinary Science Seminar
DESCRIPTION:Title: Polynomials vanishing at lattice points in convex sets \nAbstract: Let P be a convex subset of R^2. For large d\, what is the smallest degree r_d of a polynomial vanishing at all lattice points in the dilate d*P? We show that r_d / d converges to some positive number\, which we compute for many (but maybe not all) triangles P.
URL:https://cmsa.fas.harvard.edu/event/1-27-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-1.27.2022-1583x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220120T145200
DTEND;TZID=America/New_York:20220120T165200
DTSTAMP:20260505T234735
CREATED:20240215T093039Z
LAST-MODIFIED:20240215T093039Z
UID:10002719-1642690320-1642697520@cmsa.fas.harvard.edu
SUMMARY:1/20/2022 – Interdisciplinary Science Seminar
DESCRIPTION:Title: Markov chains\, optimal control\, and reinforcement learning \nAbstract: Markov decision processes are a model for several artificial intelligence problems\, such as games (chess\, Go…) or robotics. At each timestep\, an agent has to choose an action\, then receives a reward\, and then the agent’s environment changes (deterministically or stochastically) in response to the agent’s action. The agent’s goal is to adjust its actions to maximize its total reward. In principle\, the optimal behavior can be obtained by dynamic programming or optimal control techniques\, although practice is another story. \nHere we consider a more complex problem: learn all optimal behaviors for all possible reward functions in a given environment. Ideally\, such a “controllable agent” could be given a description of a task (reward function\, such as “you get +10 for reaching here but -1 for going through there”) and immediately perform the optimal behavior for that task. This requires a good understanding of the mapping from a reward function to the associated optimal behavior. \nWe prove that there exists a particular “map” of a Markov decision process\, on which near-optimal behaviors for all reward functions can be read directly by an algebraic formula. Moreover\, this “map” is learnable by standard deep learning techniques from random interactions with the environment. We will present our recent theoretical and empirical results in this direction.
URL:https://cmsa.fas.harvard.edu/event/1-20-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-01.20.22-1577x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220106T090000
DTEND;TZID=America/New_York:20220106T100000
DTSTAMP:20260505T234735
CREATED:20240215T093525Z
LAST-MODIFIED:20240529T175141Z
UID:10002721-1641459600-1641463200@cmsa.fas.harvard.edu
SUMMARY:The smooth closing lemma for area-preserving surface diffeomorphisms
DESCRIPTION:Speaker: Boyu Zhang\, Princeton University \nTitle: The smooth closing lemma for area-preserving surface diffeomorphisms \nAbstract: In this talk\, I will introduce the smooth closing lemma for area-preserving diffeomorphisms on surfaces. The proof is based on a Weyl formula for PFH spectral invariants and a non-vanishing result of twisted Seiberg- Witten Floer homology. This is joint work with Dan Cristofaro-Gardiner and Rohil Prasad.
URL:https://cmsa.fas.harvard.edu/event/1-6-2022-interdisciplinary-science-seminar/
LOCATION:Virtual
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-01.06.22.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T144600
DTEND;TZID=America/New_York:20211216T154600
DTSTAMP:20260505T234735
CREATED:20240215T093643Z
LAST-MODIFIED:20240215T093643Z
UID:10002722-1639665960-1639669560@cmsa.fas.harvard.edu
SUMMARY:12/16/2021 Interdisciplinary Science Seminar
DESCRIPTION:Title: Quadratic reciprocity from a family of adelic conformal field theories \nAbstract: We consider a deformation of the 2d free scalar field action by raising the Laplacian to a positive real power. It turns out that the resulting non-local generalized free action is invariant under two commuting actions of the global conformal symmetry algebra\, although it’s no longer invariant under the local conformal symmetry algebra. Furthermore\, there is an adelic version of this family of global conformal field theories\, parametrized by the choice of a number field\, together with a Hecke character. Tate’s thesis plays an important role here in calculating Green’s functions of these theories\, and in ensuring the adelic compatibility of these theories. In particular\, the local L-factors contribute to prefactors of these Green’s functions. We shall try to see quadratic reciprocity from this context\, as a consequence of an adelic version of holomorphic factorization of these theories. This is work in progress with B. Stoica and X. Zhong.
URL:https://cmsa.fas.harvard.edu/event/12-16-2021-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T142900
DTEND;TZID=America/New_York:20211209T152900
DTSTAMP:20260505T234735
CREATED:20240215T093828Z
LAST-MODIFIED:20240215T093828Z
UID:10002723-1639060140-1639063740@cmsa.fas.harvard.edu
SUMMARY:12/9/21 Interdisciplinary Science Seminar
DESCRIPTION:Title: Numerical Higher Dimensional Geometry \nAbstract: In 1977\, Yau proved that a Kahler manifold with zero first Chern class admits a Ricci flat metric\, which is uniquely determined by certain “moduli” data. These metrics have been very important in mathematics and in theoretical physics\, but despite much subsequent work we have no analytical expressions for them. But significant progress has been made on computing numerical approximations. We give an introduction (not assuming knowledge of complex geometry) to these problems and describe these methods.
URL:https://cmsa.fas.harvard.edu/event/12-9-21-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-12.09.21-1583x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211202T142800
DTEND;TZID=America/New_York:20211202T152800
DTSTAMP:20260505T234735
CREATED:20240215T094151Z
LAST-MODIFIED:20240301T110349Z
UID:10002725-1638455280-1638458880@cmsa.fas.harvard.edu
SUMMARY:12/2/2021 Interdisciplinary Science Seminar
DESCRIPTION:Title: Polyhomogeneous expansions and Z/2-harmonic spinors branching along graphs \nAbstract: In this talk\, we will first reformulate the linearization of the moduli space of Z/2-harmonic spinorsv branching along a knot. This formula tells us that the kernel and cokernel of the linearization are isomorphic to the kernel and cokernel of the Dirac equation with a polyhomogeneous boundary condition. In the second part of this talk\, I will describe the polyhomogenous expansions for the Z/2-harmonic spinors branching along graphs and formulate the Dirac equation with a suitable boundary condition that can describe the perturbation of graphs with some restrictions. This is joint work with Andriy Haydys and Rafe Mazzeo.
URL:https://cmsa.fas.harvard.edu/event/12-2-2021-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211118T142500
DTEND;TZID=America/New_York:20211118T152500
DTSTAMP:20260505T234735
CREATED:20240214T075510Z
LAST-MODIFIED:20240301T103505Z
UID:10002572-1637245500-1637249100@cmsa.fas.harvard.edu
SUMMARY:11/18/2021 Interdisciplinary Science Seminar
DESCRIPTION:Title: Amplituhedra\, Scattering Amplitudes and Triangulations \nAbstract: In this talk I will discuss about Amplituhedra – generalizations of polytopes inside the Grassmannian – recently introduced by physicists as new geometric constructions encoding interactions of elementary particles in certain Quantum Field Theories. In particular\, I will explain how the problem of finding triangulations of Amplituhedra is connected to computing scattering amplitudes of N=4 super Yang-Mills theory. Triangulations of polygons are encoded in the associahedron studied by Stasheff in the sixties; in the case of polytopes\, triangulations are captured by secondary polytopes constructed by Gelfand et al. in the nineties. Whereas a “secondary” geometry describing triangulations of Amplituhedra is still not known\, and we pave the way for such studies. We will discuss how the combinatorics of triangulations interplays with T-duality from String Theory\, in connection with a dual object we define – the Momentum Amplituhedron. A generalization of T-duality led us to discover a striking duality between triangulations of Amplituhedra of “m=2” type and the ones of a seemingly unrelated object – the Hypersimplex. The latter is a polytope which has been central in many contexts\, such as matroid theory\, torus orbits in the Grassmannian\, and tropical geometry. Based on joint works with Lauren Williams\, Melissa Sherman-Bennett\, Tomasz Lukowski [arXiv:2104.08254\, arXiv:2002.06164].
URL:https://cmsa.fas.harvard.edu/event/11-18-2021-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211111T142200
DTEND;TZID=America/New_York:20211111T152200
DTSTAMP:20260505T234735
CREATED:20240301T103744Z
LAST-MODIFIED:20240301T103744Z
UID:10002893-1636640520-1636644120@cmsa.fas.harvard.edu
SUMMARY:11/11/21 Interdisciplinary Science Seminar
DESCRIPTION:Title: The Kervaire conjecture and the minimal complexity of surfaces \nAbstract: We use topological methods to solve special cases of a fundamental problem in group theory\, the Kervaire conjecture.\nThe conjecture asserts that\, for any nontrivial group G and any element w in the free product G*Z\, the quotient (G*Z)/<<w>> is still nontrivial. We interpret this as a problem of estimating the minimal complexity (in terms of Euler characteristic) of surfaces in HNN extensions. This gives a conceptually simple proof of Klyachko’s theorem that confirms the Kervaire conjecture for any G torsion-free. I will also explain new results obtained using this approach.
URL:https://cmsa.fas.harvard.edu/event/11-11-21-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-11.11.21.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211104T184600
DTEND;TZID=America/New_York:20211104T204600
DTSTAMP:20260505T234735
CREATED:20240214T080650Z
LAST-MODIFIED:20240301T103919Z
UID:10002580-1636051560-1636058760@cmsa.fas.harvard.edu
SUMMARY:11/4/21 CMSA Interdisciplinary Science Seminar
DESCRIPTION:Title: Exploring Invertibility in Image Processing and Restoration \nAbstract: Today’s smartphones have enabled numerous stunning visual effects from denoising to beautification\, and we can share high-quality JPEG images easily on the internet\, but it is still valuable for photographers and researchers to keep the original raw camera data for further post-processing (e.g.\, retouching) and analysis. However\, the huge size of raw data hinders its popularity in practice\, so can we almost perfectly restore the raw data from a compressed RGB image and thus avoid storing any raw data? This question leads us to design an invertible image signal processing pipeline. Then we further explore invertibility in other image processing and restoration tasks\, including image compression\, reversible image conversion (e.g.\, image-to-video conversion)\, and embedding novel views in a single JPEG image. We demonstrate that customized invertible neural networks are highly effective in these inherently non-invertible tasks.
URL:https://cmsa.fas.harvard.edu/event/11-4-21-cmsa-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-11.04.21-1583x2048-1-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211028T184500
DTEND;TZID=America/New_York:20211028T204500
DTSTAMP:20260505T234735
CREATED:20240301T104157Z
LAST-MODIFIED:20240305T104709Z
UID:10002894-1635446700-1635453900@cmsa.fas.harvard.edu
SUMMARY:ARCH: Know What Your Machine Doesn’t Know
DESCRIPTION:Speaker: Jie Yang\, Delft University of Technology \nTitle: ARCH: Know What Your Machine Doesn’t Know \nAbstract: Despite their impressive performance\, machine learning systems remain prohibitively unreliable in safety-\, trust-\, and ethically sensitive domains. Recent discussions in different sub-fields of AI have reached the consensus of knowledge need in machine learning; few discussions have touched upon the diagnosis of what knowledge is needed. In this talk\, I will present our ongoing work on ARCH\, a knowledge-driven\, human-centered\, and reasoning-based tool\, for diagnosing the unknowns of a machine learning system. ARCH leverages human intelligence to create domain knowledge required for a given task and to describe the internal behavior of a machine learning system; it infers the missing or incorrect knowledge of the system with the built-in probabilistic\, abductive reasoning engine. ARCH is a generic tool that can be applied to machine learning in different contexts. In the talk\, I will present several applications in which ARCH is currently being developed and tested\, including health\, finance\, and smart buildings.
URL:https://cmsa.fas.harvard.edu/event/10-28-2021-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211021T184400
DTEND;TZID=America/New_York:20211021T204400
DTSTAMP:20260505T234735
CREATED:20240214T082555Z
LAST-MODIFIED:20240301T104328Z
UID:10002587-1634841840-1634849040@cmsa.fas.harvard.edu
SUMMARY:10/21/2021 Interdisciplinary Science Seminar
DESCRIPTION:Title: Mathematical resolution of the Liouville conformal field theory. \nAbstract: The Liouville conformal field theory is a well-known beautiful quantum field theory in physics describing random surfaces. Only recently a mathematical approach based on a well-defined path integral to this theory has been proposed using probability by David\, Kupiainen\, Rhodes\, Vargas. \nMany works since the ’80s in theoretical physics (starting with Belavin-Polyakov-Zamolodchikov) tell us that conformal field theories in dimension 2 are in general « Integrable »\, the correlations functions are solutions of PDEs and can in principle be computed explicitely by using algebraic tools (vertex operator algebras\, representations of Virasoro algebras\, the theory of conformal blocks). However\, for Liouville Theory this was not done at the mathematical level by algebraic methods. \nI’ll explain how to combine probabilistic\, analytic and geometric tools to give explicit (although complicated) expressions for all the correlation functions on all Riemann surfaces in terms of certain holomorphic functions of the moduli parameters called conformal blocks\, and of the structure constant (3-point function on the sphere). This gives a concrete mathematical proof of the so-called conformal bootstrap and of Segal’s gluing axioms for this CFT. The idea is to break the path integral on a closed surface into path integrals on pairs of pants and reduce all correlation functions to the 3-point correlation function on the Riemann sphere $S^2$. This amounts in particular to prove a spectral resolution of a certain operator acting on $L^2(H^{-s}(S^1))$ where $H^{-s}(S^1)$ is the Sobolev space of order -s<0 equipped with a Gaussian measure\, which is viewed as the space of fields\, and to construct a certain representation of the Virasoro algebra into unbounded operators acting on this Hilbert space. \nThis is joint work with A. Kupiainen\, R. Rhodes and V. Vargas.
URL:https://cmsa.fas.harvard.edu/event/10-21-2021-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211014T090000
DTEND;TZID=America/New_York:20211014T100000
DTSTAMP:20260505T234735
CREATED:20240214T082843Z
LAST-MODIFIED:20240529T180858Z
UID:10002588-1634202000-1634205600@cmsa.fas.harvard.edu
SUMMARY:D3C: Reducing the Price of Anarchy in Multi-Agent Learning
DESCRIPTION:Speaker: Ian Gemp\, DeepMind \nTitle: D3C: Reducing the Price of Anarchy in Multi-Agent Learning \nAbstract: In multi-agent systems the complex interaction of fixed incentives can lead agents to outcomes that are poor (inefficient) not only for the group but also for each individual agent. Price of anarchy is a technical game theoretic definition introduced to quantify the inefficiency arising in these scenarios– it compares the welfare that can be achieved through perfect coordination against that achieved by self-interested agents at a Nash equilibrium. We derive a differentiable upper bound on a price of anarchy that agents can cheaply estimate during learning. Equipped with this estimator agents can adjust their incentives in a way that improves the efficiency incurred at a Nash equilibrium. Agents adjust their incentives by learning to mix their reward (equiv. negative loss) with that of other agents by following the gradient of our derived upper bound. We refer to this approach as D3C. In the case where agent incentives are differentiable D3C resembles the celebrated Win-Stay Lose-Shift strategy from behavioral game theory thereby establishing a connection between the global goal of maximum welfare and an established agent-centric learning rule. In the non-differentiable setting as is common in multiagent reinforcement learning we show the upper bound can be reduced via evolutionary strategies until a compromise is reached in a distributed fashion. We demonstrate that D3C improves outcomes for each agent and the group as a whole on several social dilemmas including a traffic network exhibiting Braess’s paradox a prisoner’s dilemma and several reinforcement learning domains.
URL:https://cmsa.fas.harvard.edu/event/10-14-2021-interdisciplinary-science-seminar/
LOCATION:Virtual
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-10.14.21.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211007T184100
DTEND;TZID=America/New_York:20211007T184100
DTSTAMP:20260505T234735
CREATED:20240214T083403Z
LAST-MODIFIED:20240214T083403Z
UID:10002590-1633632060-1633632060@cmsa.fas.harvard.edu
SUMMARY:10/7/2021 Interdisciplinary Science Seminar
DESCRIPTION:Title: SiRNA Targeting TCRb: A Proposed Therapy for the Treatment of Autoimmunity \nAbstract: As of 2018\, the United States National Institutes of Health estimate that over half a billion people worldwide are affected by autoimmune disorders. Though these conditions are prevalent\, treatment options remain relatively poor\, relying primarily on various forms of immunosuppression which carry potentially severe side effects and often lose effectiveness over time. Given this\, new forms of therapy are needed. To this end\, we have developed methods for the creation of small-interfering RNA (siRNA) for hypervariable regions of the T-cell receptor β-chain gene (TCRb) as a highly targeted\, novel means of therapy for the treatment of autoimmune disorders. \nThis talk will review the general mechanism by which autoimmune diseases occur and discuss the pros and cons of conventional pharmaceutical therapies as they pertain to autoimmune disease treatment. I will then examine the rational and design methodology for the proposed siRNA therapy and how it contrasts with contemporary methods for the treatment of these conditions. Additionally\, the talk will compare the efficacy of multiple design strategies for such molecules by comparison over several metrics and discuss how this will be guiding future research.
URL:https://cmsa.fas.harvard.edu/event/10-7-2021-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210923T184000
DTEND;TZID=America/New_York:20210923T204000
DTSTAMP:20260505T234735
CREATED:20240214T083814Z
LAST-MODIFIED:20240301T104847Z
UID:10002591-1632422400-1632429600@cmsa.fas.harvard.edu
SUMMARY:9/23/2021 Interdisciplinary Science Seminar
DESCRIPTION:Title: The number of n-queens configurations \nAbstract: The n-queens problem is to determine Q(n)\, the number of ways to place n mutually non-threatening queens on an n x n board. The problem has a storied history and was studied by such eminent mathematicians as Gauss and Polya. The problem has also found applications in fields such as algorithm design and circuit development. \nDespite much study\, until recently very little was known regarding the asymptotics of Q(n). We apply modern methods from probabilistic combinatorics to reduce understanding Q(n) to the study of a particular infinite-dimensional convex optimization problem. The chief implication is that (in an appropriate sense) for a~1.94\, Q(n) is approximately (ne^(-a))^n. Furthermore\, our methods allow us to study the typical “shape” of n-queens configurations.
URL:https://cmsa.fas.harvard.edu/event/9-23-2021-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
END:VEVENT
END:VCALENDAR