CMSA

2017 Big Data Conference
2017-08-18 15:47 - 2017-08-19 15:47 (America/New_York) | Harvard Science Center, 1 Oxford Street, Cambridge, MA, 02138
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.
The 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.
Confirmed Speakers:

Mohammad Akbarpour, Stanford University
Albert-László Barabási, Northeastern University
Noureddine El Karoui, University of California, Berkeley
Ravi Jagadeesan, Harvard University
Lucas Janson, Harvard University
Tracy Ke, University of Chicago
Tze Leung Lai, Stanford University
Annie Liang, University of Pennsylvania
Marena Lin, Harvard University
Nikhil Naik, Harvard University
Alex Peysakhovich, Facebook
Natesh Pillai, Harvard University
Jann Spiess, Harvard University
Bradly Stadie, Open AI, University of California, Berkeley
Zak Stone, Google
Hau-Tieng Wu, University of Toronto
Sifan Zhou, Xiamen University

Following the conference, there will be a two-day workshop from August 20-21. The workshop is organized by Scott Kominers, and will feature:

Jörn Boehnke, Harvard University
Nikhil Naik, Harvard University
Bradly Stadie, Open AI, University of California, Berkeley

Conference Schedule
A PDF version of the schedule below can also be downloaded here.
August 18, Friday (Full day)

Time
Speaker
Topic

8:30 am – 9:00 am

Breakfast

9:00 am – 9:40 am
Mohammad Akbarpour
Video
Title: Information aggregation in overlapping generations and the emergence of experts
Abstract: 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).

9:40 am – 10:20 am
Lucas Janson
Video
Title: Model-Free Knockoffs For High-Dimensional Controlled Variable Selection
Abstract: 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.
Slides

10:20 am – 10:50 am

Break

10:50 pm – 11:30 pm
Noureddine El Karoui
Video
Title: Random matrices and high-dimensional statistics: beyond covariance matrices
Abstract: 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.

11:30 am – 12:10 pm
Nikhil Naik
Video
Title: Understanding Urban Change with Computer Vision and Street-level Imagery
Abstract: 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.
(Joint work with Edward L. Glaeser, Cesar A. Hidalgo, Scott Duke Kominers, and Ramesh Raskar.)

12:10 pm – 12:25 pm
Video #1
Video #2
Data Science Lightning Talks

12:25 pm – 1:30 pm

Lunch

1:30 pm – 2:10 pm
Tracy Ke
Video
Title: A new SVD approach to optimal topic estimation
Abstract: 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.
We 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.
For 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.

2:10 pm – 2:50 pm
Albert-László Barabási
Video
Title: Taming Complexity: From Network Science to Controlling Networks
Abstract: 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.

2:50 pm – 3:20 pm

Break

3:20 pm – 4:00 pm
Marena Lin
Video
Title: Optimizing climate variables for human impact studies
Abstract: 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.

4:00 pm – 4:40 pm
Alex Peysakhovich
Title: Building a cooperator
Abstract: 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.

4:40 pm – 5:20 pm
Tze Leung Lai
Video
Title: Gradient boosting: Its role in big data analytics, underlying mathematical theory, and recent refinements
Abstract: 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.

August 19, Saturday (Full day)

Time
Speaker
Topic

8:30 am – 9:00 am

Breakfast

9:00 am – 9:40 am
Natesh Pillai
Video
Title: Accelerating MCMC algorithms for Computationally Intensive Models via Local Approximations
Abstract: 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.

9:40 am – 10:20 am
Ravi Jagadeesan
Video
Title: Designs for estimating the treatment effect in networks with interference
Abstract: 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.

10:20 am – 10:50 am

Break

10:50 am – 11:30 am
Annie Liang
Video
Title: The Theory is Predictive, but is it Complete? An Application to Human Generation of Randomness
Abstract: 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).

11:30 am – 12:10 pm
Zak Stone
Video
Title: TensorFlow: Machine Learning for Everyone
Abstract: 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.

12:10 pm – 1:30 pm

Lunch

1:30 pm – 2:10 pm
Jann Spiess
Video
Title: (Machine) Learning to Control in Experiments
Abstract: 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.

2:10 pm – 2:50 pm
Bradly Stadie
Title: Learning to Learn Quickly: One-Shot Imitation and Meta Learning
Abstract: Many reinforcement learning algorithms are bottlenecked by data collection costs and the brittleness of their solutions when faced with novel scenarios.
We 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.

2:50 pm – 3:20 pm

Break

3:20 pm – 4:00 pm
Hau-Tieng Wu
Video
Title: When Medical Challenges Meet Modern Data Science
Abstract: 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.

4:00 pm – 4:40 pm
Sifan Zhou
Video
Title: Citing People Like Me: Homophily, Knowledge Spillovers, and Continuing a Career in Science
Abstract: 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.

To view photos and video interviews from the conference, please visit the CMSA blog.

Big Data,CMSA,Harvard,Math
Events,Past Events

A Celebration of Symplectic Geometry: 15 Years of JSG
2017-06-05 09:00 - 2017-06-06 17:00 | 20 Garden Street, Cambridge, MA 02138, MA, MA, 02138, United States
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.
Confirmed speakers:

Roger Casals, MIT
Chen He, Northeastern University
Yael Karshon, University of Toronto
Ailsa Keating, Institute of Advanced Study
Eckhard Meinrenken, University of Toronto
Ana Rita Pires, Fordham University
Sobhan Seyfaddini, Institute of Advanced Study
Alejandro Uribe, University of Michigan
Jonathan Weitsman, Northeastern University

The conference is co-organized by Denis Auroux and Victor Guillemin. Additional information on the conference will be announced closer to the event.
Schedule:
June 5, Monday (Full day)

Time
Speaker
Topic

8:30am – 9:0am

Breakfast

9:00am – 10:00am
Jonathan Weitsman
Title: On the geometric quantization of (some) Poisson manifolds

10:30am – 11:30am
Eckhard Meinrenken
Title: On Hamiltonian loop group spaces
Abstract: 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.

11:30am – 1:30pm

Break

1:30pm – 2:30pm
Ana Rita Pires
Title: Infinite staircases in symplectic embedding problems
Abstract: 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.
Video

3:00pm – 4:00pm
Sobhan Seyfaddini
Title: Rigidity of conjugacy classes in groups of area-preserving homeomorphisms
Abstract: 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.

4:30pm – 5:30pm
Roger Casals
Title: Differential Algebra of Cubic Graphs
Abstract: 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

June 6, Tuesday (Full day)

Time
Speaker
Topic

8:30am – 9:00am

Breakfast

9:00am – 10:00am
Alejandro Uribe
Title: Semi-classical wave functions associated with isotropic submanifolds of phase space
Abstract: 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.
Video

10:30am – 11:30am
Alisa Keating
Title: Symplectomorphisms of exotic discs
Abstract: 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.
Video

11:30am – 1:30pm

Break

1:30pm – 2:30pm
Chen He
Title: Morse theory on b-symplectic manifolds
Abstract: 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.
Video

3:00pm – 4:00pm
Yael Karshon
Title: Geometric quantization with metaplectic-c structures
Abstract: 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.
Video

5/23/2017 CMSA Special Seminar
2017-05-23 13:32 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States

5-3-2017 Random Matrix & Probability Theory Seminar
2017-05-03 13:29 | MA

5-2-2017 Social Sciences Application Forum
2017-05-02 13:30 | MA

Working Conference on Covariance Analysis in Biology, May 1-4, 2017
2017-05-01 09:00 - 2017-05-02 17:00 | 20 Garden Street, Cambridge, MA 02138, MA, MA, 02138, United States
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.
This 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.
Speakers:
Orr Ashenberg, Fred Hutchinson Cancer Research Center
John Barton, Massachusetts Institute of Technology
Simona Cocco, Laboratoire de Physique Statistique de l’ENS
Sean Eddy, Harvard University
Efthimios Kaxiras, Harvard University

Michael Laub, Massachusetts Institute of Technology
Debora S. Marks, Harvard University

Govind Menon, Brown University
Rémi Monasson, Laboratoire de Physique Théorique de l’ENS
Andrew Murray, Harvard University
Ilya Nemenman, Emory College

Chris Sander, Dana-Farber Cancer Institute, Harvard Medical School

Dave Thirumalai, University of Texas at Austin
Martin Weigt, IBPS, Université Pierre et Marie Curie
Matthieu Wyart, EPFL
More speakers will be confirmed soon.

Schedule:
(Please click here for a downloadable version of the schedule.)
Please note that the schedule for both days is currently tentative and is subject to change.
May 1, Monday

Time
Speaker
Topic

9:00-10:00am
Sean Eddy
TBA

10:00-11:00am
Mike Laub
TBA

11:00am-12:00pm
Ilya Nemenman
TBA

May 2, Tuesday

Time
Speaker
Topic

9:00-10:00am
Orr Ashenberg
TBA

10:00-11:00am
Debora Marks
TBA

11:00am-12:00pm
Martin Weigt
TBA

4:30pm-5:30pm
Simona Cocco
CMSA Colloquia

May 3, Wednesday

Time
Speaker
Topic

9:00-10:00am
Andrew Murray
TBA

10:00-11:00am
Matthieu Wyart
TBA

11:00am-12:00pm
Rémi Monasson
TBA

May 4, Thursday

Time
Speaker
Topic

9:00-10:00am
David Thirumalai
TBA

10:00-11:00am
Chris Sander
TBA

11:00am-12:00pm
John Barton
TBA

Organizers:

Michael Brenner, Lucy Colwell, Elena Rivas, Eugene Shakhnovich

* This event is sponsored by CMSA Harvard University.

Past Events

JDG 2017 Conference
2017-04-28 09:00 - 2017-05-02 17:00 | Harvard Science Center, 1 Oxford Street, Cambridge, MA, 02138
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.
Confirmed Speakers

Mina Aganagic, UC Berkeley
Denis Auroux, UC Berkeley
Caucher Birkar, University of Cambridge
Huai-Dong Cao, Lehigh University
Tristan Collins, Harvard University
Camillo De Lellis, ETH Zurich
Jean-Pierre Demailly, Grenoble Alpes University
Simon Donaldson, Stony Brook University
Dan Freed, University of Texas at Austin
Kenji Fukaya, Stony Brook University
David Gabai, Princeton University
Larry Guth, Massachusetts Institute of Technology
Richard Hamilton, Columbia University
Yujiro Kawamata, University of Tokyo
Frances Kirwan, Oxford University
Blaine Lawson, Stony Brook University
Jun Li, Stanford University
Si Li, Tsinghua University
Bong Lian, Brandeis University
Chiu-Chu Melissa Liu, Columbia University
Ciprian Manolescu, University of California, Los Angeles
Fernando Marques, Princeton University
William Meeks, University of Massachusetts Amherst
William Minicozzi, Massachusetts Institute of Technology
John Pardon, Princeton University
Duong Phong, Columbia University
Alena Pirutka, Courant Institute of New York University
Richard Schoen, University of California, Irvine
Artan Sheshmani, QGM Aarhus University/Harvard University
Cliff Taubes, Harvard University
Cumrun Vafa, Harvard University
Mu-Tao Wang, Columbia University
Shing-Tung Yau, Harvard University
Steve Zelditch, Northwestern University

* This event is co-sponsored by Lehigh University and partially supported by the National Science Foundation.

4-27-2017 CMSA Special Seminar
2017-04-27 13:21 | MA

4-24-2017 Mathematical Physics Seminar
2017-04-24 13:31 | MA

Workshop on Quantum Information
2017-04-23 09:00 - 2017-04-24 17:00 | 20 Garden Street, Cambridge, MA 02138, MA, MA, 02138, United States
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.
The following speakers are confirmed:

Fernando G.S.L Brandão (CalTech)
Jacob Biamonte (Skoltech)
Isaac Chuang (MIT)
Iris Cong (Harvard)
Aram Harrow (MIT)
Ke Li (HIT)
Mikhail D. Lukin (Harvard)
Shunlong Luo (AMSS)
Renato Renner (ETH Zürich)
Peter Shor (MIT)

4-19-2017 Random Matrix & Probability Theory Seminar
2017-04-19 13:22 | MA

4-18-2017 Social Science Applications Forum
2017-04-18 11:00 - 12:00 | CMSA, 20 Garden Street, Cambridge, MA, 02138, United States

4-17-2017 Mathematical Physics Seminar
2017-04-17 13:14 | MA

4-14-2017 Special Lecture Series
2017-04-14 12:46 | MA

4-12-2017 Social Science Applications Forum
2017-04-12 12:53 | MA

4-12-2017 Special Lecture Series
2017-04-12 12:44 | MA

04-12-2017 Random Matrix & Probability Theory Seminar
2017-04-12 11:56 | MA

4-11-2017 Social Science Applications Forum
2017-04-11 13:00 | MA

4-10-2017 Mathematical Physics Seminar
2017-04-10 13:07 | MA

4-7-2017 Special Lecture Series
2017-04-07 12:43 | MA

4-6-2017 CMSA Special Seminar
2017-04-06 12:51 | MA

4-5-2017 Special Lecture Series
2017-04-05 12:42 | MA

4-5-2017 Random Matrix & Probability Theory Seminar
2017-04-05 12:31 | MA

4-3-2017 Mathematical Physics Seminar
2017-04-03 12:50 | MA

3-30-2017 CMSA Special Seminar
2017-03-30 12:38 | MA

3-29-2017 Random Matrix & Probability Theory Seminar
2017-03-29 12:35 | MA

Working Conference on Materials and Data Analysis, March 27-30, 2017
2017-03-27 15:34 - 2017-03-30 15:34 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
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.
Photos of the event can be found on CMSA’s Blog.
Participants:

Ryan P. Adams, Harvard University
Jörg Behler, University of Göttingen
Kieron Burke, University of California, Irvine
Lucy Colwell, University of Cambridge
Gábor Csányi, University of Cambridge
Ekin Doğuş Çubuk, Stanford University
Leslie Greengard, Courant Institute of Mathematical Sciences, New York University
Petros Koumoutsakos, Radcliffe Institute for Advanced Study, Harvard University
Govind Menon, Brown University
Evan Reed, Stanford University
Patrick Riley, Google
Matthias Rupp, Fitz Haber Institute of the Max Planck Society
Sadasivan Shankar, Harvard University
Dennis Sheberla, Harvard University

Organizers:

Michael Brenner, Efthimios Kaxiras

* This event is sponsored by CMSA Harvard University.

Schedule:

Monday, March 27

Time
Speaker
Title

8:30am – 9:00am
Breakfast

9:00am – 10:00am
Kieron Burke, University of California, Irvine
Background in DFT and electronic structure calculations

10:00am – 11:00am
Kieron Burke, University of California, Irvine

The density functionals machines can learn

11:00am – 12:00pm
Sadasivan Shankar, Harvard University
A few key principles for applying Machine Learning to Materials (or Complex Systems) — Scientific and Engineering Perspectives

Tuesday, March 28

Time
Speaker
Title

8:30am – 9:00am
Breakfast

9:00am – 10:00am
Ryan Adams, Harvard
TBA

10:00am – 11:00am
Gábor Csányi, University of Cambridge

Interatomic potentials using machine learning: accuracy, transferability and chemical diversity

11:00am – 1:00pm
Lunch Break

1:00pm – 2:00pm
Evan Reed, Stanford University
TBA

Wednesday, March 29

Time
Speaker
Title

8:30am – 9:00am
Breakfast

9:00am – 10:00am
Patrick Riley, Google
The Message Passing Neural Network framework and its application to molecular property prediction

10:00am – 11:00am
Jörg Behler, University of Göttingen
TBA

11:00am – 12:00pm
Ekin Doğuş Çubuk, Stanford Univers
TBA

4:00pm
Leslie Greengard, Courant Institute
Inverse problems in acoustic scattering and cryo-electron microscopy
CMSA Colloquium

Thursday, March 30

Time
Speaker
Title

8:30am – 9:00am
Breakfast

9:00am – 10:00am
Matthias Rupp, Fitz Haber Institute of the Max Planck Society
TBA

10:00am – 11:00am
Petros Koumoutsakos, Radcliffe Institute for Advanced Study, Harvard
TBA

11:00am – 1:00pm
Lunch Break

1:00pm – 2:00pm
Dennis Sheberla, Harvard University
Rapid discovery of functional molecules by a high-throughput virtual screening

Events, Past Events

03-27-2017 Mathematical Physics Seminar
2017-03-27 12:40 | MA

3-24-2017 Random Matrix & Probability Theory Seminar
2017-03-24 10:00 - 11:00 | CMSA, 20 Garden Street, Cambridge, MA, 02138, United States

3-22-2017 Random Matrix & Probability Theory Seminar
2017-03-22 12:01 | MA
