Seminars

The MIT Statistics and Data Science Center hosts guest lecturers from around the world in the weekly Statistics and Data Science seminar series (formerly the Stochastics and Statistics Seminars). Subscribe to receive email notifications about upcoming seminars.

Spring 2025

Feb 7 – Rajarshi Mukherjee, Harvard University
Inference for ATE & GLM’s when p/n→δ∈(0,∞)

Feb 14 – No Seminar

Feb 21 – David Alvarez-Melis, Harvard University
Towards a ‘Chemistry of AI’: Unveiling the Structure of Training Data for more Scalable and Robust Machine Learning

Feb 28 – Ashia Wilson, MIT
Two Approaches Towards Adaptive Optimization 

Mar 7 – Krishna Balasubramanian, University of California – Davis
Finite-Particle Convergence Rates for Stein Variational Gradient Descent

Mar 14 – Murat A. Erdogdu, University of Toronto

Mar 21 – Claire Donnat, University of Chicago

Mar 28 – No Seminar

Apr 4 – Jessica Hullman, Northwestern University
The value of information in model assisted decision-making

Apr 11 – Jann Spiess, Stanford University

Apr 18 – Dennis Shen, University of Southern California

Apr 25 – Richard Samworth, Unviersity of Cambridge
How should we do linear regression?

May 2 – Aaron Roth, University of Pennsylvania

May 9 – Sivaraman Balakrishnan, Carnegie Mellon University

Spring 2024

Feb 9 – Christian Wolf (MIT)
Empirical methods for macroeconomic policy analysis

Feb 16 – Pravesh Kothari (Princeton University)
Efficient Algorithms for Semirandom Planted CSPs at the Refutation Threshold

Feb 23 – Kengo Kato (Cornell University)
Entropic optimal transport: limit theorems and algorithms

Mar 1 – No Seminar

Mar 8 – Joan Bruna (New York University)
On Provably Learning Sparse High-Dimensional Functions

Mar 15 – Vitaly Feldman (Apple ML Research)
Efficient Algorithms for Locally Private Estimation with Optimal Accuracy Guarantees

Mar 22 – Mark Sellke – (Harvard University)
Confinement of Unimodal Probability Distributions and an FKG-Gaussian Correlation Inequality

Mar 29 – No Seminar

Apr 5 – Edward Kennedy – (Carnegie Mellon University)
Optimal nonparametric capture-recapture methods for estimating population size

Apr 12 – No Seminar

Apr 19 – Vinod Vaikuntanathan (MIT)
Lattices and the Hardness of Statistical Problems

Apr 26 – Reza Gheissari (Northwestern University)
Emergent outlier subspaces in high-dimensional stochastic gradient descent

May 3 – Franca Hoffmann (California Institute of Technology)
Consensus-based optimization and sampling

May 10 – Yair Shenfeld (Brown University)
Matrix displacement convexity and intrinsic dimensionality

May 17 – Gabriele Farina (MIT)
Adversarial combinatorial bandits for imperfect-information sequential games

Fall 2023

Sep 8 – Alex Wein (University of California, Davis)
Fine-Grained Extensions of the Low-Degree Testing Framework

Sep 15 – Vasilis Syrgkanis (Stanford University)
Source Condition Double Robust Inference on Functionals of Inverse Problems

Sep 29 – Vladimir Spokoinyi (Humboldt University of Berlin)
Estimation and Inference for Error-in-Operator Model

Oct 6 – Nikita Zhivotovskiy (University of California, Berkeley)
Sharper Risk Bounds for Statistical Aggregation

Oct 13 – Emmanuel Abbé (EPFL)
A Proof of the RM Code Capacity Conjecture

Oct 20 – Sam Hopkins (MIT)
The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination

Oct 27 – Stephen Bates (MIT)
Hypothesis Testing with Information Asymmetry

Nov 3 – Anna Gilbert (Yale University)
Project and Forget: Solving Large-Scale Metric Constrained Problems

Nov 17 – Jianfeng Lu (Duke University
Analysis of Flow-based Generative Models

Dec 1 – Lester Mackey (Microsoft Research)
Advances in Distribution Compression

Dec 8 – Nicolas Flammarion (EPFL)
Saddle-to-saddle Dynamics in Diagonal Linear Networks

Dec 15 – Zaid Harchaoui (University of Washington)
The Discrete Schrödinger Bridge, and the Ensuing Chaos

Spring 2023

Feb 17 – Eric Vanden-Eijnden (New York University)
Generative Models, Normalizing Flows and Monte Carlo Samplers

Feb 24 – Andrej Risteski – (Carnegie Mellon University)
On the statistical cost of score matching

Mar 3 – Tim Kunisky (Yale University)
Spectral pseudorandomness and the clique number of the Paley graph

Mar 10 – Kuikui Liu (University of Washington)
Spectral Independence: A New Tool to Analyze Markov Chains

Mar 17 – Paromita Dubey (University of Southern California)
Geometric EDA for Random Objects

Mar 24 – Martin Wainwright (MIT)
Variational methods in reinforcement learning

Mar 31 – No Seminar

Apr 7 – Florian Gunsilius (University of Michigan)
Free Discontinuity Design (joint w/David van Dijcke)

Apr 14 – No Seminar

Apr 21 – Matias Cattaneo (Princeton University)
Adaptive Decision Tree Methods

Apr 28 – Samory Kpotufe (Columbia University)
Adaptivity in Domain Adaptation and Friends

May 5 – Vianney Perchet (Center for Research in Economics and Statistics, ENSAE Paris)
Learning learning-augmented algorithms.  The example of stochastics scheduling

May 12 – Jayadev Acharya (Cornell University)
Statistical Inference Under Information Constraints: User level approaches

Fall 2022

Sept 9       Yanjun Han (MIT)
                  Beyond UCB: statistical complexity and optimal algorithm for non-linear ridge        
                  bandits

Sept 16    Anette “Peko” Hosoi (MIT)
                 Short Stories About Data and Sports

Sept 30    Konstantin Tikhomirov (Georgia Institute of Technology)
                 Regularized modified log-Sobolev inequalities, and comparison of Markov chains

Oct 7         Jiaoyang Huang (University of Pennsylvania)
                  Efficient derivative-free Bayesian inference for large-scale inverse problems

Oct 14      *POSTPONED* Paromita Dubey (University of Southern California)
                  Geometric EDA for Random Objects

Oct 21       Zhou Fan (Yale University)
                 Maximum likelihood for high-noise group orbit estimation and cryo-EM

Oct 28      Ahmed El Alaoui (Cornell University)
                 Sampling from the SK measure via algorithmic stochastic localization

Nov 4       Marco Mondelli (Institute of Science and Technology Austria)
                 Inference in High Dimensions for (Mixed) Generalized Linear Models: the Linear, the
                 Spectral and the Approximate

Nov 18     Julia Palacios (Stanford University)
                 Distance-based summaries and modeling of evolutionary trees

Dec 2       Jaouad Mourtada (ENSAE Paris)
                 Coding convex bodies under Gaussian noise, and the Wills functional

Dec 9       *POSTPONED* Gérard Ben Arous (NYU Courant)
                 High-dimensional limit theorems for Stochastic Gradient Descent: effective dynamics
                 and critical scaling

Spring 2022

Feb 4th – Dan Mikulincer, (MIT)
The Brownian transport map

Feb 18th – Ilias Zadik, (MIT)
On the power of Lenstra-Lenstra-Lovasz in noiseless inference

Feb 25th – *POSTPONED* Yue Lu, (Harvard University)

Mar 4th – Edgar Dobriban, (University of Pennsylvania)
Optimal Testing for Calibration of Predictive Models

Mar 11th – Isaiah Andrews, (Harvard University)
Inference on Winners

Mar 18th – Subhabrata Sen (Harvard University)
Mean-field approximations for high-dimensional Bayesian Regression

Apr 8th – Li-Yang Tan, (Stanford University)
The query complexity of certification

April 15th – Caroline Uhler, (MIT)

Apr 22nd – Yue M. Lu, (Harvard University)
Learning with Random Features and Kernels: Sharp Asymptotics and Universality Laws

Apr 29th – Giedre Lideikyte-Huber and Marta Pittavino (University of Geneva)
Is quantile regression a suitable method to understand tax incentives for charitable giving? Case study from the Canton of Geneva, Switzerland

May 6th – Jonathan Weare (New York University)
Sampling rare events in Earth and planetary science

Fall 2021

Sept 17       Lorenzo Rosasco (MIT/Universita’ di Genova) –
                    Interpolation and Learning with Scale Dependent Kernels

Sept 24       Boaz Barak (Harvard University)
                    Representation and generalization 

Oct 1           Devavrat Shah (MIT)
                    Causal Matrix Completion

Oct 8           Yihong Wu (Yale University)
                    Recent Results in Planted Assignment Problems

Oct 15         Yuting Wei (Wharton School at the University of Pennsyvania)
                     Breaking the Sample Size Barrier in Reinforcement Learning

Oct 22         Kevin Jamieson (University of Washington)
                     Instance Dependent PAC Bounds for Bandits and Reinforcement Learning

Oct 29         Ronen Eldan (Weizmann Institute of Science/Princeton University)
                    Revealing the Simplicity of High-Dimensional Objects via Pathwise Analysis

Nov 5          Morgane Austern (Harvard University)
                   Asymptotics of learning on dependent and structured random objects

Nov 12        Cynthia Rush (Columbia University)
                    Characterizing the Type 1-Type 2 Error Trade-off for SLOPE

Nov 19        Pragya Sur (Harvard)
                    Precise high-dimensional asymptotics for AdaBoost via max-margins & min-norm
                    interpolants

Dec 3          Jesse Thaler (MIT)
                   The Geometry of Particle Collisions: Hidden in Plain Sight

Spring 2021

Feb 19        Jerry Li (Microsoft Research) – Faster and Simpler Algorithms for List Learning

Feb 26        Yury Polyanskiy (MIT) – Self-regularizing Property of Nonparametric
                    Maximum Likelihood Estimator in Mixture Models

Mar 5        Bhaswar B. Bhattacharya (University of Pennsylvania – Wharton School) –
                   Detection Thresholds for Distribution-Free Non-Parametric Tests:
                    The Curious Case of Dimension 8

Mar 12       James Robins (Harvard) – On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

Mar 19      Daniel Roy (University of Toronto) – Relaxing the I.I.D. Assumption: Adaptively Minimax Optimal Regret via Root-Entropic Regularization

Mar 26      Vladimir Vovk (Royal Holloway, University of London) – Testing the I.I.D. assumption online

Apr 2        Thibaut Le Gouic (MIT) – Sampler for the Wasserstein barycenter

Apr 9        Suriya Gunasekar (Microsoft Research) – Functions space view of linear multi-channel convolution networks with bounded weight norm

Apr 16      Eric Laber (Duke University) – Sample size considerations in precision medicine

Apr 23      Hilary Finucane (Broad Institute) – Prioritizing genes from genome-wide association studies

May 14     Ann Lee (Carnegie Mellon University) – Likelihood-Free Frequentist Inference

 

Fall 2020

Sep 11	Gesine Reinert (University of Oxford) Stein’s method for multivariate continuous distributions and applications	online
Sep 18	Caroline Uhler (MIT) Causal Inference and Overparameterized Autoencoders in the Light of Drug Repurposing for SARS-CoV-2	online
Sep 25	Dylan Foster (MIT) Separating Estimation from Decision Making in Contextual Bandits	online
Oct 2	Richard Nickl (University of Cambridge) Bayesian inverse problems, Gaussian processes, and partial differential equations	online
Oct 9	Gábor Lugosi (Pompeu Fabra University) On Estimating the Mean of a Random Vector	online
Oct 16	Carola-Bibiane Schönlieb (University of Cambridge) Data driven variational models for solving inverse problems	online
Oct 23	Jose Blanchet (Stanford University) Statistical Aspects of Wasserstein Distributionally Robust Optimization Estimators	online
Oct 30	Alessandro Rinaldo (Carnegie Mellon University)	POSTPONED
Nov 6	Daniela Witten (University of Washington) Valid hypothesis testing after hierarchical clustering	online
Nov 13	Mary Wootters (Stanford University) Sharp Thresholds for Random Subspaces, and Applications	online
Nov 20	Arnaud Doucet (University of Oxford) Perfect Simulation for Feynman-Kac Models using Ensemble Rejection Sampling	online
Dec 4	Rong Ge (Duke University) A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Net	online

Spring 2020

 

Feb 7	Weijie Su (University of Pennsylvania) Gaussian Differential Privacy, with Applications to Deep Learning	E18-304
Feb 14	Xiaohui Chen (University of Illinois at Urbana-Champaign) Diffusion K-means Clustering on Manifolds: provable exact recovery via semidefinite relaxations	E18-304
Feb 21	Rina Foygel Barber (University of Chicago) Predictive Inference with the Jackknife+	E18-304
Feb 28	Kavita Ramanan (Brown University) Tales of Random Projections	E18-304
Mar 6	Suriya Gunasekar (Microsoft Research)	POSTPONED
Mar 13	Dan Spielman (Yale University)	POSTPONED
Mar 20	Hilary Finucane (Broad Institute of MIT)	POSTPONED
Apr 10	Alessandro Rinaldo (Carnegie Mellon University)	POSTPONED
Apr 10	Jonathan Niles-Weed (New York University) Matrix Concentration for Products	online
Apr 17	Ery Arias-Castro (University of California, San Diego) On the Estimation of Distances Using Graph Distances	online
Apr 24	Jon Wellner (University of Washington)	POSTPONED
Apr 24	Sebastien Bubeck (Microsoft Research)	online
May 1	Annie Liang (University of Pennsylvania)	POSTPONED
May 1	Alexandre d’Aspremont (ENS, CNRS) Naive Feature Selection: Sparsity in Naive Bayes	online
May 8	Gerard Ben Arous (New York University, Courant Institute)	POSTPONED

Fall 2019

IDS.190 – Topics in Bayesian Modeling and Computation

Slides related to this course are available for MIT students and faculty here.

Fall 2019

Sep 6	Tengyuan Liang (University of Chicago) GANs, Optimal Transport, and Implicit Density Estimation	E18-304
Sep 20	Samory Kpotufe (Columbia) Some New Insights On Transfer Learning	E18-304
Sep 27	Adam Klivans (UT Austin) Frontiers of Efficient Neural-Network Learnability	E18-304
Oct 11	Christopher Moore (Santa Fe Institute) The Planted Matching Problem	E18-304
Oct 18	Stanislav Minsker (USC) Towards Robust Statistical Learning Theory	E18-304
Oct 25	Maria-Pia Victoria-Feser (University of Geneva) Accurate Simulation-Based Parametric Inference in High Dimensional Settings	E18-304
Nov 8	Yudong Chen (Cornell) SDP Relaxation for Learning Discrete Structures: Optimal Rates, Hidden Integrality, and Semirandom Robustness	E18-304
Nov 15	Lenka Zdeborova (Institut de Physique Théorique, CNRS) Understanding Machine Learning with Statistical Physics	E18-304
Nov 22	Tamara Broderick (MIT) Automated Data Summarization for Scalability in Bayesian Inference	E18-304
Dec 6	Simon Tavaré (Columbia) Inferring the Evolutionary History of Tumors	E18-304

Spring 2019

Feb 1	Andrea Montanari (Stanford) Optimization of the Sherrington-Kirkpatrick Hamiltonian	32-141
Feb 8	Polina Golland (MIT CSAIL) Medical Image Imputation	E18-304
Feb 15	Zhou Fan (Yale) TAP free energy, spin glasses, and variational inference	E18-304
Feb 22	Nike Sun (MIT) Capacity lower bound for the Ising perceptron	E18-304
Mar 1	Eric Kolaczyk (Boston University) Why Aren’t Network Statistics Accompanied By Uncertainty Statements?	E18-304
Mar 8	Aditya Guntuboyina (UC Berkeley) Univariate total variation denoising, trend filtering and multivariate Hardy-Krause variation denoising	E18-304
Mar 15	Alex Belloni (Duke University) Subvector Inference in Partially Identified Models with Many Moment Inequalities	E18-304
Mar 22	Eliran Subag (New York University) Optimization of random polynomials on the sphere in the full-RSB regime	E18-304
Apr 12	Aaditya Ramdas (Carnegie Mellon University) Exponential line-crossing inequalities	E18-304
Apr 19	Dylan Foster (MIT) Logistic Regression: The Importance of Being Improper	E18-304
Apr 26	Chao Gao (University of Chicago) Robust Estimation: Optimal Rates, Computation and Adaptation	E18-304
May 3	Tracy Ke (Harvard) Optimal Adaptivity of Signed-Polygon Statistics for Network Testing (Tracy Ke, Harvard University)	E18-304
May 10	Will Perkins (University of Illinois at Chicago) Counting and sampling at low temperatures	E18-304

Fall 2018

Sep 7	Dejan Slepcev (MIT) Varational problems on random structures and their continuum limits	E18-304, 11am – 12pm
Sep 14	Gregory Wornell (MIT) An information-Geometric View of Learning in High Dimensions	E18-304, 11am – 12pm
Sep 21	Boaz Nadler (Weizmann Institute) Unsupervised Ensemble Learning	E18-304, 11am – 12pm
Sep 28	Jingbo Liu (MIT) Reverse hypercontractivity beats measure concentration for information theoretic converses	E18-304, 11am – 12pm
Oct 5	Tselil Schramm (Harvard University) Efficient Algorithms for the Graph Matching Problem in Correlated Random Graphs	E18-304, 11am – 12pm
Oct 12	John Duchi (Stanford University) Locally private estimation, learning, inference, and optimality	E18-304, 11am – 12pm
Oct 19	Aukosh Jagannath (Harvard) Algorithmic thresholds for tensor principle component analysis	E18-304, 11am – 12pm
Oct 26	Alan Frieze (Carnegie Mellon University) On the cover time of two classes of graph	E18-304, 11am – 12pm
Nov 2	Sumit Mukherjee (Columbia University) Joint estimation of parameters in Ising Model	E18-304, 11am – 12pm
Nov 9	Zongming Ma (University of Pennsylvania) Optimal hypothesis testing for stochastic block models with growing degrees	E18-304, 11am – 12pm
Nov 16	Lucas Janson (Harvard University) Model-X knockoffs for controlled variable selection in high dimensional nonlinear regression	E18-304, 11am – 12pm
Nov 30	Vladimir Koltchinskiib (Georgia Tech) Bias Reduction and Asymptotic Efficiency in Estimation of Smooth Functionals of High-Dimensional Covariance	E18-304, 11am – 12pm
Dec 7	Guy Breslerb (MIT) Reducibility and Computational Lower Bounds for Some High-dimensional Statistics Problems	E18-304, 11am – 12pm
Dec 14	Lutz Warnke (Georgia Tech) Large girth approximate Steiner triple systems	E18-304, 11am – 12pm

Spring 2018

Feb 2	Sahand Negahban (Yale) Connections between structured estimation and weak submodularity	E18-304, 11am – 12pm
Feb 9	Garvesh Raskutti (Wisconsin) Variable selection using presence-only data with applications to biochemistry	E18-304, 11am – 12pm
Feb 16	Arnak Dalalyan CREST (Paris) User-friendly guarantees for the Langevin Monte Carlo	E18-304, 11am – 12pm
Feb 23	Nathan Srebro-Bartom (TTI-Chicago) Optimization’s Implicit Gift to Learning: Understanding Optimization Bias as a Key to Generalization	E18-304, 11am – 12pm
Mar 2	Alexandra Carpentier (Potsdam) One and two sided composite-composite tests in Gaussian mixture models	E18-304, 11am – 12pm
Mar 9	Afonso Bandeira (NYU) Statistical estimation under group actions: The Sample Complexity of Multi-Reference Alignment	E18-304, 11am – 12pm
Mar 16	David Sontag (MIT) When Inference is tractable	E18-304, 11am – 12pm
Mar 23	Johannes Schmidt Hieber (Leiden) Statistical theory for deep neural networks with ReLU activation function	E18-304, 11am – 12pm
Apr 6	Jianqing Fan (Princeton) Optimality of Spectral Methods for Ranking, Community Detections and Beyond	E18-304, 11am – 12pm
Apr 13	Subrabatha Sen (Microsoft) Testing degree corrections in Stochastic Block Models	E18-304, 11am – 12pm
Apr 27	Genevera Allen (Rice) Inference, Computation, and Visualization for Convex Clustering and Biclustering	E18-304, 11am – 12pm
May 4	Ohad Shamir (Weizman) Size-Independent Sample Complexity of Neural Networks	E18-304, 11am – 12pm
May 11	Adel Javanmard (USC) Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions	E18-304, 11am – 12pm
May 25	Hariharan Narayanan (MIT/UW) Fitting a putative manifold to noisy data	E18-304, 11am – 12pm

Fall 2017

Sep 8	Andrej Risteski (Princeton University) New provable techniques for learning and inference in probabilistic graphical models	E18-304, 11am – 12pm
Sep 15	Yury Polyanskiy (MIT) Sample complexity of population recovery	E18-304, 11am – 12pm
Sep 22	Amir Dembo (Stanford University) Walking within growing domains: recurrence versus transience	E18-304, 11am – 12pm
Sep 29	Jelani Nelson (Harvard University) Optimal lower bounds for universal relation, and for samplers and finding duplicates in streams	E18-304, 11am – 12pm
Oct 6	Youssef Marzouk (MIT) Transport maps for Bayesian computation	E18-304, 11am – 12pm
Oct 13	Galen Reeves (Duke University) Additivity of Information in Deep Generative Networks: The I-MMSE Transform Method	E18-304, 11am – 12pm
Oct 19	John Cunningham (Columbia) Structure in multi-index tensor data: a trivial byproduct of simpler phenomena?	E18-304, 11am – 12pm
Oct 20	Sayan Mukherjee (Duke) Inference in dynamical systems and the geometry of learning group actions	E18-304, 11am – 12pm
Oct 27	Amit Daniely (Google) On Learning Theory and Neural Networks	E18-304, 11am – 12pm
Nov 1	Pierre Jacob (Harvard) Unbiased Markov chain Monte Carlo with couplings	E18-304, 11am – 12pm
Nov 3	Joan Bruna Estrach (NYU) Statistics, Computation and Learning with Graph Neural Networks	E18-304, 11am – 12pm
Nov 17	Alex Dimakis (University of Texas at Austin) Generative Models and Compressed Sensing	E18-304, 11am – 12pm
Dec 1	Susan Murphy (Harvard University) Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time	E18-304, 11am – 12pm
Dec 8	Alex Bloemendal (Broad Institute) Genome-wide association, phenotype prediction, and population structure: a review and some open problems	E18-304, 11am – 12pm

Spring 2017

Feb 3	Mayya Zhilova (Georgia Tech) Non-classical Berry-Esseen inequality and accuracy of the weighted bootstrap	E18-304, 11am – 12pm
Feb 10	Pierre Bellec (Rutgers) Slope meets Lasso in sparse linear regression	E18-304, 11am – 12pm
Feb 17	Frederick Eberhardt (CalTech) Causal Discovery in Systems with Feedback Cycles	E18-304, 11am – 12pm
Feb 24	Yihong Wu (Yale) Estimating the number of connected components of large graphs based on subgraph sampling	E18-304, 11am – 12pm
Mar 3	Alexander Barvinok (University of Michigan) Computing partition functions by interpolation	E18-304, 11am – 12pm
Mar 10	Ankur Moitra (MIT) Robust Statistics, Revisited	E18-304, 11am – 12pm
Mar 17	David Dunson (Duke) Probabilistic factorizations of big tables and networks	32-141, 11am – 12pm
Mar 24	Shankar Bhamidi (UNC) Jagers-Nerman stable age distribution theory, change point detection and power of two choices in evolving networks	E18-304, 11am – 12pm
Apr 7	David Steurer (Cornell) Sample-optimal inference, computational thresholds, and the methods of moments	E18-304, 11am – 12pm
Apr 14	Daniel Hsu (Columbia) Active learning with seed examples and search queries	E18-304, 11am – 12pm
Apr 28	Ronitt Rubinfeld (MIT) Testing properties of distributions over big domains	32-141, 11am – 12pm
May 5	Sebastien Roch (Wisconsin) Some related phase transitions in phylogenetics and social network analysis	E18-304, 11am – 12pm
May 12	Jonas Peters (University of Copenhagen) Invariance and Causality	E18-304, 11am – 12pm
May 19	Vianney Perchet (ENS Paris-Saclay) Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe	E18-304, 11am – 12pm

Fall 2016

Sep 9	Pierre Jacob (Harvard) Couplings of Particle Filters	E18-304, 11am – 12pm
Sep 16	Lorenzo Rosasco (University of Genoa) Less is more: optimal learning by subsampling and regularization	E18-304, 11am – 12pm
Sep 30	Bin Yu (UC Berkeley) Theory to gain insight and inform practice: re-run of IMS Rietz Lecture, 2016	E18-304, 11am – 12pm
Oct 7	Mark Rudelson (University of Michigan) Invertibility and Condition Number of Sparse Random Matrices	E18-304, 11am – 12pm
Oct 14	Mikhail Belkin (Ohio State University) Eigenvectors of Orthogonally Decomposable Functions and Applications	E18-304, 11am – 12pm
Oct 21	Arian Maleki (Columbia) On The Asymptotic Performance of fq-regularized Least Squares	E18-304, 11am – 12pm
Oct 28	Sourav Chatterjee (Stanford) Matrix estimation by Universal Singular Value Thresholding	E18-304, 11am – 12pm
Nov 4	Po-Ling Loh (University of Pennsylvania) Influence maximization in stochastic and adversarial settings	E18-304, 11am – 12pm
Nov 18	Liza Levina (University of Michigan) Interpretable prediction models for network-linked data	E18-304, 11am – 12pm
Dec 2	Elchanan Mossel (MIT) Shotgun Assembly of Graphs	E18-304, 11am – 12pm
Dec 16	Yash Deshpande (Microsoft Research) Sparse PCA via covariance thresholding	E18-304, 11am – 12pm

Spring 2016

Feb 5	Andrew Nobel (UNC) Large Average Submatrices of a Gaussian Random Matrix: Landscapes and Local Optima	32-123, 11am – 12pm
Feb 12	David Donoho (Stanford) Incremental Methods for Additive Convex Cost Optimization	32-123, 11am – 12pm
Feb 19	Asu Ozdaglar (MIT) Overcoming Overfitting with Algorithmic Stability	32-123, 11am – 12pm
Feb 26	John Lafferty (U Chicago) On Shape Constrained Estimation	E18-304, 11am – 12pm
Mar 4	Shivani Agarwal (Indian Institute of Science/Radcliffe) On Complex Supervised Learning Problems, and On Ranking and Choice Models	32-123, 11am – 12pm
Mar 18	Martin Wainwright (UC Berkeley) Pairwise Comparison Models for High-Dimensional Ranking	32-123, 11am – 12pm
Apr 1	Roberto Oliveira (IMPA) Sub-Gaussian Mean Estimators	32-123, 11am – 12pm
Apr 8	Tony Cai (U Penn) Confidence Intervals for High-Dimensional Linear Regression: Minimax Rates and Adaptivity	32-123, 11am – 12pm
Apr 15	Gabor Szekely (NSF) The Energy of Data	32-123, 11am – 12pm
Apr 22	Ryan Tibshirani (Carnegie Mellon) Recent Advances in Trend Filtering	32-123, 11am – 12pm
Apr 29	Victor Chernozhukov (MIT) Double Machine Learning: Improved Point and Interval Estimation of Treatment and Causal Parameters	32-123, 11am – 12pm
May 6	Rachel Ward (UT Austin) Extracting Governing Equations in Chaotic Systems From Highly Corrupted Data	32-123, 11am – 12pm
May 13	David Blei (Columbia) Scaling and Generalizing Variational Inference	32-123, 11am – 12pm

Fall 2015

Sep 11	Rob Freund (MIT Sloan) An Extended Frank-Wolfe Method with Application to Low-Rank Matrix Completion	32-141, 11am-12pm
Sep 18	Mustazee Rahman (MIT Mathematics) Independent sets, local algorithms and random regular graphs	32-141, 11am-12pm
Sep 25	Edo Airoldi (Harvard University) Some Fundamental Ideas for Causal Inference on Large Networks	32-141, 11am-12pm
Oct 2	Constantine Caramanis (University of Texas at Austin) Fast algorithms and (other) min-max optimal algorithms for mixed regression	32-141, 11am-12pm
Oct 9	Roman Vershynin (University of Michigan) Discovering hidden structures in complex networks	32-141, 11am-12pm
Oct 16	Stefan Wager (Stanford University) Causal Inference with Random Forests	32-141, 11am-12pm
Oct 23	Robert Nowak (University of Wisconsin) Ranking and Embedding From Pairwise Comparisons	32-141, 11am-12pm
Oct 30	Rina Foygel Barber (University of Chicago) MOCCA: a primal/dual algorithm for nonconvex composite functions with applications to CT imaging	32-141, 11am-12pm
Nov 6	Gábor Lugosi (Pompeu Fabra University) On a High-Dimensional Random Graph Process	32-141, 11am-12pm
Nov 13	Jun Liu (Harvard University) Expansion of biological pathways by integrative Genomics	32-141, 11am-12pm
Nov 20	James Robins (Harvard University) Minimax Estimation of Nonlinear Functionals with Higher Order Influence Functions: Results and Applications	32-141, 11am-12pm
Dec 4	Eric Tchetgen Tchetgen (Harvard University) Next Generation Missing Data Methodology	32-141, 11am-12pm
Dec 11	Peter Bartlett (UC Berkeley) Efficient Optimal Strategies for Universal Prediction	32-141, 11am-12pm

Spring 2015

Feb 6	Denis Chetverikov (UCLA) Central Limit Theorems and Bootstrap in High Dimensions	E62-450, 11am-12pm
Feb 13	Victor-Emmanuel Brunel (Yale) Random polytopes and estimation of convex bodies	E17-133, 11am-12pm
Feb 27	Nike Sun (MSR New England and MIT Mathematics) The exact k-SAT threshold for large k	E62-450, 11am-12pm
Apr 10	Moritz Hardt (IBM Almaden) How good is your model? Guilt-free interactive data analysis	E62-450, 11am-12pm
Apr 17	Vianney Perchet (Université Paris Diderot) From Bandits to Ethical Clinical Trials. Optimal Sample Size for Multi-Phases Problems	E62-450, 11am-12pm
Apr 24	Ankur Moitra (MIT CSAIL) Tensor Prediction, Rademacher Complexity and Random 3-XOR	E62-450, 11am-12pm
May 1	Han Liu (Princeton) Nonparametric Graph Estimation	E62-450, 11am-12pm
May 8	Lester Mackey (Stanford) Measuring Sample Quality with Stein’s Method	E62-450, 11am-12pm

Fall 2014

Aug 15	Lenka Zdeborova (CEA) Clustering of sparse networks: Phase transitions and optimal algorithms	E62-587, 3:15pm-4:15pm
Aug 15	Florent Krzakala (Université Pierre et Marie) Superposition codes and approximate-message-passing decoder	E62-587, 2pm-3pm
Sep 23	Richard Nickl (University of Cambridge) Uncertainty quantification and confidence sets in high-dimensional models	E62-587, 12pm – 1pm
Oct 10	Vladimir Koltchinskii (Georgia Tech) Asymptotics and concentration for sample covariance	E62-650, 11am – 12pm
Oct 24	Anna Mikusheva (MIT Economics) A Geometric Approach to Weakly Identified Econometric Models	E62-687, 11am – 12pm
Oct 31	Yuan Liao (University of Maryland) High Dimensional Covariance Matrix Estimations and Factor Models	E62-587, 11am-12pm
Nov 7	Constantinos Daskalakis (MIT EECS) Beyond Berry Esseen: Structure and Learning of Sums of Random Variables	E62-587, 11am-12pm
Nov 21	Alfred Galichon (Sciences Po, Paris) Optimal stochastic transport	E62-587, 11am-12pm
Dec 5	Harrison Huibin Zhou (Yale University) Sparse Canonical Correlation Analysis: Minimaxity and Adaptivity	E62-587, 11am-12pm
Dec 12	Whitney Newey (MIT Economics) Linear Regression with Many Included Covariates	E62-587, 11am-12pm

Spring 2014

Feb 7	Michael Brautbar (MIT) On the Power of Adversarial Infections in Networks	E62-587, 11am-12pm
Mar 7	Karthekeyan Chandrasekaran (Harvard) Integer Feasibility of Random Polytzopes	E62-587, 11am-12pm
Mar 21	Alexandre Tsybakov (CREST-ENSAE) Linear and Conic Programming Approaches to High-Dimensional Errors-in-variables Models	E62-587, 11am-12pm
Apr 11	Alexander Rakhlin (University of Pennsylvania, The Wharton School) Learning and estimation: separated at birth, reunited at last	E62-587, 11am-12pm
Apr 18	Sébastien Bubeck (Princeton University) On the influence of the seed graph in the preferential attachment model	E62-587, 11am-12pm
Apr 25	Joel Spencer (Courant Institute, New York University) Avoiding Outliers	E62-587, 11am-12pm
May 2	Sahand Negahban (Yale University) Computationally and Statistically Efficient Estimation in High-Dimensions	E62-587, 2pm-3pm
May 16	Antar Bandyopadhyay (University of California, Berkeley) De-Preferential Attachment Random Graphs	E62-587, 11am-12pm
May 23	Alex Belloni (Duke University) Uniform Post Selection Inference for Z-estimation problems	E62-587, 11am-12pm
May 30	Nathan Kallus (MIT) Regression-Robust Designs of Controlled Experiments	E62-587, 11am-12pm

Fall 2013

Sep 20	Elad Hazan (Technion) Sublinear Optimization	N/A
Sep 27	Jim Dai (Cornell University) Semimartingale reflecting Brownian motions: tail asymptotics for stationary distributions	E62-587, 11am-12pm
Nov 8	David Choi (Heinz College, Carnegie Mellon University) Consistency of Co-clustering exchangeable graph data	E62-587, 11am-12pm
Nov 13	Lie Wang (MIT) Multivariate Regression with Calibration	E62-587, 4pm-5pm
Nov 15	Ramon van Handel (Princeton University) Conditional Phenomena in Time and Space	E62-587, 11am-12pm
Dec 13	Nelly Litvak (University of Twente) Degree-degree dependencies in random graphs with heavy-tailed degrees	E62-587, 11am-12pm

Spring 2013

Feb 8	Rahul Jain (University of Southern California) The Art of Gambling in a Team: Multi-Player Multi-Armed Bandits
Apr 12	Yashodhan Kanoria (MSR New England and Columbia University) Which side chooses in large random matching markets?
May 3	Rahul Jain (University of Southern California) Transitory Queueing Systems

Fall 2012

Nov 30th	Rahul Mazumder (MIT) Low-rank Matrix Completion: Statistical Models and Large Scale Algorithms
Nov 16th	Kuang Xu (MIT) Queueing system topologies with limited flexibility
Oct 26th	Philippe Rigollet (Princeton University) Optimal detection of a sparse principal component

Spring 2012

Mar 30	Dmitry Shabanov (Yandex and Moscow Institute of Physics and Technology) Van der Warden Number and Coloring of Hypergraphs with Large Girth
Mar 29	Liudmila Ostroumova (Yandex and Moscow Institute of Physics and Technology) An Application of Talagrand’s Inequality to Prove a Concentration of Second Degrees in Buckley-Osthus Random Graph Model
Mar 28	Daniil Musatov (Yandex and Moscow Institute of Physics and Technology) Conditional Coding with Limiting Computational Resources
Mar 27	Andrei Raigorodksii (Yandex and Moscow Institute of Physics and Technology) Web Graph Models and Their Applications
Mar 26	Andrei Raigorodksii (Yandex and Moscow Institute of Physics and Technology) Research Groups at Yandex and Moscow Institute of Physics and Technology
Apr 20	Guy Bresler (University of California, Berkeley) Information theory of DNA sequencing
Apr 27	Alexander Rybko (Institute for Information Transmission Problems, Russia) Mean-field Limit for General Queueing Networks on Infinite Graphs
Apr 27	Semen Shlosman (CNRS, France and Institute for Information Transmission Problems, Russia) The Coherence Phase Transition
May 18	Alexei Borodin (Massachusetts Institute of Technology) Growth of random surfaces

Fall 2011

Oct 7	Erol Peköz (Boston University) Asymptotics for preferential attachment random graphs via Stein’s method
Oct 21	Eitan Bachmat (Ben-Gurion University) Does god play dice? An I/O scheduling and airplane boarding perspective
Dec 16	Yuan Zhong (MIT ORC) Delay optimality in switched networks

Spring 2009

Mar 13	Mohsen Bayati (Microsoft Research New England) Sequential algorithms for generating random graphs
Apr 3	Mokshay Madiman (Yale University) A New Look at the Compound Poisson Distribution and Compound Poisson Approximation using Entropy
Apr 10	Scott Sheffield (MIT Math) Fractional simple random walk
Apr 17	Vivek F. Farias (MIT Sloan) The Smoothed Linear Program for Approximate Dynamic Programming
May 1	Vivek Goyal (MIT EECS) On Resolution, Sparse Signal Recovery, and Random Access Communication

Fall 2008

Sep 23

Benoît Collins (University of Ottawa) Convergence of unitary matrix integrals

Spring 2008

Feb 29	Victor Chernozhukov (MIT Econ & ORC) Quantile and Probability Curves without Crossing
Mar 21	Daron Acemoglu (MIT Economics) Fragility of Asymptotic Agreement under Bayesian Learning
Apr 10	Peter Glynn(Stanford MS&E) Bounds on Stationary Expectations for Markov Processes
Apr 18	Ton Dieker(Georgia Tech I&SE) Large deviations for random walks under subexponentiality: the big-jump domain
Apr 25	Edward Farhi (MIT Physics) Quantum Computation by Adiabatic Evolution
May 23	Johan van Leeuwaarden (Eindhoven University of Technology, EURANDOM, NYU) The Gaussian random walk, sampling Brownian motion, and the Riemann zeta function

Fall 2007

Nov 16

David Forney (MIT LIDS) Exponential Error Bounds for Random Codes on the BSC