Seminar Archive

Spring 2020

 

Feb 7 Weijie Su (University of Pennsylvania)

Gaussian Differential Privacy, with Applications to Deep Learning

E18-304, 11am-12pm
Feb 14 Xiaohui Chen (University of Illinois at Urbana-Champaign)

Diffusion K-means Clustering on Manifolds: provable exact recovery via semidefinite relaxations

E18-304, 11am-12pm
Feb 21 Rina Foygel Barber (University of Chicago)

Predictive Inference with the Jackknife+

E18-304, 11am-12pm
Feb 28 Kavita Ramanan (Brown University)

Tales of Random Projections

E18-304, 11am-12pm
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

Sep 6 Tengyuan Liang (University of Chicago)
GANs, Optimal Transport, and Implicit Density Estimation
E18-304, 11am-12pm
Sep 20 Samory Kpotufe (Columbia)
Some New Insights On Transfer Learning
E18-304, 11am-12pm
Sep 27 Adam Klivans (UT Austin)
Frontiers of Efficient Neural-Network Learnability
E18-304, 11am-12pm
Oct 11 Christopher Moore (Santa Fe Institute)
The Planted Matching Problem
E18-304, 11am-12pm
Oct 18 Stanislav Minsker (USC)
Towards Robust Statistical Learning Theory
E18-304, 11am-12pm
Oct 25 Maria-Pia Victoria-Feser (University of Geneva)
Accurate Simulation-Based Parametric Inference in High Dimensional Settings
E18-304, 11am-12pm
Nov 8 Yudong Chen (Cornell)
SDP Relaxation for Learning Discrete Structures: Optimal Rates, Hidden Integrality, and Semirandom Robustness
E18-304, 11am-12pm
Nov 15 Lenka Zdeborova (Institut de Physique Théorique, CNRS)
Understanding Machine Learning with Statistical Physics
E18-304, 11am-12pm
Nov 22 Tamara Broderick (MIT)
Automated Data Summarization for Scalability in Bayesian Inference
E18-304, 11am-12pm
Dec 6 Simon Tavaré (Columbia)
Inferring the Evolutionary History of Tumors
E18-304, 11am-12pm

Spring 2019

Feb 1 Andrea Montanari (Stanford)
Optimization of the Sherrington-Kirkpatrick Hamiltonian
32-141, 11am – 12pm
Feb 8 Polina Golland (MIT CSAIL)
Medical Image Imputation
E18-304, 11am – 12pm
Feb 15 Zhou Fan (Yale)
TAP free energy, spin glasses, and variational inference
E18-304, 11am – 12pm
Feb 22 Nike Sun (MIT)
Capacity lower bound for the Ising perceptron
E18-304, 11am – 12pm
Mar 1 Eric Kolaczyk (Boston University)
Why Aren’t Network Statistics Accompanied By Uncertainty Statements?
E18-304, 11am – 12pm
Mar 8 Aditya Guntuboyina (UC Berkeley)
Univariate total variation denoising, trend filtering and multivariate Hardy-Krause variation denoising
E18-304, 11am – 12pm
Mar 15 Alex Belloni (Duke University)
Subvector Inference in Partially Identified Models with Many Moment Inequalities
E18-304, 11am – 12pm
Mar 22 Eliran Subag (New York University)
Optimization of random polynomials on the sphere in the full-RSB regime
E18-304, 11am – 12pm
Apr 12 Aaditya Ramdas (Carnegie Mellon University)
Exponential line-crossing inequalities
E18-304, 11am – 12pm
Apr 19 Dylan Foster (MIT)
Logistic Regression: The Importance of Being Improper
E18-304, 11am – 12pm
Apr 26 Chao Gao (University of Chicago)
Robust Estimation: Optimal Rates, Computation and Adaptation
E18-304, 11am – 12pm
May 3 Tracy Ke (Harvard)
Optimal Adaptivity of Signed-Polygon Statistics for Network Testing (Tracy Ke, Harvard University)
E18-304, 11am – 12pm
May 10 Will Perkins (University of Illinois at Chicago)
Counting and sampling at low temperatures
E18-304, 11am – 12pm

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 Polytopes
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