These videos are freely available at Here’s the list so far.

[IMS ML = IMS Medallion Lecture]


BS/IMS Schramm Lecture Balloons in Space(s) – Omer Angel

IMS Wald Lectures I: Random Walks and Fractal Graphs; II: Low Dimensional Random Fractals; and III: Higher Dimensional Spaces – Martin Barlow

IMS ML: Gambler’s Ruin Problems – Laurent Saloff-Coste

IMS ML: Random Determinants and the Elastic ManifoldGérard Ben Arous

IMS ML: Simplicity and complexity of belief-propagation – Elchanan Mossel

BS/IMS Doob Lecture: Parking on Cayley trees and Frozen Erdős–Rényi – Nicolas Curien

IMS Brown Award: Toward instance-optimal reinforcement learning – Ashwin Pananjady

IMS Brown Award: Efficient manifold approximation with SphereletsDidong Li

IMS Brown Award: Bayesian pyramids: Identifying interpretable discrete latent structures from discrete dataYuqi Gu

IMS Blackwell Lecture: Estimating the mean of a random vectorGabor Lugosi

IMS ML: Selective inference for trees – Daniela Witten

IMS ML: High-dimensional interpolators: From linear regression to neural tangent models – Andrea Montanari

IMS Wald Lectures I & II: Modeling and Estimating Large Sparse Networks I & Modeling and Estimating Large Sparse Networks IIJennifer Chayes

IMS President Address: Proactive and All-Encompassing StatisticsRegina Liu

IMS ML: DNA Copy Number Profiling from Bulk Tissues to Single CellsNancy Zhang

IMS Lawrence D. Brown PhD Student Award Session 2021: First-Order Newton-Type Estimator for Distributed Estimation and InferenceYichen Zhang; Minimax Optimality of Permutation TestsIlmun Kim; and Inference in Interpretable Latent Factor Regression ModelsXin Bing

IMS ML: What Kinds of Functions Do Neural Networks Learn?Robert Nowak

IMS ML: Empirical Optimal Transport: Inference, Algorithms, ApplicationsAxel Munk

IMS Le Cam Lecture: Understanding Spectral EmbeddingJianqing Fan

IMS ML: Statistical Optimal TransportPhilippe Rigollet