Our collection of IMS lectures on YouTube is growing. You can watch at https://www.youtube.com/@InstMathStat/videos.
These ones are uploaded already, with more to be added:
Alicia Carriquiry, IMS Medallion Lecture: “Statistics and Its Application in Forensic Science and the Criminal Justice System”
Jing Lei, IMS Medallion Lecture: “Winners with Confidence: Discrete Argmin Inference Using Cross-validated Exponential Mechanism”
Nancy Reid, IMS Medallion Lecture: “Data Integration for Heterogeneous Data”
Annie Qu, IMS Wahba Lecture: “Models and Parameters: Inference Under Model Misspecification”
Sylvia Richardson, ICSDS 2022 Plenary Talk: “Scaling up Bayesian Modeling and Computation for real-world biomedical and public health applications”
Susan Murphy, ICSDS 2022 Plenary Talk: “Inference for Longitudinal Data After Adaptive Sampling”
Guido Imbens, ICSDS 2022 Plenary Talk: “Multiple Randomization Designs”
Emmanuel Candès, ICSDS 2022 Plenary Talk: “Conformal Prediction in 2022”
Huixia Judy Wang, IMS Medallion Lecture: “Extreme Conditional Quantiles”
Dylan Small, IMS Medallion Lecture: “Protocols for Observational Studies: Methods and open problems”
Alexei Borodin, IMS Medallion Lecture: “Deformed Polynuclear Growth in (1+1) Dimensions”
Ramon van Handel, IMS Medallion Lecture: “Non-asymptotic Random Matrix Theory”
Rungang Han, IMS Brown Session: “Multiway Clustering in Tensor Block Model: Statistical optimality and computational limit”
Chan Park, IMS Brown Session: “Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects”
Rong Ma, IMS Brown Session: “Statistical Inference for High-Dimensional Generalized Linear Models with Binary Outcomes”
Russell Lyons, IMS/BS Schramm Lecture: “Monotonicity for Continuous-Time Random Walks”
Michael Jordan, IMS Wahba Lecture: “On the Blending of Statistical Machine Learning and Microeconomics”
Hans-Georg Müller, IMS Rietz Lecture: “Statistical Tools for Random Objects in Metric Spaces”
Heping Zhang, IMS Neyman Lecture: “Genes, Brain, and Us”
Martin Hairer, IMS Wald Lectures: “Universality and Crossover in 1+1 Dimensions (Part I)” and “Universality and Crossover in 1+1 Dimensions (Part II)”
Roman Vershynin, IMS Medallion Lecture IV: “Privacy, Probability, and Synthetic Data”
Vlada Limic, IMS Medallion Lecture III: “Multiplicative Coalescent Related Processes”
Rina Foygel Barber, IMS Medallion Lecture II: “Distribution-free Prediction: Exchangeability and beyond”
Rodrigo Bañuelos, IMS Medallion Lecture I: “A Doob h-process and its Applications to Singular Integrals on Z^d”
Omer Angel, BS/IMS Schramm Lecture “Balloons in Space(s)”
Martin Barlow, IMS Wald Lectures, I: “Random Walks and Fractal Graphs”; II: “Low Dimensional Random Fractals”; III: “Higher Dimensional Spaces”
Laurent Saloff-Coste, IMS Medallion Lecture: “Gambler’s Ruin Problems”
Gérard Ben Arous, IMS Medallion Lecture: “Random Determinants and the Elastic Manifold”
Elchanan Mossel, IMS Medallion Lecture: “Simplicity and complexity of belief-propagation”
Nicolas Curien, BS/IMS Doob Lecture: “Parking on Cayley trees and Frozen Erdős–Rényi”
Ashwin Pananjady, IMS Brown Award: “Toward instance-optimal reinforcement learning”
Didong Li, IMS Brown Award: “Efficient manifold approximation with Spherelets”
Yuqi Gu, IMS Brown Award:“Bayesian pyramids: Identifying interpretable discrete latent structures from discrete data”
Gabor Lugosi, IMS Blackwell Lecture: “Estimating the mean of a random vector”
Daniela Witten, IMS Medallion Lecture: “Selective inference for trees”
Andrea Montanari, IMS Medallion Lecture: “High-dimensional interpolators: From linear regression to neural tangent models”
Jennifer Chayes, IMS Wald Lectures: “Modeling and Estimating Large Sparse Networks I” and “Modeling and Estimating Large Sparse Networks II”
Regina Liu, IMS Presidential Address: “Proactive and All-Encompassing Statistics”
Nancy Zhang, IMS Medallion Lecture: “DNA Copy Number Profiling from Bulk Tissues to Single Cells”
IMS Lawrence D. Brown PhD Student Award Session 2021
Robert Nowak, IMS Medallion Lecture: “What Kinds of Functions Do Neural Networks Learn?”
Axel Munk, IMS Medallion Lecture: “Empirical Optimal Transport: Inference, Algorithms, Applications”
Jianqing Fan, IMS Le Cam Lecture: “Understanding Spectral Embedding”
Philippe Rigollet, IMS Medallion Lecture: “Statistical Optimal Transport”