Recorded Talks

  • 2023 Machine Learning Lab Public Lecture with Scott Aaronson

    Scott Aaronson, theoretical computer scientist and David J. Bruton Jr. Centennial Professor of Computer Science, UT Austin

  • Machine Learning Lab 2022 Public Lecture with Alan Bovik

    Alan Bovik, Director, Laboratory for Image & Video Engineering, Machine Learning Lab, UT Austin

  • The Power of Adaptivity in Representation Learning: From Meta-Learning to Federated Learning

    Aryan Mokhtari, Assistant Professor, Electrical and Computer Engineering, UT Austin

  • Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning

    Dr. Peter Stone, David Bruton, Jr. Centennial Professor, Associate Chair of Computer Science, Director of Texas Robotics

  • Deployable Robots that Learn

    Xuesu Xiao, George Mason University (Fall 2022)

  • Multi-Modal Deep Learning of Electrocardiograms for Precision Cardiovascular Health

    Benjamin Glicksberg, Assistant Professor, Icahn School of Medicine at Mount Sinai

  • Clustering Mixtures with Almost Optimal Separation in Polynomial Time

    Allen Liu, graduate student in EECS at MIT.

  • IFML Public Lecture: AI for Accurate and Fair Imaging

    Alex Dimakis, IFML and MLL co-director

  • Optimal Control for Electroceutical Therapies

    Joshua Chang, M.D., Ph.D., Assistant Professor, Department of Neurology, The University of Texas at Austin

  • Function Space View of Bounded Weight Norm Networks

    Suriya Gunasekar (Microsoft Research)

  • The Planted Matching Problem: Sharp Threshold and Infinite-Order Phase Transition

    Dana Yang (Simons Institute)

  • Scalable and Reliable Inference for Probabilistic Modeling

    Ruqi Zhang (UT Austin)

  • Learning and Optimization With Adaptive Smoothed Adversaries

    Nika Haghtalab (UC Berkeley)

  • The Surprising Power of the Lenstra-Lenstra-Lovasz Algorithm for Noiseless Inference

    Ilias Zadik (MIT)

  • Yes, Generative Models are the New Sparsity

    Alex Dimakis, UT Austin

  • On the Power of Differentiable Learning

    Nathan Srebro (Toyota Technological Institute at Chicago)

  • A Data-Centric View On Robustness

    Ludwig Schmidt (University of Washington)

  • Evaluating AI-based MR image reconstruction models: lessons from the fastMRI project

    Matthew Muckley

  • Closing the Virtuous Cycle of AI for IC and IC for AI

    David Pan

  • Mapping timescales of cortical language processing

    Alex Huth

  • Bootstrapping the Error of Oja's Algorithm

    Purnamrita Sarkar

  • The Role of Explicit Regularization in Overparameterized Neural Networks

    Shiyu Liang

  • Mind the Gap: From Predictions to ML-Informed Decisions

    Maria De-Arteaga

  • Facing an Adult Problem: New Data Sources for Fair Machine Learning

    Moritz Hardt

  • Disparate Predictions: A Complicated Landscape

    Jamie Morgenstern

  • CityLearn: Demand Response using Multi-Agent Reinforcement Learning

    Zoltan Nagy

  • Data Analytics and ML for Subsurface Engineering and Geoscience

    Michael Pyrcz

  • Computational MRI w Deep Learning

    Jon Tamir

  • ApBio: Teaching Machines Biochemistry

    Andrew Ellington

  • Harnessing Machine Learning to Study the Life Cycle of Stars

    Stella Offner

  • N Body Hessians

    George Biros

  • Remote Sensing and AI Applications in Paleoanthropology

    Denné Reed

  • In Search of New Algorithms Part II: Graphical Models

    Adam Klivans

  • In Search of New Algorithms Part I: Neural Networks

    Adam Klivans

  • The lottery ticket hypothesis for gigantic pre-trained models

    Atlas Wang

  • AI and Society

    Ken Fleischmann

  • Applications of AI

    Raymond Mooney

  • What Is Artificial Intelligence and Machine Learning?

    Joydeep Biswas