Team Members

The IFML team includes researchers from the University of Texas at Austin, the University of Washington, Wichita State University, and Microsoft Research.

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Leadership

  • Adam Klivans

    Adam Klivans

    Director, Institute for Foundations of Machine Learning
    Professor, Computer Science
    University of Texas at Austin

    Areas of interest: Deep Learning, Graphical Models

  • Alex Dimakis

    Alex Dimakis

    Co-Director, Institute for Foundations of Machine Learning 
    Professor, Electrical Engineering & Computer Sciences
    University of California, Berkeley

    Areas of interest: Information theory, Coding theory, Unsupervised Machine Learning

  • Brent Winkelman

    Brent Winkelman

    Managing Director, Institute for Foundations of Machine Learning
     

Senior Personnel

  • Scott Aaronson

    Professor, Computer Science

    Areas of interest: Theoretical Computer Science; Capabilities and limits of quantum computers; Computational complexity heory

  • Byron Boots

    Associate Professor, Computer Science and Engineering
    University of Washington

    Areas of interest: Learning with Dynamic Data, Exploiting Structure in Data, Optimizing Real World Objectives

  • Al Bovik

    Professor, Electrical and Computer Engineering
    University of Texas at Austin

    Areas of interest: Advanced Algorithms for Deep Learning, Learning with Dynamic Data, Exploiting Structure in Data

  • Sebastien Bubeck

    Sr. Principal Research Manager
    Microsoft Research

    Areas of interest: Machine Learning Theory, Optimization, Statistical Machine Learning

  • Constantine Caramanis

    Professor, Electrical & Computer Engineering
    University of Texas at Austin

  • Swarat Chaudhuri

    Associate Professor, Computer Science, 
    University of Texas at Austin

    Areas of interest: Exploiting Structure in Data, Optimizing Real World Objectives, Explainable AI

  • Daniel Diaz

    Postdoctoral Fellow, Lead of Deep Proteins group.

    Areas of interest: Area of Interest: Machine Learning-guided Protein Engineering and Design

  • Isil Dillig

    Associate Professor, Computer Science 
    University of Texas at Austin

    Areas of interest: Static program analysis/verification, Program synthesis, Automated logical reasoning

  • Simon S. Du

    Assistant Professor
    University of Washington

    Areas of interest: Advanced Algorithms for Deep Learning, Exploiting Structure in Data, Optimizing Real World Objectives

  • Greg Durrett

    Assistant Professor, Computer Science
    University of Texas at Austin
     

    Areas of interest: Deep Learning, Natural Language

  • Ken Fleischmann

    Professor, School of Information 
    University of Texas at Austin

    Areas of interest: AI Ethics, Social Informatics, Value-Sensitive Design

  • Kristen Grauman

    Professor, Computer Science
    University of Texas at Austin

    Areas of interest: Deep Learning, Robotics, Vision

  • Suriya Gunasekar

    Senior Researcher
    Microsoft Research Redmond

    Areas of interest: Advanced Algorithms for Deep Learning, Deep Learning, Machine Learning Theory

  • Zaid Harchaoui

    Associate Professor, Statistics 
    University of Washington

    Areas of interest: Robust statistical machine learning, Learning feature representations of complex data, Computationally-efficient optimization algorithms for learning and inference

  • Qijia Jiang

    Postdoc
    PhD, Stanford University

    Areas of interest: Optimization/Sampling, Signal Processing, Statistics

  • Sham Kakade

    Professor, Computer Science and Engineering, 
    University of Washington

    Areas of interest: Reinforcement learning and controls, Representation Learning, Deep Learning

  • Guoliang Kang

    Postdoc
    PhD, University of Texas at Austin

    Areas of interest: Visual Domain Adaptation, Unsupervised Learning, Semi-Supervised Learning

  • Philipp Krähenbühl

    Assistant Professor, Computer Science 
    University of Texas at Austin

    Areas of interest: Optimizing Real World Objectives, Deep Learning, Vision

  • Jason D. Lee

    Associate Professor, Electrical and Computer Engineering, Princeton

    Areas of interest: Foundations of AI and Deep Learning, Representation Learning, Foundations of Deep Reinforcement Learning

  • Jerry Li

    Senior Researcher
    Microsoft Research

    Areas of interest: Advanced Algorithms for Deep Learning, Machine Learning Theory, Statistical Machine Learning

  • Qiang Liu

    Assistant Professor, Computer Science
    University of Texas at Austin

    Areas of interest: Advanced Algorithms for Deep Learning, Learning with Dynamic Data, Exploiting Structure in Data

  • Raghu Meka

    Associate Professor, Computer Science
    UCLA Samueli School of Engineering
     

    Areas of interest: Complexity theory, Learning theory, Algorithms, Probability theory

  • Aryan Mokhtari

    Assistant Professor, Electrical and Computer Engineering
    University of Texas at Austin

    Areas of interest: Optimization, Machine Learning, Artificial Intelligence

  • Cris Moore

    Professor, Santa Fe Institute
     

    Areas of interest: Problems at the interface of physics, computer science, and mathematics, such as phase transitions in statistical inference

  • Jamie Morgenstern

    Assistant Professor, Computer Science and Engineering
    University of Washington

    Areas of interest: Learning with Dynamic Data, Active Learning, Machine Learning Theory

  • Scott Niekum

    Assistant Professor, Computer Science
    University of Texas at Austin

    Areas of interest: Optimizing Real World Objectives, Active Learning, Deep Learning

  • Sewoong Oh

    Associate Professor, Computer Science and Engineering
    University of Washington

    Areas of interest: Advanced Algorithms for Deep Learning, Learning with Dynamic Data, Exploiting Structure in Data

  • Eric Price

    Assistant Professor, Computer Science 
    University of Texas at Austin

  • Jay Reddy

    Director, Engineering Analytics
    Infrastructure Solutions Group
    Dell Technologies

  • Alessandro Rinaldo

    Alessandro Rinaldo, Professor, Department of Statistics & Data Sciences, University of Texas at Austin

    Areas of interest: Theoretical properties of high-dimensional statistical models

  • Sujay Sanghavi

    Associate Professor, Electrical & Computer Engineering
    University of Texas at Austin

    Areas of interest: Advanced Algorithms for Deep Learning, Active Learning, Deep Learning

  • Purnamrita Sarkar

    Assistant Professor, Statistics & Data Sciences
    University of Texas at Austin

    Areas of interest: Large Scale Machine Learning, Machine Learning Theory, Networks

  • Ludwig Schmidt

    Professor
    Toyota Research Institute and University of Washington (starting Fall 2021)

    Areas of interest: Advanced Algorithms for Deep Learning, Deep Learning, Machine Learning Theory

  • Sanjay Shakkottai

    Professor, Electrical & Computer Engineering
    University of Texas at Austin

    Areas of interest: Advanced Algorithms for Deep Learning, Learning with Dynamic Data, Active Learning

  • Kaushik Sinha

    Associate Professor, Electrical Engineering and Computer Science
    Wichita State University

    Areas of interest: Exploiting Structure in Data, Deep Learning, Knowledge Representation

  • Peter Stone

    Professor, Computer Science 
    University of Texas at Austin

    Areas of interest: Learning with Dynamic Data, Exploiting Structure in Data, Optimizing Real World Objectives

  • Jon Tamir

    Assistant Professor, Electrical & Computer Engineering
    University of Texas at Austin

    Areas of interest: bioECE, Decision, Information, and Communications Engineering (DICE)

  • Ngoc Mai Tran

    Assistant Professor, Mathematics
    University of Texas at Austin

    Areas of interest: Tropical Geometry, Probability, Combinatorics, Economics, Neuroscience

  • Atlas Wang

    Assistant Professor, Electrical and Computer Engineering
    University of Texas at Austin

    Areas of interest: Advanced Algorithms for Deep Learning, Learning with Dynamic Data, Exploiting Structure in Data, Optimizing Real-World Objectives

  • Rachel Ward

    Professor, Mathematics
    University of Texas at Austin

    Areas of interest: Advanced Algorithms for Deep Learning, Learning with Dynamic Data, Exploiting Structure in Data

  • Yisong Yue

    Professor, Computing and Mathematical Sciences, CalTech

    Areas of interest: Primarily in machine learning and spanning the entire theory-to-application spectrum from foundational advances all the way to deployment in real systems

  • Amy Zhang

    Assistant Professor, Electrical & Computer Engineering, University of Texas at Austin

    Areas of interest: Reinforcement Learning, Exploiting Structure in Data, Representation Learning

Advisory Board

  • Pieter Abbeel

    Professor, Electrical Engineering and Computer Sciences
    University of California, Berkeley

  • Piotr Indyk

    Thomas D. and Virginia W. Cabot Professor
    Department of Electrical Engineering and Computer Science
    MIT

  • Ravi Kumar

    Principal Research Scientist
    Google

  • Jitendra Malk

    Professor, Electrical Engineering and Computer Science
    University of California, Berkeley

  • Ronitt Rubinfeld

    Professor, Electrical Engineering and Computer Science
    MIT

  • Satinder Singh

    Professor, Computer Science and Engineering
    University of Michigan

  • Alexander Tropsha

    Associate Dean for Pharmacoinformatics & Data Science
    K.H. Lee Distinguished Professor, Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy

    University of North Carolina, Chapel Hill

  • Manuela Veloso

    Head of AI Research/Professor, AI Research
    JPMorgan Chase/Carnegie Mellon University

  • Rebecca Willett

    Professor, Statistics and Computer Science
    University of Chicago

Partners

We are proud to be engaged in research projects with the following industrial and governmental partners:

  • Colleges

    • College of Natural Sciences
    • Cockrell School of Engineering
    • School of Information
    • McCombs School of Business
    • College of Liberal Arts

  • Departments

    • Department of Computer Science
    • Department of Electrical & Computer Engineering
    • Department of Information, Risk, and Operations Management
    • Department of Linguistics
    • Department of Mathematics
    • Department of Psychology
    • Department of Statistics & Data Science

  • Others

    • Office of the Vice President for Research
    • Oden Institute
    • Texas Advanced Computing Center
    • Texas Computing
    • Texas Robotics
    • Good Systems