General Chair
Tong Zhang
University of Illinois
Tong Zhang is a professor in the Computer Science department at the University of Illinois Urbana Champaign. He is a fellow of the IEEE, American Statistical Association, and Institute of Mathematical Statistics. His research interests include machine learning theory, algorithms, and applications. Tong Zhang has served as the chair or area chair in major machine learning conferences such as NeurIPS, ICML, and COLT, and has also served on the editorial boards of leading machine learning journals such as PAMI, JMLR, and the Machine Learning Journal.
Tong Zhang is a professor in the Computer Science department at the University of Illinois Urbana Champaign. He is a fellow of the IEEE, American Statistical Association, and Institute of Mathematical Statistics. His research interests include machine learning theory, algorithms, and applications. Tong Zhang has served as the chair or area chair in major machine learning conferences such as NeurIPS, ICML, and COLT, and has also served on the editorial boards of leading machine learning journals such as PAMI, JMLR, and the Machine Learning Journal.
Program Chair
Alekh Agarwal
Google
Program Chair
Miroslav Dudik
Microsoft Research
Miroslav Dudík is a Senior Principal Researcher in machine learning at Microsoft Research, NYC. His research focuses on combining theoretical and applied aspects of machine learning, statistics, convex optimization, and algorithms. Most recently he has worked on contextual bandits, reinforcement learning, and algorithmic fairness. He received his PhD from Princeton in 2007. He is a co-creator of the Fairlearn toolkit for assessing and improving the fairness of machine learning models and of the Maxent package for modeling species distributions, which is used by biologists around the world to design national parks, model the impacts of climate change, and discover new species.
Miroslav Dudík is a Senior Principal Researcher in machine learning at Microsoft Research, NYC. His research focuses on combining theoretical and applied aspects of machine learning, statistics, convex optimization, and algorithms. Most recently he has worked on contextual bandits, reinforcement learning, and algorithmic fairness. He received his PhD from Princeton in 2007. He is a co-creator of the Fairlearn toolkit for assessing and improving the fairness of machine learning models and of the Maxent package for modeling species distributions, which is used by biologists around the world to design national parks, model the impacts of climate change, and discover new species.
Program Chair
Martin Jaggi
EPFL
Program Chair
Sharon Li
Assistant Professor
University of Wisconsin-Madison
Ethics Chair
Asia Biega
Max Planck
Ethics Chair
Lydia T. Liu
Princeton University
Lydia T. Liu is an assistant professor of computer science at Princeton University. Her research examines the theoretical and scientific foundations of machine learning and algorithmic decision-making, with a focus on long-term societal impact. She obtained her Ph.D. in electrical engineering and computer sciences from the University of California, Berkeley, and completed her postdoctoral research at Cornell University. She is the recipient of several internationally recognized awards, including the ICML Best Paper Award, the Amazon Research Award, the Microsoft Ada Lovelace Fellowship, and the Open Philanthropy AI Fellowship.
Lydia T. Liu is an assistant professor of computer science at Princeton University. Her research examines the theoretical and scientific foundations of machine learning and algorithmic decision-making, with a focus on long-term societal impact. She obtained her Ph.D. in electrical engineering and computer sciences from the University of California, Berkeley, and completed her postdoctoral research at Cornell University. She is the recipient of several internationally recognized awards, including the ICML Best Paper Award, the Amazon Research Award, the Microsoft Ada Lovelace Fellowship, and the Open Philanthropy AI Fellowship.
Position Paper Track Chair
Dale Schuurmans
Google DeepMind / University of Alberta
Position Paper Track Chair
Jerry Zhu
University of Wisconsin-Madison
Scientific Integrity Chair
Katherine Heller
Google
Scientific Integrity Chair
Nihar Shah
Carnegie Mellon University
Scientific Integrity Chair
Weijie Su
OpenAI
Workshop Chair
Gergely Neu
Univ Pompeu Pabra
Workshop Chair
Courtney Paquette
McGill University/Google DeepMind
Tutorial Chair
Claire Vernade
Research scientist
University of Tübingen
Tutorial Chair
Adam White
University of Alberta, Alberta Machine Intelligence Institute (Amii), RL Core
Keywords: Continual Learning, Reinforcement Learning, Robotics, Knowledge Representation and Intrinsic Motivation
Adam's research is focused on understanding the fundamental principles of learning in both simulated worlds and industrial control applications. His research program explores how the problem of intelligence can be modeled as a reinforcement learning agent interacting with some unknown environment, learning from a scalar reward signal rather than explicit feedback. Adam's group is deeply passionate about good empirical practices and new methodologies to help determine if our algorithms are ready for deployment in the real world. Adam has pioneered applications of reinforcement learning to real drinking and wastewater treatment plants and is the co-founder of RL Core Technologies, a startup applied AI and machine learning across industrial control.
Keywords: Continual Learning, Reinforcement Learning, Robotics, Knowledge Representation and Intrinsic Motivation
Adam's research is focused on understanding the fundamental principles of learning in both simulated worlds and industrial control applications. His research program explores how the problem of intelligence can be modeled as a reinforcement learning agent interacting with some unknown environment, learning from a scalar reward signal rather than explicit feedback. Adam's group is deeply passionate about good empirical practices and new methodologies to help determine if our algorithms are ready for deployment in the real world. Adam has pioneered applications of reinforcement learning to real drinking and wastewater treatment plants and is the co-founder of RL Core Technologies, a startup applied AI and machine learning across industrial control.
Publication Chair
Felix Berkenkamp
Aleph Alpha Research
Publication Chair
Alberto Bietti
Flatiron Institute
Publication Chair
Hanze Dong
Microsoft
Associate Chair
Hyeong Kyu Choi
University of Wisconsin-Madison
Associate Chair
Alexander Hägele
EPFL
Associate Chair
Buxin Su
University of Pennsylvania
Communications Chair
Tegan Emerson
Pacific Northwest National Laboratory
Communications Chair
Katherine Gorman
Talking Machines
Communications Chair
Gautam Kamath
Mr.
University of Waterloo
Social Chair
Kevin Leyton-Brown
Professor
University of British Columbia
Social Chair
Chulhee Yun
KAIST
Local Chairs
Chang Yoo
Kaist
Inclusion & Accessibility Chair
Maria Skoularidou
Broad Institute
Inclusion & Accessibility Chair
Amy Zhang
UT Austin
Expo Chair
Ismini Lourentzou
University of Illinois Urbana-Champaign
Expo Chair
Wenming Ye
Model AI Corp
Workflow Chair
Zhenyu (Sherry) Xue
ICML
Conference Production
Brad Brockmeyer
ICML Staff
Conference Production
Lee Campbell
Bioinformatics
Eventhosts
Conference Production
Tony Manzo
ICML Staff
Conference Production
Brian Nettleton
Eventhosts
Conference Production
Mary Ellen Perry
Admin
Eventhosts
Conference Production
Max Wiesner
ICML
Conference Production
Stephanie Willes
ICML Conference Staff
