ICML President
Organizers
ICML 2021
General Chair
Nina Balcan
Maria-Florina Balcan is an Associate Professor in the School of Computer Science at Carnegie Mellon University. Her main research interests are machine learning and theoretical computer science. Her honors include the CMU SCS Distinguished Dissertation Award, an NSF CAREER Award, a Microsoft Faculty Research Fellowship, a Sloan Research Fellowship, and several paper awards. She has served as a Program Committee Co-chair for COLT 2014, a Program Committee Co-chair for ICML 2016, and a board member of the International Machine Learning Society.
Maria-Florina Balcan is an Associate Professor in the School of Computer Science at Carnegie Mellon University. Her main research interests are machine learning and theoretical computer science. Her honors include the CMU SCS Distinguished Dissertation Award, an NSF CAREER Award, a Microsoft Faculty Research Fellowship, a Sloan Research Fellowship, and several paper awards. She has served as a Program Committee Co-chair for COLT 2014, a Program Committee Co-chair for ICML 2016, and a board member of the International Machine Learning Society.
Program Chair
Marina Meila
Program Chair
Tong Zhang
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.
Workshop Chair
Raman Arora
Raman Arora received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Wisconsin-Madison in 2005 and 2009, respectively. From 2009-2011, he was a Postdoctoral Research Associate at the University of Washington in Seattle and a Visiting Researcher at Microsoft Research Redmond. Since 2011, he has been with Toyota Technological Institute at Chicago (TTIC). His research interests include machine learning, speech recognition and statistical signal processing.
Raman Arora received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Wisconsin-Madison in 2005 and 2009, respectively. From 2009-2011, he was a Postdoctoral Research Associate at the University of Washington in Seattle and a Visiting Researcher at Microsoft Research Redmond. Since 2011, he has been with Toyota Technological Institute at Chicago (TTIC). His research interests include machine learning, speech recognition and statistical signal processing.
Workshop Chair
Jun Zhu
Tutorial Chair
Quoc Le
Tutorial Chair
Caroline Uhler
Caroline Uhler joined the MIT faculty in 2015 as the Henry L. and Grace Doherty assistant professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. She holds an MSc in mathematics, a BSc in biology, and an MEd in high school mathematics education from the University of Zurich. She obtained her PhD in statistics, with a designated emphasis in computational and genomic biology, from the University of California, Berkeley. Before joining MIT, she spent a semester as a research fellow in the program on Theoretical Foundations of Big Data Analysis at the Simons Institute at UC Berkeley, postdoctoral positions at the Institute for Mathematics and its Applications at the University of Minnesota and at ETH Zurich, and 3 years as an assistant professor at IST Austria. She is an elected member of the International Statistical Institute, a Sloan Research Fellow, and she received an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation and a START Award from the Austrian Science Foundation. Her research focuses on mathematical statistics and computational biology, in particular on graphical models and causal inference.
Caroline Uhler joined the MIT faculty in 2015 as the Henry L. and Grace Doherty assistant professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. She holds an MSc in mathematics, a BSc in biology, and an MEd in high school mathematics education from the University of Zurich. She obtained her PhD in statistics, with a designated emphasis in computational and genomic biology, from the University of California, Berkeley. Before joining MIT, she spent a semester as a research fellow in the program on Theoretical Foundations of Big Data Analysis at the Simons Institute at UC Berkeley, postdoctoral positions at the Institute for Mathematics and its Applications at the University of Minnesota and at ETH Zurich, and 3 years as an assistant professor at IST Austria. She is an elected member of the International Statistical Institute, a Sloan Research Fellow, and she received an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation and a START Award from the Austrian Science Foundation. Her research focuses on mathematical statistics and computational biology, in particular on graphical models and causal inference.
Accessibility and Diversity & Inclusion Chair
Miroslav Dudik
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.
Accessibility and Diversity & Inclusion Chair
Barbara Engelhardt
Barbara E. Engelhardt is a professor at Stanford University. She joined from Princeton, where she was a professor from 2014-2022, and previously Duke University, where she was an assistant professor from 2011-2014. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.
Barbara E. Engelhardt is a professor at Stanford University. She joined from Princeton, where she was a professor from 2014-2022, and previously Duke University, where she was an assistant professor from 2011-2014. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.
Sponsorship Chair
Avrim Blum
Sponsorship Chair
Simon Lacoste-Julien
Simon Lacoste-Julien is a full professor in the department of computer science and operations research at Université de Montréal, a co-founding member and associate scientific director of Mila, and the part-time director of the Samsung AI Lab Montreal. He received the B.Sc. degree in mathematics, physics and computer science from McGill University, and the PhD degree in computer science with a designated emphasis in statistics from University of California, Berkeley, in 2009. Before joining Université de Montréal, he completed a post-doctoral fellowship at University of Cambridge as well as at Inria Paris, and was an Inria researcher in the Department of Computer Science at the Ecole Normale Supérieure (ENS) in Paris. His research interests are in machine learning, optimization and statistics with applications to computer vision and natural language processing. He has published more than 80 scientific publications in machine learning, has served as an area chair for all the major machine learning conferences, he is an associate editor for TPAMI, JMLR and TMLR and acted as co-program chair for ICML 2025. He received a Google Focused Research Award in 2016 and a CIFAR AI Chair in 2018 and 2024.
Simon Lacoste-Julien is a full professor in the department of computer science and operations research at Université de Montréal, a co-founding member and associate scientific director of Mila, and the part-time director of the Samsung AI Lab Montreal. He received the B.Sc. degree in mathematics, physics and computer science from McGill University, and the PhD degree in computer science with a designated emphasis in statistics from University of California, Berkeley, in 2009. Before joining Université de Montréal, he completed a post-doctoral fellowship at University of Cambridge as well as at Inria Paris, and was an Inria researcher in the Department of Computer Science at the Ecole Normale Supérieure (ENS) in Paris. His research interests are in machine learning, optimization and statistics with applications to computer vision and natural language processing. He has published more than 80 scientific publications in machine learning, has served as an area chair for all the major machine learning conferences, he is an associate editor for TPAMI, JMLR and TMLR and acted as co-program chair for ICML 2025. He received a Google Focused Research Award in 2016 and a CIFAR AI Chair in 2018 and 2024.
Social Chair
Nika Haghtalab
Social Chair
Olga Isupova
Social Chair
Adam White
Publications Chair
Daniel Hsu
Publications Chair
Yingyu Liang
Communications Chair
Stefanie Jegelka
Communications Chair
Ameet Talwalkar
Workflow Chair
Xinwei Shen
Workflow Chair
Zhenyu (Sherry) Xue
Workflow Chair
Hanyu Zhang
Expo Chair
Hsuan-Tien (Tien) Lin
Expo Chair
Alice Zheng
Virtual Chair
Y-Lan Boureau
Virtual Chair
Marija Stanojevic
Virtual Chair
Hendrik Strobelt
Currently, I am working as a Researcher in Information Visualisation, Visual Analytics, and Machine Learning at IBM Research AI in Cambridge, MA. I am interested in Visualization of large data sets of unstructured/semi-structured data, biological data, and neural network models. I enjoy advising students and enable them to do great work.
I try to be a good citizen in the community by reviewing regularly (InfoVis 2010-2018, BioVis 2012/13, CHI 2011/2014/2017-18, VAST 2010-2018, EuroVis,...), participating in InfoVis PC 2017-19, the VIS OC 2017-19, the BioVis 2013 OC and PC, and other committees. I had the honor to attend three great and motivating Dagstuhl seminars on InfoVis, BioVis, and Progressive Data Science. Oh, and I like to give talks from time to time.
Currently, I am working as a Researcher in Information Visualisation, Visual Analytics, and Machine Learning at IBM Research AI in Cambridge, MA. I am interested in Visualization of large data sets of unstructured/semi-structured data, biological data, and neural network models. I enjoy advising students and enable them to do great work.
I try to be a good citizen in the community by reviewing regularly (InfoVis 2010-2018, BioVis 2012/13, CHI 2011/2014/2017-18, VAST 2010-2018, EuroVis,...), participating in InfoVis PC 2017-19, the VIS OC 2017-19, the BioVis 2013 OC and PC, and other committees. I had the honor to attend three great and motivating Dagstuhl seminars on InfoVis, BioVis, and Progressive Data Science. Oh, and I like to give talks from time to time.
Executive Director
Terri Auricchio
Logistics and Conference Planning
Brad Brockmeyer
Logistics and Conference Planning
Lee Campbell
Logistics and Conference Planning
Brian Nettleton
Logistics and Conference Planning
Mary Ellen Perry
Logistics and Conference Planning
Max Wiesner
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