Publications Chair
Iain Murray
University of Edinburgh
Iain Murray is a SICSA Lecturer in Machine Learning at the University of
Edinburgh. Iain was introduced to machine learning by David MacKay and
Zoubin Ghahramani, both previous NIPS tutorial speakers. He obtained his
PhD in 2007 from the Gatsby Computational Neuroscience Unit at UCL. His
thesis on Monte Carlo methods received an honourable mention for the
ISBA Savage Award. He was a commonwealth fellow in Machine Learning at
the University of Toronto, before moving to Edinburgh in 2010.
Iain's research interests include building flexible probabilistic models
of data, and probabilistic inference from indirect and uncertain
observations. Iain is passionate about teaching. He has lectured at
several Summer schools, is listed in the top 15 authors on
videolectures.net, and was awarded the EUSA Van Heyningen Award for
Teaching in Science and Engineering in 2015.
Iain Murray is a SICSA Lecturer in Machine Learning at the University of
Edinburgh. Iain was introduced to machine learning by David MacKay and
Zoubin Ghahramani, both previous NIPS tutorial speakers. He obtained his
PhD in 2007 from the Gatsby Computational Neuroscience Unit at UCL. His
thesis on Monte Carlo methods received an honourable mention for the
ISBA Savage Award. He was a commonwealth fellow in Machine Learning at
the University of Toronto, before moving to Edinburgh in 2010.
Iain's research interests include building flexible probabilistic models
of data, and probabilistic inference from indirect and uncertain
observations. Iain is passionate about teaching. He has lectured at
several Summer schools, is listed in the top 15 authors on
videolectures.net, and was awarded the EUSA Van Heyningen Award for
Teaching in Science and Engineering in 2015.
General Chair
David Blei
Columbia University
David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.
David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.
Program Chair
Hal Daumé
University of Maryland
Program Chair
Aarti Singh
Carnegie Mellon University
Tutorial Chair
Alessandra Tosi
Senior Scientist
Mind Foundry
Tutorial Chair
Jerry Zhu
University of Wisconsin-Madison
Workshop Chair
Mohammad Emtiyaz Khan
Dr.
RIKEN
Workshop Chair
Po-Ling Loh
UW-Madison
Sponsorship Chair
Simon Lacoste-Julien
Associate Professor
UdeM, Mila & Samsung SAIL Montreal
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.
Sponsorship Chair
Caroline Uhler
Assistant Professor
Massachusetts Institute of Technology
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.
Workflow Chair
Ivan Stelmakh
New Economic School
Workflow Chair
Zhenyu (Sherry) Xue
ICML
