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.

Virginia Smith is an assistant professor in the Machine Learning Department at Carnegie Mellon University, and a courtesy faculty member in the Electrical and Computer Engineering Department. Her research interests span machine learning, optimization, and distributed systems. Prior to CMU, Virginia was a postdoc at Stanford University, received a Ph.D. in Computer Science from UC Berkeley, and obtained undergraduate degrees in Mathematics and Computer Science from the University of Virginia.

Andrew Gordon Wilson is faculty in the Courant Institute and Center for Data Science at NYU. His interests include probabilistic modelling, Gaussian processes, Bayesian statistics, physics inspired machine learning, and loss surfaces and generalization in deep learning. His webpage is https://cims.nyu.edu/~andrewgw.

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.

Kiri Wagstaff is serving as a AAAS Congressional Fellow in Artificial Intelligence for Senator Mark Kelly in Washington D.C. She also teaches classes at Oregon State University. She previously worked at the NASA Jet Propulsion Laboratory, where she applied machine learning for space exploration. She published "Machine Learning that Matters" at ICML 2012 and is delighted to chair the new Position Paper Track at ICML 2024. She is passionate about keeping machine learning efforts relevant to our society's needs.

I've recently started as a core professor at Mila, after 3 wonderful years at University of Toronto. I'm actively recruiting students and postdocs!

In general I'm concerned and passionate about AI ethics, safety, and the application of ML to environmental management, health, and social welfare. I think we need to do a much better job of the technosocial engagement necessary to develop AI safely and responsibly -- if you have good ideas about how to do that, let's chat!

Amin Karbasi is currently a chief scientist at Robust Intelligence and an associate professor (on leave) at Yale University. Before that, he was a staff research scientist at Google. He has been the recipient of the National Science Foundation (NSF) Career Award, Office of Naval Research (ONR) Young Investigator Award, Air Force Office of Scientific Research (AFOSR) Young Investigator Award, DARPA Young Faculty Award, National Academy of Engineering Grainger Award, Amazon Research Award, Nokia Bell-Labs Award, Google Faculty Research Award, Microsoft Azure Research Award, Simons Research Fellowship, and ETH Research Fellowship. His work has also been recognized with a number of paper awards, including Graphs in Biomedical Image Analysis (GRAIL), Medical Image Computing and Computer Assisted Interventions Conference (MICCAI), International Conference on Artificial Intelligence and Statistics (AISTATS), IEEE ComSoc Data Storage, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), ACM SIGMETRICS, and IEEE International Symposium on Information Theory (ISIT). His Ph.D. thesis received the Patrick Denantes Memorial Prize from the School of Computer and Communication Sciences at EPFL, Switzerland.

PhD student at Mila and the University of Montreal. Working on non-convex constrained optimization for machine learning under the supervision of Simon Lacoste-Julien.