Simon Lacoste-Julien is an associate professor at Mila and DIRO from Université de Montréal, and Canada CIFAR AI Chair holder. He also heads part time the SAIT AI Lab Montreal from Samsung. His research interests are machine learning and applied math, with applications in related fields like computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in 2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.

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.