Keynote Speakers
Julie Josse
Inria-Inserm Premedical team, Montpellier, France
Bio
Julie Josse is a senior researcher at Inria, leading the PreMeDICaL team with Inserm. Their work advances personalized medicine through causal learning and federated methods that preserve data confidentiality, aiming to accelerate targeted therapies and decision-support tools with quantified uncertainty. Julie Josse’s expertise includes missing data, causal inference, and machine learning on multi-source, multi-modal health data, enhancing decisions in respiratory disease, oncology, and fertility. She led the Traumatrix project, creating decision-support tools for ambulance trauma care optimization. Prior to Inria, she was a professor at Ecole Polytechnique (Institut Polytechnique de Paris – IP Paris), directing the Data Science for Business Master’s with HEC Paris. She has also held visiting roles at Stanford University and Google Brain Paris and has been recognized with honors such as the Inria–French Academy of Sciences Young Researchers Prize.
Lester Mackey
Microsoft Research New England, United States
Bio
Lester Mackey is a Senior Principal Researcher at Microsoft Research, where he develops machine learning methods, models, and theory for large-scale learning tasks driven by applications from meteorology, healthcare, and the social good. Lester moved to Microsoft from Stanford University, where he was an assistant professor of Statistics and, by courtesy, of Computer Science. He earned his PhD in Computer Science and MA in Statistics from UC Berkeley and his BSE in Computer Science from Princeton University. He co-organized the second place team in the Netflix Prize competition for collaborative filtering; won the Prize4Life ALS disease progression prediction challenge; won prizes for temperature and precipitation forecasting in the yearlong real-time Subseasonal Climate Forecast Rodeo; and received best paper, outstanding paper, and best student paper awards from the ACM Conference on Programming Language Design and Implementation, the Conference on Neural Information Processing Systems, and the International Conference on Machine Learning. He is a 2023 MacArthur Fellow, a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, an elected member of the COPSS Leadership Academy, and the recipient of the 2023 Ethel Newbold Prize and the 2025 COPSS Presidents' Award.
Julia Stoyanovich
New York University, United States
Bio
Dr. Julia Stoyanovich is the Institute Associate Professor of Computer Science and Engineering, Associate Professor of Data Science, and Director of the Center for Responsible AI (https://r-ai.co) at New York University. Her goal is to make “responsible AI” synonymous with “AI.” She pursues this goal through academic research, education, technology policy, and public engagement, regularly speaking about both the benefits and the risks of AI. Her research spans data management and AI systems, as well as the ethics and governance of AI. Julia holds an M.S. and Ph.D. in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics & Statistics from the University of Massachusetts Amherst. She is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and is a Senior Member of the Association for Computing Machinery (ACM).
Last updated: February 12, 2026 11:31 (UTC)
Sponsors: TBD