Thematic Quarter on Causality - Opening Session

Antoine Bertoncello is the head of Next Generation AI at TotalEnergies. He holds a doctoral degree from Stanford University and Engineering degree from ENSG-Nancy. At TotalEnergies, his team aims to develop causal discovery and inference methods for renewable energy applications.

David Blei is a professor of Statistics and Computer Science at Columbia University. He is also a member of the Columbia Data Science Institute. He works primarily in the fields of machine learning and Bayesian statistics.

Caleb H. Miles is an assistant professor in the Department of Biostatistics at the Columbia University Mailman School of Public Health. He works on developing semi-parametric methods for causal inference and applying them to problems in public health. His applied work has largely been in HIV/AIDS, and he has more recently begun to work on psychiatric applications. His current methodological research interests include causal inference, its intersection with machine learning, mediation analysis, interference, and measurement error.

Jonas Peters is a professor of statistics at ETH Zurich. Previously, he has been a professor at the Department of Mathematical Sciences at the University of Copenhagen. He studied Mathematics at the University of Heidelberg and the University of Cambridge and obtained his PhD jointly from MPI and ETH. He is interested in inferring causal relationships from different types of data and in building statistical methods that are robust with respect to distributional shifts. In his research, Peters seeks to combine theory, methodology, and applications. His work relates to areas such as computational statistics, causal inference, graphical models, independence testing or high-dimensional statistics.

Thomas Richardson is a professor in the Department of Statistics. He is also an adjunct professor in the Departments of Economics and Electrical Engineering and a member of the eScience Steering Committee. He received his BA from the University of Oxford and his MS and PhD from Carnegie Mellon University. He is a Fellow of the Center for Advanced Studies in the Behavioral Sciences at Stanford University. His research interests include Graphical Models and Causality.

Nataliya Sokolovska is a full professor at Sorbonne University. She holds a doctoral degree from Telecom ParisTech. Her research interests include graphical models, probabilistic inference, causal inference, interpretable models, semi-supervised learning, reinforcement learning, and game theory.