The aim of this meeting is to gather researchers from across the Bay Area interested in Likelihood-Free Inference and Density Estimation for the Physical Sciences. This small-scale meeting will provide a forum to exchange problems, ideas, and techniques while leaving plenty of time for discussion and collaboration/hacking. Among the topics we would like to cover are techniques for likelihood-free and simulation-based inference, active learning/sampling, density estimation, uncertainty quantification and any applications of these techniques to problems in the physical sciences.
Registration is free; pre-registration is required due to space limitations.
Registration deadline: Wednesday, November 27
ChangHoon Hahn, Berkeley Center for Cosmological Physics, UC Berkeley
Francois Lanusse, Foundation of Data Analysis Institute, UC Berkeley
Phil Marshall, Kavli Institute for Particle Astrophysics and Cosmology, Stanford University
Benjamin Nachman, Lawrence Berkeley National Laboratory
While at UC Berkeley, François Lanusse was a Data Science Fellow at BIDS and a Postdoctoral Scholar with the Berkeley Center for Cosmological Physics (BCCP) and the Foundations of Data Analysis (FODA) Institute, exploring the intersection between cosmology, statistics, and machine learning. His research was focused on measuring and exploiting the gravitational lensing effect (in which distant galaxies appear distorted due to the presence of massive structures along the line of sight) with the development of novel tools and methodologies based on sparse signal representations, convex optimization, and deep learning. Before joining Berkeley, Dr. Lanusse worked as a postdoctoral researcher within the McWilliams Center for Cosmology at Carnegie Mellon University, after completing a PhD in astrophysics at CEA Saclay near Paris.