Bay Area Likelihood-Free Inference Meeting


December 2, 2019
9:00am to 5:00pm
190 Doe Library
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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

Organizing Committee
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


François Lanusse

Postdoctoral Scholar, Berkeley Center for Cosmological Physics

I am a Data Science Fellow at BIDS and the Berkeley Center for Cosmological Physics exploring the intersection between Cosmology, Statistics, and Machine Learning. My research has been 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.

I am an active member of the Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration which aims at answering pressing questions about the nature of Dark Energy, a force thought to drive the accelerated expansion of the Universe. LSST will observe billions of galaxies over a period of ten years, measuring in particular the lensing effect in great details to constrain cosmological models. The unprecedented scale and complexity of these modern cosmological surveys involve a number of outstanding challenges which drive most of my research into new methodologies impacting different stages of the science analysis, from image processing to the statistical inference of cosmological parameters.

Before joining Berkeley I have been working 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 in 2015.