This week, Dr. Wooseok Ha, postdoc with the Foundations of Data Analysis institute, will be giving an introduction to graph theory and present an application to the estimation of population migration patterns from genetic data (https://www.nature.com/articles/ng.3464). Full details will be posted here: https://www.benty-fields.com/manage_jc?groupid=191.
The Berkeley Statistics and Machine Learning Discussion Group meets weekly to discuss current applications across a wide variety of research domains and software methodologies. Register here to view, propose and vote for this group's upcoming discussion topics. All interested members of the UC Berkeley and LBL communities are welcome and encouraged to attend. Questions may be directed to François Lanusse.
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.
Uroš Seljak is a professor in UC Berkeley's Physics and Astronomy departments, a Senior Scientist at LBNL in the Physics Division, as well as a co-director of Berkeley Center for Cosmological Physics. His main research is in cosmology, where he combines theoretical, numerical, and data analysis methods to investigate the universe properties using cosmological observations, from cosmic microwave background to present day galaxy and dark matter distributions. His recent work combines statistics, numerical optimization, and N-body simulation methods to analyze large cosmological surveys, both space based (WMAP, Planck, Euclid, WFIRST) and ground based (SDSS, DESI, LSST). At Berkeley he teaches a course on Bayesian statistics and data science in Physics department. He has a PhD from MIT, was a postdoctoral fellow at Harvard and a faculty at Princeton University and Zurich University prior to Berkeley. He is a recipient of Sloan and Packard Fellowships, NSF CAREER award, and the Warner Prize of the American Astronomical Society.
Chirag Modi a fifth year physics graduate student at Berkeley Center for Cosmological Physics. His research focuses on large-scale structures (LSS) of the Universe. With his adviser, Uros Seljak, he is working on developing new ways to forward model LSS survey observables using machine learning models, such as neural networks, and then to use these models for the reconstruction of initial conditions for our Universe.