Date: Tuesday 25 May 2021 at 15:00 (Europe/London)
Speaker: Professor Shirley Ho, Cosmology X Data Science Group, Flatiron Institute, New York (USA) & Department of Astrophysical Sciences, Princeton University (USA)
Abstract
Scientists have always attempted to identify and document analytic laws that underlie physical phenomena in nature. The process of finding natural laws has always been a challenge that requires not only experimental data, but also theoretical intuition. Often times, these fundamental physical laws are derived from many years of hard work over many generation of scientists. Automated techniques for generating, collecting, and storing data have become increasingly precise and powerful, but automated discovery of natural laws in the form of analytical laws or mathematical symmetries have so far been elusive. Over the past few years, the application of deep learning to domain sciences – from biology to chemistry and physics is raising the exciting possibility of a data-driven approach to automated science, that makes laborious hand-coding of semantics and instructions that is still necessary in most disciplines seemingly irrelevant. The opaque nature of deep models, however, poses a major challenge. For instance, while several recent works have successfully designed deep models of physical phenomena, the models do not give any insight into the underlying physical laws. This requirement for interpretability across a variety of domains, has received diverse responses. In this talk, the group’s analysis is presented which suggests a surprising alignment between the representation in the scientific model and the one learned by the deep model.
Biography
Shirley Ho joined the Flatiron Institute at in 2018 as leader of the Cosmology X Data Science group at the Center for Computational Astrophysics (CCA). Her research interests have ranged from fundamental cosmological measurements to exoplanet statistics to using machine learning to estimate how much dark matter is in the universe. Ho has broad expertise in theory, observation and data science. Ho’s recent interest has been on understanding and developing novel tools in statistics and machine learning techniques, and applying them to astrophysical challenges. Her goal is to understand the universe’s beginning, evolution and its ultimate fate. In her bidding to understand our Universe, Ho plans, builds and analyses data from a number of astronomical surveys such as Actacama Cosmology Telescope, Euclid, the Large Synoptic Survey Telescope, Simons Observatory, Sloan Digital Sky Survey and the Wide Field Infrared Survey Telescope.
Ho earned her Ph.D. in astrophysical sciences from Princeton University in 2008 and her bachelor’s degrees in computer science and physics from the University of California, Berkeley in 2004. She was a Chamberlain fellow and a Seaborg fellow at Lawrence Berkeley National Laboratory before joining Carnegie Mellon University in 2011 as an assistant professor. She became the Cooper Siegel Career Development Chair Professor and was appointed associate professor with tenure in 2016. She moved to Lawrence Berkeley Lab as a Senior Scientist in 2016.
Since 2011, she has been a primary mentor to more than 15 postdoctoral fellows, six graduate students and 14 undergraduates in the fields of astrophysics, computer science and statistics. She plans to continue mentoring future generations of astrophysicists and data scientists at CCA and other institutions.
