CSE DSI Machine Learning Seminar with Benjamin Nachman (LBNL)

Phystatistics: The Rise of the Data Physicist

Across the physical sciences, there has been a shift in paradigm from a theory-driven to a data-driven era. In this new regime, we let the data speak for themselves by using modern machine learning tools unimaginable prior to the deep learning revolution of the last decade. At the same time, the physical sciences face unique challenges that require dedicated solutions to maximize the potential for discovery. Now, more than ever, we need a new kind of researcher - a phystatistician (like biostatistician) or a data physicist (like data scientist). In this talk, I’ll describe unique challenges faced by phystatisticians and how innovative, reproducible, and scalable methodologies and scientific software are enabling researchers to harness the power of modern machine learning for discoveries in the physical sciences.

Nachman received a Ph.D. in Physics and Ph.D. minor in Statistics from Stanford University in 2016.  He then became a Chamberlain Fellow in the Physics Division at Lawrence Berkeley National Laboratory (LBNL) before becoming a staff scientist in 2020.  Currently, Nachman leads the Machine Learning for Fundamental Physics Group in the Physics Division at LBNL.