Abstracts from: https://statistics.stanford.edu/events/statistics-seminar

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.

Speaker Bio: Dr. Nachman is a Staff Scientist in the Physics Division at Lawrence Berkeley National Lab (LBNL) where he founded and leads the Machine Learning for Fundamental Physics Group. He was a Churchill Scholar at Cambridge University and then received his Ph.D. in Physics and Ph.D. minor in Statistics from Stanford University. After graduating, he was a Chamberlain Fellow in the Physics Division at LBNL. Nachman Group website