Speaker(s):Benjamin Nachman, Staff Scientist, Lawrence Berkeley National Laboratory
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.