University of Arizona, Theoretical Astrophysics Program (TAP) Colloquia Series
TITLE
Why is machine learning for fundamental science different?
ABSTRACT
Machine learning and artificial intelligence are very trendy topics in virtually every part of modern society now. However, the tremendous success seen in the broader society does not seem to transfer to fundamental science as much as we hope it to be. What are the reasons behind such differences? And how should we think of using machine learning in fundamental science research? In this talk, I will illustrate some of my opinions on this through some successes and failures the gravitational wave community had in applying machine learning methods.
BIO
Kaze W. K. Wong is a Flatiron research fellow at the Center for Computation Astrophysics, Flatiron Institute. His research primarily focuses on developing machine learning-enhanced algorithms and software for astrophysics. He earned his Ph.D. from Johns Hopkins University in physics and astronomy in 2021 and his bachelor degree from The Chinese University of Hong Kong in 2017. He was awarded the GWIC Thesis Prize for his distinct contribution to building Machine-learning-enhanced tools for gravitational-wave data analysis.
