Kaze Wong, Flatiron Institute, CCA
When
Where
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.
MEETING DETAILS |
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Time: 3:30 PM MST/10:30 PM UTC Location: Kuiper Space Sciences Building, Room 308 Reception: Refreshments at 3:00 PM, Kuiper Building, 3rd Floor Atrium Live stream: Zoom Meeting ID: 417 674 3144 Passcode: 1985Astro Watch later: TAP YouTube Channel |
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.
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