Today we’re joined by Yunfan Gerry Zhang, a PhD student in the Department of Astrophysics at UC Berkely, and an affiliate of Berkeley’s SETI research center.
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In our conversation, Gerry details his research in applying machine learning techniques to astrophysics and astronomy. We also discuss his paper “Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach,” which describes the techniques used to detect 72 previously undetected fast radio bursts at the Green Bank Telescope. We explore the types of data sources used for this project, challenges Gerry encountered along the way, the role of GANs and much more.
You are invited to join us for the very first TWIMLcon conference, which will focus on the tools, technologies, and practices necessary to scale the delivery of machine learning and AI in the enterprise. The event will be held October 1st & 2nd in San Francisco and early bird registration is open today at twimlcon.com.
Mentioned in the Interview
- SETI Institute
- Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach
- TWIML Events Page
- TWIML Meetup
- TWIML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0