Fast Radio Burst Pulse Detection with Gerry Zhang - TWIML Talk #278

This article summarizes a discussion with Yunfan Gerry Zhang, a PhD student at UC Berkeley and SETI research affiliate. The conversation focuses on Zhang's research applying machine learning to astrophysics and astronomy. The primary focus is on his paper, "Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach." The discussion covers data sources, challenges faced, and the use of Generative Adversarial Networks (GANs). The article highlights the intersection of AI and scientific discovery, specifically in the context of radio astronomy and the search for extraterrestrial intelligence.

• •Gerry Zhang's research applies machine learning to analyze fast radio bursts.
• •The project uses machine learning to detect pulses and identify periodicity in radio burst data.
• •The research explores the use of GANs in astrophysics.