The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Fast Radio Burst Pulse Detection with Gerry Zhang - TWIML Talk #278
Jun 27, 2019
Yunfan Gerry Zhang, a PhD student at UC Berkeley and SETI research center affiliate, dives into the fascinating intersection of machine learning and astrophysics. He discusses his groundbreaking paper on detecting fast radio bursts using innovative techniques. Highlights include the use of Generative Adversarial Networks for predicting cosmic signals and the challenges of processing immense astronomical datasets. Gerry also shares insights on detecting periodicity in signals and the transformative impact of AI in analyzing radio frequency data.
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SETI at Home
- Sam Charrington recalls the SETI at Home project from the early 2000s.
- It distributed signal analysis to millions of users via a downloadable client, like a screensaver.
Machine Learning in SETI
- Machine learning helps SETI search for unknown signal types, unlike traditional match filters.
- This flexible approach allows for broader exploration of potential extraterrestrial signals.
Gerry Zhang's Background
- Yunfan Gerry Zhang's background is in particle physics.
- He transitioned to machine learning after realizing its potential for solving complex problems in astrophysics, particularly SETI.