Josh explains how astronomy and machine learning have informed each other, their current limitations, and where their intersection goes from here.

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Josh is a Professor of Astronomy and Chair of the Astronomy Department at UC Berkeley. His research interests include the intersection of machine learning and physics, time-domain transients events, artificial intelligence, and optical/infared instrumentation.

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⏳ Timestamps:
0:00 Intro, sneak peek
1:15 How astronomy has informed ML
4:20 The big questions in astronomy today
10:15 On dark matter and dark energy
16:37 Finding life on other planets
19:55 Driving advancements in astronomy
27:05 Putting telescopes in space
31:05 Why Josh started using ML in his research
33:54 Crowdsourcing in astronomy
36:20 How ML has (and hasn't) informed astronomy
47:22 The next generation of cross-functional grad students
50:50 How Josh started coding
56:11 Incentives and maintaining research codebases
1:00:01 ML4Science's tech stack
1:02:11 Uncertainty quantification in a sensor-based world
1:04:28 Why it's not good to always get an answer
1:07:47 Outro

🌟 Show notes:
- http://wandb.me/gd-josh-bloom 

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The link between astronomy and ML with Josh Bloom, Chair of Astronomy at UC Berkeley

Brought to you by the folks at Weights & Biases, Gradient Dissent is a weekly machine learning podcast that takes you behind-the-scenes to learn how industry leaders are putting deep learning models in production at Facebook, Google, Lyft, OpenAI, Salesforce, iRobot, Stanford and more.

The link between astronomy and ML with Josh Bloom, Chair of Astronomy at UC Berkeley

Gradient Dissent - a Machine Learning Podcast