David Pfau: Manifold Factorization and AI for Science

Episode 130 I spoke with David Pfau about: * Spectral learning and ML * Learning to disentangle manifolds and (projective) representation theory * Deep learning for computational quantum mechanics * Picking and pursuing research problems and directions David’s work is really (times k for some very large value of k ) interesting—I’ve been inspired to descend a number of rabbit holes because of it. (if you listen to this episode, you might become as cool as this guy) While I’m at it — I’m still hovering around 40 ratings on Apple Podcasts . It’d mean a lot if you’d consider helping me bump that up! Enjoy—and let me know what you think! David is a staff research scientist at Google DeepMind . He is also a visiting professor at Imperial College London in the Department of Physics , where he supervises work on applications of deep learning to computational quantum mechanics. His research interests span artificial intelligence, machine learning and scientific computing. Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :) You can also support upkeep for the full Gradient team/project through a paid subscription on Substack!

You can listen to David Pfau: Manifold Factorization and AI for Science online on Radio and Podcast. Open the player on this page to stream the available audio.

David Pfau: Manifold Factorization and AI for Science is an episode from The Gradient Podcast by Daniel Bashir.

This episode is 120:52 long.

This episode was published on Jul 11, 2024.

Yes. Use the heart button on the episode page to add it to your favorite episodes list.

Yes. This page shows related episodes from The Gradient Podcast when more episodes are available from the podcast feed.