Machine Learning Meets Statistics

Michael R. Douglas is a theoretical physicist and Professor at Stony Brook University, and Visiting Scholar at Harvard University. His research focuses on string theory, theoretical physics and its relations to mathematics. Michael's PhD thesis is titled, "G/H Conformal Field Theory", which he completed in 1988 at Caltech. We talk about working with Feynman, Sussman, and Hopfield during his PhD days, the superstring revolutions and string theory, and machine learning's role in the future of science and mathematics. - Episode notes: https://cs.nyu.edu/~welleck/episode33.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview