Charles Sutton is a Research Scientist at Google Brain and an Associate Professor at the University of Edinburgh. His research focuses on deep learning for generating code and helping people write better programs.

Charles' PhD thesis is titled "Efficient Training Methods for Conditional Random Fields", which he completed in 2008 at UMass Amherst. We start with his work in the thesis on structured models for text, and compare/contrast with today's large language models. From there, we discuss machine learning for code & the future of language models in program synthesis.

- Episode notes: https://cs.nyu.edu/~welleck/episode42.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

[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields

Each episode of The Thesis Review is a conversation centered around a researcher's PhD thesis, giving insight into their history, revisiting older ideas, and providing a valuable perspective on how their research has evolved (or stayed the same) since.