Dr. Aditya Thakur, a computer science professor at U.C. Davis, walks us through his work on developing analysis tools that he wished he had while working in industry at places like Google. Aside from program analysis, we talk about making a research group successful by exposing them to industry. Towards the end, he shares his work on techniques and tools for repairing a trained deep neural network once a mistake has been discovered. Along the way, we learn about things like abstract interpretation, non-determinism, the trickiness of parallelism, and other concepts pertinent to analysis in an approachable way.
You can watch this episode on our Youtube Channel: https://youtube.com/c/BuildingBetterSystemsPodcast
Joey Dodds: https://galois.com/team/joey-dodds/
Shpat Morina: https://galois.com/team/shpat-morina/
Aditya Thakur: http://thakur.cs.ucdavis.edu/
Galois, Inc.: https://galois.com/
Contact us: podcast@galois.com
The podcast Building Better Systems is embedded on this page from an open RSS feed. All files, descriptions, artwork and other metadata from the RSS-feed is the property of the podcast owner and not affiliated with or validated by Podplay.