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#7: Aditya Thakur – “If it goes too slow, they'll turn it off”: Analysis Tools That Work

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Manage episode 288985734 series 2824530
Content provided by Galois Inc., Joey Dodds, and Shpat Morina. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Galois Inc., Joey Dodds, and Shpat Morina or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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

  continue reading

22 episodes

Artwork
iconShare
 
Manage episode 288985734 series 2824530
Content provided by Galois Inc., Joey Dodds, and Shpat Morina. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Galois Inc., Joey Dodds, and Shpat Morina or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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

  continue reading

22 episodes

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