Podcasts Technology Datacast Episode 44: Computer Systems, Machine Learning Security Research, and Women in Tech with Shreya Shankar

Episode 44: Computer Systems, Machine Learning Security Research, and Women in Tech with Shreya Shankar

Published: Oct. 12, 2020, 2 p.m.

Show Notes

  • (2:02) Shreya discussed her initial exposure to Computer Science and her favorite CS course on Advanced Topics in Operating Systems at Stanford.
  • (4:07) Shreya emphasized the importance of distilling technical concepts to a non-technical audience, thanks to her experience as a section leader and teaching assistant for CS198.
  • (6:26) Shreya shared the lack of representation in technical roles that keep women away from considering technology as a career path, and the initiative she was involved with at SHE++.
  • (9:40) Shreya reflected on her software engineering internship experience at Facebook, working on Civic Engagement tools to help representatives connect with their constituents.
  • (12:33) Shreya went over the anecdote of how she worked on Machine Learning Security research at Google Brain.
  • (15:36) Shreya unpacked the paper “Adversarial Examples That Fool Both Computer Vision and Time-Limited Humans,” - where her team constructs adversarial examples that transfer computer vision models to the human visual system.
  • (20:08) Shreya reflected on the lessons learned from her experience working with seasoned researchers at Google Brain.
  • (23:31) Shreya gave her advice for engineers who are interested in multiple specializations.
  • (25:34) Shreya provided resources on the fundamentals of computer systems.
  • (27:15) Shreya explained her reason to work at an early-stage startup right after college (check out the blog post on her decision-making process).
  • (28:41) Shreya was the first ML Engineer at Viaduct, a startup that develops end-to-end machine learning and data analytics platform to empower OEMs to manage, analyze, and utilize their connected vehicle data.
  • (32:27) Shreya discussed two common misconceptions people have about the differences between machine learning in research and practice (read her reflection on one-year of making ML actually useful).
  • (35:24) Shreya expanded on the organizational silo challenge that hinders collaboration between data scientists and software engineers while designing a machine learning product.
  • (40:48) Shreya has been quite open about the challenge of recruiting female engineers, explaining that it is hard to sell women candidates when their alternatives are “conventionally sexy."
  • (47:24) Shreya and a few others have developed and open-sourced GPT-3 Sandbox, a library that helps users get started with the GPT-3 API.
  • (51:52) Shreya explained her prediction on why OpenAI can be the AWS of modeling.
  • (54:24) Shreya shared the benefits of going to therapy to cope with mental illness challenges.
  • (58:36) Closing segment.

Her Contact Info

Her Recommended Resources