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

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Content provided by James Le. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by James Le 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.

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

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email khanhle.1013@gmail.com.

Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

  continue reading

133 episodes

Artwork
iconShare
 
Manage episode 274260349 series 2593033
Content provided by James Le. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by James Le 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.

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

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email khanhle.1013@gmail.com.

Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

  continue reading

133 episodes

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