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Recommendation systems, being an Applied Scientist & Building a good research career | Mina Ghashami

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Manage episode 341243068 series 2859018
Content provided by Jay Shah. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jay Shah 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://ppacc.player.fm/legal.

Mina Ghashami is an Applied Scientist in the Alexa Video team at Amazon Science alongside being a lecturer at Stanford University. Prior to joining Amazon, she was a Research Scientist at Visa Research working on recommendation systems built on transactions from users and a few other projects. She completed her Ph.D. in Computer Science from the University of Utah followed by a PostDoctoral position at Rutgers University. At Amazon, she is mainly focused on Video-based ranking recommendation systems, something we talk about in detail in this conversation.
Time stamps of the conversation
00:00:50 Introductions
00:01:40 Alexa Video - Ranking and Recommendation research
00:05:25 Feature engineering for recommendation systems
00:08:30 Ground truth for training recommendation systems
00:12:46 What does an Applied Scientist do? (at Amazon)
00:19:17 What got you into AI? And specifically recommendation systems
00:24:30 Matrix approximation
00:27:15 Challenges in recommendation research
00:32:00 What's more interesting, theoretical or applied side of research?
00:37:10 Over parametrization vs generalizability
00:39:55 Managing academic and industry positions at the same time
00:46:26 Should one do a Ph.D. for research roles in the industry?
00:50:00 Skills learned while pursuing a PhD
00:54:22 Deciding industry vs academia
00:56:20 Coping up with research in deep learning
01:02:14 What makes a good research dissertation?
01:04:16 Advice to young students navigating their interest in machine learning
To learn more about Mina:
Homepage: https://mina-ghashami.github.io/
Linkedin: https://www.linkedin.com/in/minaghashami
Research: https://scholar.google.com/citations?user=msJHsYcAAAAJ&hl=en
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/

  continue reading

95 episodes

Artwork
iconShare
 
Manage episode 341243068 series 2859018
Content provided by Jay Shah. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jay Shah 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://ppacc.player.fm/legal.

Mina Ghashami is an Applied Scientist in the Alexa Video team at Amazon Science alongside being a lecturer at Stanford University. Prior to joining Amazon, she was a Research Scientist at Visa Research working on recommendation systems built on transactions from users and a few other projects. She completed her Ph.D. in Computer Science from the University of Utah followed by a PostDoctoral position at Rutgers University. At Amazon, she is mainly focused on Video-based ranking recommendation systems, something we talk about in detail in this conversation.
Time stamps of the conversation
00:00:50 Introductions
00:01:40 Alexa Video - Ranking and Recommendation research
00:05:25 Feature engineering for recommendation systems
00:08:30 Ground truth for training recommendation systems
00:12:46 What does an Applied Scientist do? (at Amazon)
00:19:17 What got you into AI? And specifically recommendation systems
00:24:30 Matrix approximation
00:27:15 Challenges in recommendation research
00:32:00 What's more interesting, theoretical or applied side of research?
00:37:10 Over parametrization vs generalizability
00:39:55 Managing academic and industry positions at the same time
00:46:26 Should one do a Ph.D. for research roles in the industry?
00:50:00 Skills learned while pursuing a PhD
00:54:22 Deciding industry vs academia
00:56:20 Coping up with research in deep learning
01:02:14 What makes a good research dissertation?
01:04:16 Advice to young students navigating their interest in machine learning
To learn more about Mina:
Homepage: https://mina-ghashami.github.io/
Linkedin: https://www.linkedin.com/in/minaghashami
Research: https://scholar.google.com/citations?user=msJHsYcAAAAJ&hl=en
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/

  continue reading

95 episodes

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