DiscoverJay Shah PodcastIntuition for research in Social Reinforcement Learning | Natasha Jacques
Intuition for research in Social Reinforcement Learning | Natasha Jacques

Intuition for research in Social Reinforcement Learning | Natasha Jacques

Update: 2021-07-19
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How can we build intuition for interdisciplinary fields in order to tackle challenges in social reinforcement learning?

Natasha Jaques is currently a Research Scientist at Google Brain and a post-doc fellow at UC Berkeley, where her research interests are in designing multi-agent RL algorithms while focusing on social reinforcement learning. She received her Ph.D. from MIT and has also received multiple awards for her research works submitted to venues like ICML and NeurIPS She has interned at DeepMind, Google Brain, and is an OpenAI  Scholars mentor.

About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/

You can reach out to 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.***

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Intuition for research in Social Reinforcement Learning | Natasha Jacques

Intuition for research in Social Reinforcement Learning | Natasha Jacques

Jay Shah, Natasha Jacques