Ed H. Chi
Ed H. Chi is a Distinguished Scientist at Google, leading several machine learning research teams focusing on neural modeling, reinforcement learning, dialog modeling, reliable/robust machine learning, and recommendation systems in Google Brain team. His team has delivered significant improvements for YouTube, News, Ads, Google Play Store at Google with >420 product improvements since 2013. With 39 patents and >150 research articles, he is also known for research on user behavior in web and social media.
Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group, where he led the team in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Recognized as an ACM Distinguished Scientist and elected into the CHI Academy, he recently received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo. See Ed's personal website.
Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group, where he led the team in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Recognized as an ACM Distinguished Scientist and elected into the CHI Academy, he recently received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo. See Ed's personal website.
Authored Publications
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Google
Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems
Shuo Yang
Aniruddh Nath
Yang Liu
Li Wei
Shawn Andrews
Maciej Kula
Jarrod Kahn
Zhe Zhao
Lichan Hong
2024
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Jason Wei
Sharan Narang
Aakanksha Chowdhery
ICLR 2023 (to appear)
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Ben Coleman
Ruoxi Wang
Lichan Hong
Advances in Neural Information Processing Systems (2023), pp. 56234-56255
Improving Training Stability for Multitask Ranking Models in Recommender Systems
Justin Gilmer
Li Wei
Lichan Hong
Mahesh Sathiamoorthy
KDD 2023 (2023)
LaMDA: Language Models for Dialog Applications
Aaron Daniel Cohen
Alena Butryna
Alicia Jin
Apoorv Kulshreshtha
Ben Zevenbergen
Chung-ching Chang
Cosmo Du
Daniel De Freitas Adiwardana
Dehao Chen
Dmitry (Dima) Lepikhin
Erin Hoffman-John
Igor Krivokon
James Qin
Jamie Hall
Joe Fenton
Johnny Soraker
Kathy Meier-Hellstern
Maarten Paul Bosma
Marc Joseph Pickett
Marcelo Amorim Menegali
Marian Croak
Maxim Krikun
Noam Shazeer
Rachel Bernstein
Ravi Rajakumar
Ray Kurzweil
Romal Thoppilan
Steven Zheng
Taylor Bos
Toju Duke
Tulsee Doshi
Vincent Y. Zhao
Will Rusch
Yuanzhong Xu
Zhifeng Chen
arXiv (2022)
Emergent abilities of large language models
Barret Zoph
Colin Raffel
Dani Yogatama
Jason Wei
Liam B. Fedus
Maarten Paul Bosma
Percy Liang
Sebastian Borgeaud
Tatsunori B. Hashimoto
Yi Tay
TMLR (2022)
Learning to Augment for Casual User Recommendation
Elaine Le
Jianling Wang
Yuyan Wang
The ACM Web Conference 2022 (2022)
Surrogate for Long-Term User Experience in Recommender Systems
Can Xu
Lisa Mijung Chung
Mohit Sharma
Qian Sun
Sriraj Badam
Yuyan Wang
KDD 2022 (2022)
HyperPrompt: Prompt-based Task-Conditioning of Transformers
Cosmo Du
Steven Zheng
Vamsi Aribandi
Yi Tay
Yun He
Zhao Chen
Zhe Zhao
ICML (2022)
