Kehang Han

Kehang Han

I joined Google Brain as an AI Resident in 2020 and now am a Research Engineer there. My research focus has been Graph Neural Networks and Bayesian methods to make deep learning models more reliable (e.g., reduce overconfidence, improve calibration) under distributional shift. Before that I was a senior data scientist at Staples working on Operations Research and Internet of Things. I obtained PhD from MIT on Chemical Engineering and Computation.
Authored Publications
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    Google
Plex: Towards Reliability using Pretrained Large Model Extensions
Du Phan
Mark Patrick Collier
Zi Wang
Zelda Mariet
Clara Huiyi Hu
Neil Band
Tim G. J. Rudner
Karan Singhal
Joost van Amersfoort
Andreas Christian Kirsch
Rodolphe Jenatton
Honglin Yuan
Kelly Buchanan
D. Sculley
Yarin Gal
ICML 2022 Pre-training Workshop (2022)
Improving Hit-finding: Multilabel Neural Architecture with DEL
Steven Kearnes
AI for Science NeurIPS 2021 workshop (2021)