Jiaxin Shi
Research Scientist at Google DeepMind
📍 New York, New York, United States
Jiaxin Shi is a Research Scientist at Google DeepMind specializing in probabilistic machine learning. His research focuses on developing generative and algorithmic models, particularly adapting diffusion models for discrete data like text. He holds a PhD in Computer Science from Tsinghua University and was a postdoctoral scholar at Stanford and Microsoft Research.
His co-authored paper on gradient estimation received an Outstanding Paper Award at NeurIPS 2022, a top-tier AI conference.
Read the full overview →Experience
5 Years
Current Role
Research Scientist
Location
New York, New York, United States
Personality Overview
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Priorities
Topics Jiaxin cares about
Generative AI Models
His core research involves unifying generative models for discrete and continuous data, developing novel methods like "masked diffusion models".
Probabilistic Machine Learning
His work is grounded in probabilistic methods, including variational inference and score-based modeling, to capture uncertainty in complex data.
Career
Work history
3-2023
Research Scientist
Google DeepMind
7-2022 - 3-2023
Postdoctoral Scholar
Stanford University
8-2020 - 6-2022
Postdoctoral Researcher
Microsoft Research New England
6-2019 - 9-2019
Research Intern
DeepMind
7-2018 - 9-2018
Research Intern
RIKEN
In the press
Media appearances
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Education
2015 - 2020
Doctor of Philosophy - PhD
Tsinghua University
2011 - 2015
Bachelor's degree
Tsinghua University
Social presence
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