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.
Jiaxin Shi
Research Scientist at Google DeepMind
New York, New York, United States
Overview
Topics They Care 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.
AI for Language
He applies his research to language, training large-scale models that have surpassed previous diffusion model benchmarks on text-based tasks.
Media Appearances
Jiaxin has no verified media appearances
Work History
3-2023
Research Scientist at Google DeepMind
7-2022 - 3-2023
Postdoctoral Scholar at Stanford University
8-2020 - 6-2022
Postdoctoral Researcher at Microsoft Research New England
6-2019 - 9-2019
Research Intern at DeepMind
7-2018 - 9-2018
Research Intern at RIKEN
Education
2015 - 2020
Doctor of Philosophy - PhD from Tsinghua University
2011 - 2015
Bachelor's degree from Tsinghua University
More Information
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Location : New York, New York, United States
Job Level : N/A
Designation : Research Scientist at Google DeepMind
