Jun Zhu
Jun Zhu develops statistical machine learning methods for complex scientific and engineering data. Zhu joined Tsinghua in 2011 after work as a postdoctoral researcher and project scientist at Carnegie Mellon. His research covers probabilistic learning, Bayesian methods, deep learning, and reinforcement learning. His team’s Analytic-DPM paper received an ICLR Outstanding Paper Award in 2022. The paper studies an analytic estimate of reverse variance in diffusion probabilistic models.
- Now at
- Tsinghua University
- Works
- 394
- Citations
- 24,073
- h-index
- 62
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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2011
Joining Tsinghua
Zhu joined Tsinghua in 2011 after work as a postdoctoral researcher and project scientist at Carnegie Mellon. His research covers probabilistic learning, Bayesian methods, deep learning, and reinforcement learning.
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2022
Diffusion model research
His team’s Analytic-DPM paper received an ICLR Outstanding Paper Award in 2022. The paper studies an analytic estimate of reverse variance in diffusion probabilistic models.
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2023
A probabilistic learning book
Zhu published a Chinese-language book titled Probabilistic Machine Learning in July 2023. The book was based on his Tsinghua machine learning courses.
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2024
Research recognition
AAAI elected Jun Zhu as a Fellow in 2024.
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As of October 2026
Open research tools
Zhu’s group released tools including Tianshou for deep reinforcement learning, ZhuSuan for Bayesian deep learning, and DPM-Solver for diffusion models.
Sources and credits
Photo: tsaf.cas.cn; Jun Zhu--TAN KAH KEE SCIENCE AWARD FOUNDATION; creator not stated. License: Not stated by source. Source: tsaf.cas.cn. Cropped to a square and resized. Ask us to remove or replace this photo.