Mingyuan Zhou
Scholar

Mingyuan Zhou

Google Scholar ID: LXwCIisAAAAJ
Professor, University of Texas at Austin
Machine LearningDeep LearningGenerative AIBayesian Statistics
Homepage↗Google Scholar↗
Citations & Impact
All-time
Citations
8,335
 
H-index
45
 
i10-index
124
 
Publications
20
 
Co-authors
48
list available
Contact
CVOpen ↗GitHubOpen ↗
Publications
2 items
REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge
2026
Cited
0
Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling
2026
Cited
0
Resume (English only)
Academic Achievements
  • - Received the 2022-2023 Research Excellence Award for Associate Professors from the McCombs School of Business in April 2023
  • - Published multiple papers at top conferences such as NeurIPS 2023 and ICML 2023
  • - Developed state-of-the-art diffusion distillation methods (SiD and SiD-LSG) and made the source code publicly available
  • - Released the LEGO-diffusion multi-scale diffusion modeling framework
  • - Made the report and code for RPO, a method for fine-tuning LLMs under both paired and unpaired settings, publicly available
  • - Supervised multiple Ph.D. students who have successfully defended their theses and secured positions at companies like Apple and Google or pursued academic careers
Research Experience
  • - Professor at The University of Texas at Austin
  • - Visiting Faculty Researcher at Google and Google DeepMind
  • - Specializes in probabilistic machine learning, particularly in generative AI
  • - Served as Area Chair for conferences like ICLR, ICML, and NeurIPS
  • - Research supported by NSF, NIH, TACC, Apple, Microsoft, and Google
Education
  • - Ph.D.: 2013, Duke University, Advisor: Dr. Lawrence Carin
  • - Master's: 2008, Chinese Academy of Sciences
  • - B.Sc.: 2005, Nanjing University
Background
  • - Research Interests: Probabilistic machine learning, with a current emphasis on advancing generative AI
  • - Professional Fields: Probabilistic methods, Bayesian analysis, approximate inference, generative models, deep neural networks, reinforcement learning
  • - Introduction: Currently a Professor at The University of Texas at Austin, previously worked as a Visiting Faculty Researcher at Google and Google DeepMind. Primarily affiliated with the Statistics Group at McCombs School of Business, and also serves as core faculty in the Department of Statistics and Data Sciences (SDS) and is a core member of the Machine Learning Laboratory.
Miscellany
  • - Welcomes applications to the Ph.D. in Statistics within the IROM department or the Ph.D. in Use-Inspired AI within the IROM department
  • - Welcomes applications to the statistics and data science Ph.D. program of the SDS department
Co-authors
48 total
Bo Chen
Bo Chen
Professor at School of Electronic Engineering, Xidian University
Huangjie Zheng
Huangjie Zheng
Apple, Machine Learning Research
Lawrence Carin
Lawrence Carin
Duke University
Zhendong Wang
Zhendong Wang
University of Texas at Austin
Xiaoning Qian
Xiaoning Qian
Texas A&M University
Zhibin Duan
Zhibin Duan
Assistant professor at Xi'an jiaotong university
Chaojie Wang
Chaojie Wang
AI Researcher, Skywork AI
Shujian Zhang
Shujian Zhang
University of Texas at Austin