Contact Information
137 CAB
Champaign, IL 61820
Biography
Ruoqing Zhu completed his Ph.D. in Biostatistics from the University of North Carolina, Chapel Hill in 2013. From 2013 to 2015, he worked as a Postdoctoral Associate in the Department of Biostatistics at Yale University. He joined the Department of Statistics at UIUC in 2015. Besides this primary appointment, he is an inaugural member of the new "Engineering-Based" Carle Illinois College of Medicine. He was a Faculty Fellow at the National Center for Supercomputing Applications and affiliated with the Center for Genomic Diagnostics and the Personalized Nutrition Initiative at the Carl R. Woese Institute for Genomic Biology. He is also an Advisory Board member of Prenosis Inc.
Research Interests
I have a broad interest in developing statistical methodology, theory, and computational algorithms for decision-making problems, particularly in personalized medicine and reinforcement learning. Many statistical and machine learning methods face barriers to practical adoption because of unrealistic assumptions, unstable performance, limited interpretability, and challenges posed by small sample sizes, high dimensionality, distributional shift, and complex data structures. Addressing these issues is an important and exciting direction in statistical research.
My recent work focuses on uncertainty quantification, distributional shift, causal inference, and trustworthy decision making, with the goal of developing reliable and practical methods for real-world applications. I also work on classical statistical learning and machine learning, including random forests, sufficient dimension reduction, and survival analysis, with applications in bioinformatics, infectious diseases, and nutrition science. More recently, I have been developing open-source agentic AI tools and workflows to support research pipelines, scientific discovery, teaching, and learning, with an emphasis on reliability, transparency, and meaningful human direction.
Education
PhD, Biostatistics, University of North Carolina at Chapel Hill, 2013
MA., Statistics, Bowling Green State University, 2008
B.S., Mathematics, Nanjing University, 2006
B.S., Financial Engineering, Nanjing University, 2005
Courses Taught
At Department of Statistics:
STAT546, STAT542, STAT432, STAT420, STAT400, CS598
At Carle Illinois College of Medicine (co-teaching):
Data Science Project, Foundations: Molecules to Populations
Additional Campus Affiliations
Carle Illinois College of Medicine
- Curriculum Oversight Committee, 2017-2020,
- Course Associate Director, 2017-
National Center for Supercomputing Applications
- Faculty Fellow, 2018-2019 and 2020-2021
Carl R. Woese Institute for Genomic Biology
- Steering Committee of Personalized Nutrition Initiative, 2022-
- Center for Genomic Diagnostics
Division of Nutritional Sciences
External Links
Recent Publications
Li, Y., Han, E., Hu, Y., Zhou, W., Qi, Z., Cui, Y., & Zhu, R. (2026). Reinforcement Learning with Continuous Actions Under Unmeasured Confounding. Journal of the American Statistical Association, 121(553), 209-222. https://doi.org/10.1080/01621459.2025.2590175
Southey, N. L., Zhu, R., & Holscher, H. D. (2026). Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations. Journal of Nutrition, 156(6), Article 101528. https://doi.org/10.1016/j.tjnut.2026.101528
Watson, G. L., Staples, G., Carver, R., Bhargava, A., López-Espina, C., Schmalz, L., Ali, F., Antkowiak, P. S., Azad, S., Berghea, R., Chawla, L., Crisp, M., Dagan, A., Davila, F., Davila, H., DeMarco, C., Doodlesack, A., Espinosa, A., Evans, N. S., ... Shapiro, N. I. (2026). Interpretability of an FDA-authorized AI/ML sepsis diagnostic tool improved by SHAP values. JAMIA Open, 9(1), Article ooag020. https://doi.org/10.1093/jamiaopen/ooag020
Watson, G. L., Updike, L. C., López-Espina, C. G., Bhargava, A., Schmalz, L. A., Khan, S., Urdiales, D. S., Sims, M. D., Palagiri, A. V., Haimovich, A. D., Dagan, A., Davis, B. P., White, K. C., Gurbel, P. A., Mayer, S. M., Syed, A., Zhao, S. D., Zhu, R., Bashir, R., ... Reddy, B. (2026). The Sepsis ImmunoScore Predicts Sepsis, Mortality, and Deterioration Better than Clinical Scores and Widely Available Biomarkers. Diagnostics, 16(13). https://doi.org/10.3390/diagnostics16131962
Cao, X., Li, H., Xu, Q., & Zhu, R. (2025). Detecting Gender Stereotype Biases Against Women Entrepreneurs in Large Language Models. Journal of Business Ethics. Advance online publication. https://doi.org/10.1007/s10551-025-06216-1