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.
Research Interests
Personalized Medicine
Random Forests
Reinforcement Learning
Survival Analysis
Sufficient Dimension Reduction
Optimization
Biomedical Research: infectious diseases, food and nutrition, cancer
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:
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
External Links
Recent Publications
Guo, B., Holscher, H. D., Auvil, L. S., Welge, M. E., Bushell, C. B., Novotny, J. A., Baer, D. J., Burd, N. A., Khan, N. A., & Zhu, R. (2023). Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests. Statistics in Biosciences, 15(3), 545-561. https://doi.org/10.1007/s12561-021-09310-w
Sarker, K., Zhu, R., Holscher, H. D., & Zhai, C. X. (2023). Augmenting nutritional metabolomics with a genome-scale metabolic model for assessment of diet intake. In ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics Article 4 (ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics). Association for Computing Machinery. https://doi.org/10.1145/3584371.3612958
Shinn, L. M., Mansharamani, A., Baer, D. J., Novotny, J. A., Charron, C. S., Khan, N. A., Zhu, R., & Holscher, H. D. (2023). Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults. The Journal of nutrition, 152(12), 2956-2965. Article nxac195. https://doi.org/10.1093/jn/nxac195
Zhu, R., Formentini, S. E., & Cui, Y. (2023). Random Forests for Survival Analysis and High-Dimensional Data. In Springer Handbooks (pp. 831-847). (Springer Handbooks). Springer. https://doi.org/10.1007/978-1-4471-7503-2_40
Cui, Y., Zhu, R., Zhou, M., & Kosorok, M. (2022). CONSISTENCY OF SURVIVAL TREE AND FOREST MODELS: SPLITTING BIAS AND CORRECTION. Statistica Sinica, 32(3), 1245-1267. https://doi.org/10.5705/ss.202020.0263