Dabao Zhang
Biography
Dabao Zhang, Ph.D., is a Professor in the Department of Epidemiology and Biostatistics at the Joe C. Wen School of Population and Public Health at the University of California, Irvine. A statistician specializing in massive data analysis, causal inference, and interpretable AI, he develops statistical and computational methods for genomic/metabolomic data analysis, causal gene regulatory networks (cGRNs), and interpretable prediction models. He received his Ph.D. in Statistical Science from Cornell University in 2003.
Zhang’s methodological work includes high-dimensional variable selection, coefficients of determination for generalized linear and mixed-effects models, causal inference for disease-specific regulatory programs, and methods for quantifying feature contributions in AI/ML models. He received the NSF CAREER Award in 2009 for his work on regularization methods for identifying composite signatures and has served as principal investigator on multiple NIH-funded projects.
He has published extensively in journals and conferences including the Proceedings of the National Academy of Sciences, Journal of the American Statistical Association, and NeurIPS. He has also developed statistical software including rsq for R² measures in generalized linear models, SIGNET for transcriptome-wide cGRN inference, and Q-SHAP for quantifying feature contributions to predictive performance. The rsq package has been downloaded more than 232,300 times.
At UC Irvine, Zhang is also a faculty member of the Chao Family Comprehensive Cancer Center and has mentored graduate students and postdoctoral researchers throughout his academic career at Purdue University and UC Irvine.
Return to topEducation
- PhD in Statistical Science, Cornell University, 2003
- MS in Statistical Science, Cornell University, 2001
- MS in Probability & Statistics, Peking University, 1993
- BS in Mathematical Statistics, Nankai University, 1990
Distinctions
- Research Excellence Award, Joe C. Wen School of Population & Public Health, University of California, Irvone, 2026
- Outstanding Service Award, College of Science, Purdue University, 2023
- CAREER Award, National Science Foundation, 2009
- Interdisciplinary Award, College of Science, Purdue University, 2009
- Liu Memorial Award, Cornell University, 2003
- First Prize Winner of National PC-Software Competition, China, 1995
Areas of Expertise
- Analysis of Large Biomedical Data
- Inference of Causal Gene Regulatory Networks
- Interpretable Machine Learning Models
- Identification of Disease-Specific Gene Regulatory Programs
- Genomic and Metabolomic Data Analysis
Recent Publications
- Liu D., Jiang Z., Kim H., Tukker A.M., Dalvi A., Xie J., Li Y., Yuan C., Bowman A.B., Zhang D., Zhang M., “From correlation to causation: cell-type-specific gene regulatory networks in Alzheimer's disease” (opens in new tab), Alzheimer S and Dementia, vol. 22, 2026.
- Li Y, Alemdjrodo K, Lin Y, Zhang M, Zhang D, “Exploring massive risk factors of categorical outcomes via supervised dimension reduction” (opens in new tab), Journal of Data Science, vol. 23, pp. 607-623, 2025.
- Jiang Z, Zhang D, Zhang M, “Fast calculation of feature-specific contributions in boosting trees”, Proceedings of Machine Learning Research, vol. 244, pp. 1859-1875, 2025. Presented at 41st Conference on Uncertainty in Artificial Intelligence (UAI 2025).
- Dixit A, Chen W, Zhang M, Zhang D, “Prediction interval transfer learning for linear regression using an empirical Bayes approach”, Stat, vol. 14, pp. e70036, 2025.
- Liu D, Gowda G. A. N., Jiang Z, Alemdjrodo K, Zhang M, Zhang D, Raftery D, “Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts” (opens in new tab), Proceedings of the National Academy of Sciences of the United States of America, vol. 121, no. 8, pp. e2307430121, 2024.
- Jiang Z, Zhang D, “Analysis of variance of multiple causal networks”, Advances in Neural Information Processing Systems, vol. 36, pp. 11580-11591, 2023. Presented at 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
- Jiang Z, Chen C, Xu Z, Wang X, Zhang M, Zhang D, “SIGNET: transcriptome-wide causal inference for gene regulatory networks” (opens in new tab), Scientific Reports, vol. 13, pp. 19371, 2023.
- Liu D, Yang Z, Chandler K, Oshodi A, Zhang T, Ma J, Kusumanchi P, Huda N, Heathers L, Perez K, Tyler K, Ross RA, Johnson N, Jiang Y, Zhang D, Zhang M, Liang-punsakul S, “Serum metabolomic analysis reveals several novel metabolites in association with excessive alcohol use - an exploratory study” (opens in new tab), Translational Research, vol. 240, pp. 87-98, 2022.
- Cobb J, Cheny C, Shi Y, Maron L, Liuy D, Rutzke M, Greenberg A, Craft E, Sha J, Paul, Akther K, Wang S, Kochian L, Zhang D, Zhang M, McCouch S, “Genetic architecture of root and shoot ionomes in rice Oryza sativa L.” (opens in new tab), Theoretical and Applied Genetics, vol. 134, pp. 2613-2637, 2021.
Most Cited Publications
- Zhang D, “A Coefficient of determination for generalized linear models” (opens in new tab), The American Statistician, vol. 71, pp. 310-316, 2017.
- Zhang D, Huang X, Regnier FE, Zhang M, “Two-dimensional correlation optimized warping algorithm for aligning GC×GC-MS data” (opens in new tab), Analytical Chemistry, vol. 80, pp. 2664-2671, 2008.
- Zhang HT, Zhang D, Zha ZG, Hu CD, “Transcriptional activation of PRMT5 by NF-Y is required for cell growth and negatively regulated by the PKC/c-Fos signaling in prostate cancer cells” (opens in new tab), Biochimica Et Biophysica Acta Gene Regulatory Mechanisms, vol. 1839, pp. 1330-1340, 2014.
- Zhang D, Wells MT, Peng L, “Nonparametric estimation of the dependence function for a multivariate extreme value distribution” (opens in new tab), Journal of Multivariate Analysis, vol. 99, pp. 577-588, 2008.
- Zhang M, Zhang D, Wells MT, “Variable selection with large p small n regression models: mapping QTL with epistasis” (opens in new tab), BMC Bioinformatics, vol. 9, pp. 251, 2008.
- Zhang M, Montooth KL, Wells MT, Clark AG, Zhang D, “Mapping Multiple Quantitative Trait Loci by Bayesian Classification” (opens in new tab), Genetics, vol. 169, pp. 2305-2318, 2005.
- Zhang D, “Coefficients of determination for mixed-effects models” (opens in new tab), Journal of Agricultural, Biological and Environmental Statistics, vol. 27, pp. 674-689, 2022.
- Chen C, Ren M, Zhang M, Zhang D, “Two-stage penalized least squares method for constructing large systems of structural equations”, Journal of Machine Learning Research, vol. 19, pp. 1-34, 2018.
- Zhang D, Lin Y, Zhang M, “Penalized orthogonal-components regression for large p small n data” (opens in new tab), The Electronic Journal of Statistics, vol. 3, pp. 781-796, 2009.
- Hi Y, Peng L, Zhang D, Zhao Z, “Risk analysis via generalized Pareto distributions” (opens in new tab), Journal of Business and Economic Statistics, vol. 40, pp. 852-867, 2021.
Contact Information
Email: dabao.zhang@uci.edu
Address: Department of Epidemiology & Biostatistics, Wen Public Health, 856 Health Sciences Road
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Last updated on 10/2/2026.