Curtis M. Tatsuoka, PhD
- Academic Title: Professor
- Primary Appointment: Epidemiology & Public Health
- Administrative Title: Director, University of Maryland Marlene & Stewart Greenebaum Comprehensive Cancer Center Biostatistics Shared Resource Center
Education and Training
PhD, Statistics, Cornell University
MA, Mathematics, UCLA
BS, Statistics, University of Illinois
Biosketch
Dr. Curtis Tatsuoka is Professor in the Department of Epidemiology and Public Health at the University of Maryland School of Medicine and Director of the Biostatistics Shared Resource at the Greenebaum Comprehensive Cancer Center. He is a biostatistician with expertise in advanced statistical methods and their application to complex biomedical and population health research. Dr. Tatsuoka provides leadership in quantitative sciences for cancer research, supporting multidisciplinary teams across basic, translational, and clinical oncology. His work contributes to the design and analysis of cancer studies, including clinical trials, biomarker discovery, and high-dimensional data analysis. Through his role at the Cancer Center, he collaborates with investigators to apply and/or develop innovative analytic approaches that strengthen study design, data interpretation, and overall research impact. See:
https://research.umgccc.org/shared-resources/biostatistics
Dr. Tatsuoka has received numerous grants as principal investigator from the National Science Foundation, the National Institute of Health, and industry partners for his methodological work in sequential experimental designs. His methods research interests include early phase adaptive clinical trials, biomarker driven trials, early diagnosis and screening, ultra-high dimensional variable selection, and statistical applications with high performance computing.
Research/Clinical Keywords
Biostatistics, Cancer research, Clinical trials, Adaptive designs, Biomarker analysis, Big data analytics, Predictive modeling, Precision medicine, Ultra-high dimensional variable selection, High-performance computing
Highlighted Publications
Wang GM and Tatsuoka C, “Bayesian Ordered Lattice Design for Phase I Clinical Trials,” Statistics in Medicine 45, no. 6-7 (2026): e70456, https://doi.org/10.1002/sim.70456.
Chen W, Qi H, Tatsuoka C, Lu X (co-corresponding). SBMGT: Scaling Bayesian Multinomial Group Testing. PPoPP '25: Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming. 2025 February 28; :512-523. https://doi.org/10.1145/3710848.37108
Tatsuoka C, Chen W, Lu X. Bayesian group testing with dilution effects. Biostatistics. 2023 Oct 18;24(4):885-900. PubMed Central PMCID: PMC10583721.
Shiny app for Bayesian ordered lattice design (BOLD) Phase I clinical trials: https://github.com/hiddenmanna1996