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Faculty
Jordan Bryan
Assistant Professor of Data Science
School of Data Science
Email: uen8eq@virginia.edu
Additional Contact Information
Website: j-g-b.github.io/
Office: 1919 Ivy Rd., Rm. 347
Jordan Bryan is a statistician with broad expertise in multivariate data analysis. He has studied and developed statistical methods in the fields of environmental monitoring, high-energy physics, and cancer genomics. His research interests include Bayesian statistics, robust estimation, and information-assisted hypothesis testing.
Prior to joining the faculty at UVA, he was a postdoctoral researcher at the University of North Carolina at Chapel Hill, where he was supported by training grants from the National Institute of Environmental Health Sciences (NIEHS) and the National Heart Lung and Blood Institute (NHLBI). He also worked as an associate computational biologist at the Broad Institute of MIT and Harvard. As of January 2024, he is the Secretary of the junior section of the International Society for Bayesian Analysis. He received his Ph.D. in Statistics from Duke University in 2023.
Education
- Ph.D., Statistics, Duke University
- B.S., Mathematics, Stanford University
Data Science Domains
Analytics
Areas of Practice
Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences; Environment and Climate Science; Theory, Foundations, and Advanced Methodologies
Selected Publications
- Bryan, JG, Zhou, H, and Li, D. "Subscedastic weighted least squares estimates." https://arxiv.org/abs/2404.00753
- Bryan, JG, Niu, H, and Li, D. "Incorporating LLM-Derived Information into Hypothesis Testing for Genomics Applications." https://doi.org/10.1101/2025.04.30.651464
- Bryan, JG, Niles-Weed, J, and Hoff, PD, "The multirank likelihood for semiparametric canonical correlation analysis." Journal of Multivariate Analysis, (November 2025) https://doi.org/10.1016/j.jmva.2025.105484
- Bryan, JG, Hoff, PD, and Osburn, CL. "Linear source apportionment using generalized least squares." Technometrics, (July 2024) https://doi.org/10.1080/00401706.2024.2379850
- Bryan, J and Li D. "Comments on Contemporary Uses of Machine Learning for Electronic Health Records." North Carolina Medical Journal, (July 2024) https://doi.org/10.18043/001c.120570
- Bryan, JG, Hoff, PD, and Osburn, CL. "Routine Estimation of Dissolved Organic Matter Sources Using Fluorescence Data and Linear Least Squares." ACS ES&T Water, (May 2023) https://doi.org/10.1021/acsestwater.2c00605
- Bryan, JG, and Hoff, PD. "Smaller p-values in genomics studies using distilled auxiliary information." Biostatistics, (July 2021)
- Meyers, RM, and Bryan, JG et al. "Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells." Nature Genetics, (October 2017)
- Mackey, L, Bryan, J, and Mo, MY. "Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge." Proceedings of the NIPS 2014 Workshop on High Energy Physics and Machine Learning, (December 2015)