James Wilson

James is a statistics and data science educator, researcher, mentor, and enthusiast. His research merges computational statistics, random graph theory, and machine learning to provide interpretable machinery to model, explore, and analyze complex interacting and imaging systems. He is particularly interested in understanding the interplay between social dynamics and neuro-biological systems that describe aging, behavior and disease. His research has been supported by grants from the National Science Foundation and the National Institutes of Health. 

He developed from scratch five courses in the undergraduate and graduate programs for data science at USF, and has taught 12 different courses, including mathematical statistics, intro to data science, machine learning, linear regression, and computational statistics. As an educator and mentor, James aims to inspire curiosity, creative and critical thinking about how to apply statistical and computational techniques, while encouraging students to become life-long learners.

• Statistics
• Network analysis
• Brain imaging
• Interpretable machine learning

• Director, BS in Data Science Program
• Associate Director of Research, The Data Institute
• Founder and Director of the Postdoctoral Fellow Program, The Data Institute"

• PhD, Statistics and Operations Research, University of North Carolina, 2015
• MS, Mathematical Sciences, Clemson University, 2010
• BS, Mathematics and Chemistry, Campbell University, 2008

• Associate Professor in Statistics and Data Science, University of San Francisco
• Assistant Professor in Statistics and Data Science, University of San Francisco
• Visiting Assistant Professor in Psychiatry, University of Pittsburgh

• Eastern North American Region (ENAR) International Biometric Society Distinguished Student Paper Award, 2017
• Most downloaded and cited paper in journal of Applied Stochastic Models in Business and Industry, 2018-2019
• PI for National Science Foundation grant NSF DMS - 1830547: Spatio-Temporal Data Analysis with Dynamic Network Models. 2018 - 2021.
• PI for National Institute of Aging grant NIA P30 AG066468: Network modeling of functional connectivity trajectories for Alzheimer’s disease.

• Parr, T., Hamrick, J., and Wilson, J.D. (2024) Nonparametric feature impact and importance. Information Sciences 653, 119563.

• Wilson, J.D., Gerlach, A., Aizenstein, H., and Andreescu, C. (2024) Sex matters: Acute functional connectivity changes as markers of remission in late-life depression differ by sex. Molecular Psychiatry 28 (12), 5228-5236.

• Lee, J., Li, G., and Wilson J.D. (2020) Varying-coefficient models for dynamic networks. Computational Statistics and Data Analysis 152: 107052.

• Wilson, J.D., Stevens, N.T., and Woodall, W.H. (2019) Modeling and estimating change in temporal networks via a dynamic degree corrected stochastic block model. Quality and Reliability Engineering International 35(5), 1363 - 1378.

• Wilson, J.D., Palowitch, J., Bhamidi, S., and Nobel, A.B. (2017) Community extraction in multilayer networks with heterogeneous community structure. The Journal of Machine Learning Research 18(1), 5458 - 5506.

• National Science Foundation Grant NSF DMS - 1830547: Spatio-Temporal Data Analysis with Dynamic Network Models. August, 2018 - July, 2021. (Co-PI)
• National Science Foundation Grant NSF DMS - 1841307: The Annual Data Institute Conference (March, 2019). (PI)