James is an Assistant Professor of Statistics and Co-Director of the BS in Data Science program at the University of San Francisco. He has joint appointments in the Department of Mathematics and Statistics and the MS in Data Science program, where he has developed and taught courses in Bayesian statistics, machine learning, data science, and network analysis.
In research, James develops new statistical and computational techniques to model, analyze, and explore high-dimensional and relational (network) data. Driven by applications in neuroscience and political and social science, James seeks to understand and motivate the interplay between statistics, data science, and application.
James also works closely with companies in the Bay area to solve exciting data science and network analysis problems. He has worked with companies including Airbnb, Eventbrite, the San Francisco 49'ers, the Houston Astros, Xoom, and Zipcar.
At the University of San Francisco, James seeks to promote the next generation of cross-disciplinary research among data scientists in academia, industry, and government. As a first step to accomplish this aim, he recently organized and chaired the 1st annual Data Institute Conference, which brought in over 75 renowned data scientists from all three sectors, and plans to continue holding this conference annually.
- Program Director, BS in Data Science
- PhD, Statistics and Operations Research, University of North Carolina, 2015
- MS, Mathematical Sciences, Clemson University, 2010
- BS, Mathematics, Campbell University, 2008
- BS, Chemistry, Campbell University, 2008
- Statistical analysis and modeling of complex networks
- Community detection
- Data mining and machine learning
- Social network monitoring
Wilson, J.D., Palowitch, J., Bhamidi, S., and Nobel, A.B. (2017). "Community extraction in multilayer networks with heterogeneous community structure." Journal of Machine Learning Research.
Woodall, W.H., Zhao, M., Paynabar, K., Sparks, R., and Wilson, J.D. (2017). "An overview and perspective on social network monitoring." IISE Transactions 49:3, 354 - 365.
Stillman, P.E., Wilson J.D., Denny, M.J., Desmarais, B., Bhamidi, S., Cranmer, S., and Lu, Z-L (2017). "Statistical modeling of the default mode brain network reveals a segregated highway structure." Scientific Reports 7(1), 11694.
Wilson, J.D., Desmarais, B., Cranmer, S., Denny, M. and Bhamidi, S. (2017). "Stochastic weighted graphs: flexible model specification and simulation." Social Networks, 49, 37-47.
Szekely, E., Pappa, I., Wilson, J.D., Bhamidi, S., Jaddoe, V., Verhulst, H.T., and Shaw, P. (2015). "Childhood peer network characteristics: genetic influences and links with early mental health trajectories." Journal of Child Psychology and Psychiatry, 57(6), 687-694.
Parker, K.S., Wilson, J.D., Marschall, J., Mucha, P.J., and Henderson, J.P. (2015). "Network analysis reveals sex and antibiotic resistance associated antivirulence targets in clinical uropathogens." American Chemical Society: Infectious Diseases, 1(11), 523-532.
Wilson, J.D., Wang, S., Mucha, P.J., Bhamidi, S., and Nobel, A.B. (2014). "A testing based extraction algorithm for identifying significant communities in networks." Annals of Applied Statistics, 8(3), 1853-1891.
Wilson, J.D., Bhamidi, S., and Nobel, A.B. (2013). "Measuring the statistical significance of local connections in directed networks." Neural Information Processing Systems Workshop on Frontiers of Network Analysis: Methods, Models and Applications.