James Wilson

James is an Associate 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 first and second Data Institute Conferences, which brought together nearly 200 renowned data scientists from all three sectors, and plans to continue holding this conference annually.

• Statistical analysis and modeling of complex networks
• Community detection
• Data mining and machine learning
• Social network monitoring

• 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

• Kent, D., Wilson, J.D. and Cranmer, S.J., 2022. A Permutation-Based Changepoint Technique for Monitoring Effect Sizes. Political Analysis, 30(2), pp.167-178.

• Parr, T. and Wilson, J.D., 2021. Partial dependence through stratification. Machine Learning with Applications, 6, p.100146.

• Stevens, N.T., Wilson, J.D., Driscoll, A.R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., Perry, M.B., Reisi-Gahrooei, M., Sengupta, S. and Sparks, R., 2021. Broader impacts of network monitoring: Its role in government, industry, technology, and beyond. Quality Engineering, 33(4), pp.749-757. 

• Stevens, N.T., Wilson, J.D., Driscoll, A.R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., Perry, M.B., Reisi-Gahrooei, M., Sengupta, S. and Sparks, R., 2021. Foundations of network monitoring: Definitions and applications. Quality Engineering, 33(4), pp.719-730. 

• Stevens, N.T., Wilson, J.D., Driscoll, A.R., McCulloh, I., Michailidis, G., Paris, C., Parker, P., Paynabar, K., Perry, M.B., Reisi-Gahrooei, M. and Sengupta, S., 2021. Research in network monitoring: Connections with SPM and new directions. Quality Engineering, 33(4), pp.736-748.

• Stevens, N.T., Wilson, J.D., Driscoll, A.R., McCulloh, I., Michailidis, G., Paris, C., Parker, P., Paynabar, K., Perry, M.B., Reisi-Gahrooei, M. and Sengupta, S., 2021. The interdisciplinary nature of network monitoring: Advantages and disadvantages. Quality Engineering, 33(4), pp.731-735. 

• Stevens, N.T. and Wilson, J.D., 2021. The past, present, and future of network monitoring: A panel discussion. Quality Engineering, 33(4), pp.715-718.

• Yu, L., Zwetsloot, I.M., Stevens, N.T., Wilson, J.D. and Tsui, K.L., 2021. Monitoring dynamic networks: A simulation‐based strategy for comparing monitoring methods and a comparative study. Quality and Reliability Engineering International.

• Siegel, S.R., True, L., Pfeiffer, K.A., Wilson, J.D., Martin, E.M., Branta, C.F., Pacewicz, C. and Battista, R.A., 2021. Recalled age at menarche: A Follow-up to the Michigan State University motor performance study. Measurement in Physical Education and Exercise Science, 25(1), pp.78-86. 

• Wilson, J.D., Cranmer, S. and Lu, Z.L., 2020. A hierarchical latent space network model for population studies of functional connectivity. Computational Brain & Behavior, 3(4), pp.384-399.

• Houghton, I.A. and Wilson, J.D., 2020. El niño detection via unsupervised clustering of argo temperature profiles. Journal of Geophysical Research: Oceans, 125(9), p.e2019JC015947.

• Sparks, R. and Wilson, J.D., 2019. Monitoring communication outbreaks among an unknown team of actors in dynamic networks. Journal of Quality Technology, 51(4), pp.353-374.

• Wilson, J.D., 2019. Discussion on “Real-time monitoring of events applied to syndromic surveillance”. Quality Engineering, 31(1), pp.91-9

• 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." In Press, Quality and Reliability Engineering International.

• Stillman, P.E., Wilson, J.D., Denny, M.J., Desmarais, B.A., Cranmer, S.J., and Lu, Z.L. (2019) "A Consistent Organizational Structure Across Multiple Functional Subnetworks of the Human Brain." In Press, NeuroImage.

• Sparks, R., and Wilson, J.D. (2018) "Monitoring communication outbreaks among an unknown team of actors in dynamic networks." Journal of Quality Technology, 1-22

• Wilson, J.D. (2018) "Discussion of "Real-time Monitoring of Events Applied to Syndromic Surveillance.''" Quality Engineering, 1-6.

• Jeske, D., Stevens, N.T., Wilson, J.D., and Tartakovsky, A. (2018) "Statistical network surveillance." Wiley StatsRef-Statistics Reference Online.

• Jeske, D., Stevens, N.T., Tartakovsky, A., and Wilson, J.D. (2018) "Statistical Methods for Network Surveillance." Applied Stochastic Models in Business and Industry, 34 (4), 425-445.

• 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. 18(1), 5458 - 5506.

• 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.

• 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)