Vice Provost for Research
William & Mary
Office: 311J Blow Memorial Hall
Phone: 757-221-6442
Email: agwilson01@wm.edu
Curriculum Vita
Bio
Alyson Gabbard Wilson is a statistician whose work advances the reliability and uncertainty quantification of complex engineered systems — determining whether systems like the nuclear stockpile, missile defense, and fielded military hardware will perform when it matters, often when they cannot be fully tested. Across three decades at Los Alamos National Laboratory, the Institute for Defense Analyses, Iowa State University, North Carolina State University, and now William & Mary, she has developed Bayesian methods for combining heterogeneous evidence — physical tests, computer models, component data, and expert judgment — into rigorous assessments of whole-system reliability that inform national-security decisions.
At Los Alamos, she developed and led the reliability-assessment methodology used in the nation’s stockpile stewardship programs after the end of underground nuclear testing, work recognized with multiple Department of Energy Defense Programs Awards of Excellence. The same framework has shaped test and evaluation across defense systems and, more broadly, uncertainty quantification in fields from materials science to aircraft-noise certification. She is a co-author of
Because the field turned to her for its most demanding reliability and data challenges, she has built the institutions around them: as Principal Investigator of the Laboratory for Analytic Sciences, a $94M collaboration between the intelligence community and academia; as founder of NC State’s Data Science and AI Academy; and, today, as Vice Provost for Research at William & Mary, where she leads the university’s research enterprise.
Selected Publications
- Hamada, M. S., Wilson, A. G., Reese, C. S., Martz, H. F. (2008). Bayesian Reliability. Springer.
- Wilson, A., Wilson, G., Olwell, D. (Eds.). (2006). Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication. Springer.
- Wendelberger, L., Reich, B., Wilson, A., Gray, J. (2026). Detecting Deforestation Using Robust Online Bayesian Monitoring. Data Science in Science 5(1).
- Edwards, C., Smith, R., Mattingly, J., Wilson, A. (2025). Localization of Stationary and Moving Radiation Sources Using a Feedforward Neural Network with an Array of Sensors. Nuclear Technology 211(11): 2832-2845.
- Bakerman, J., Pazdernik, K., Korkmaz, G., Wilson, A. (2021). Dynamic Logistic Regression and Variable Selection: Forecasting and Contextualizing Civil Unrest. International Journal of Forecasting 38(2): 648-661.
- Chakraborty, A., Lahiri, S., Wilson, A. (2020). A Statistical Analysis of Noisy Crowdsourced Weather Data. Annals of Applied Statistics 14(1): 116-142.
- Bakerman, J., Pazdernik, K., Wilson, A., Bahran, R., Fairchild, G. (2018). Twitter Geolocation: A Hybrid Approach. ACM Transactions on Knowledge Discovery from Data 20(3): 1-17.
- Zhang, X., Wilson, A. (2017). System Reliability and Component Importance under Dependence: A Copula Approach. Technometrics 59(2): 215-224.
- Wilson, A., Fronczyk, K. (2017). Bayesian Reliability: Combining Information. Quality Engineering 29(1): 119-129.
- Fancher, C., Han, Z., Levin, I., Page, K., Reich, B., Smith, R., Wilson, A., Jones, J. (2016). Use of Bayesian Inference in Crystallographic Structure Refinement via Full Diffraction Profile Analysis. Scientific Reports 6:31625.
- Dickinson, R., Freeman, L., Simpson, B., Wilson, A. (2015). Statistical Models for Combining Information: Stryker Reliability Case Study. Journal of Quality Technology 47(4): 400-415.
- Guo, J., Wilson, A. (2013). Bayesian Methods for Estimating the Reliability of Complex Systems Using Heterogeneous Multilevel Information. Technometrics 55(4): 461-472.
- Wilson, A., Graves, T., Hamada, M., Reese, C. S. (2006). Advances in Data Combination, Analysis, and Collection for System Reliability Assessment. Statistical Science 21(4): 514-531.