Annie Booth

Education

• Ph.D. in Statistics, Virginia Tech (2023)
  • Dissertation: “Deep Gaussian Process Surrogates for Computer Experiments”
• M.S. Statistics, Virginia Tech (2019)
• B.S. Applied Mathematics and B.A. Psychology, Auburn University (2018)

Professional Experience

• Assistant Professor, Department of Statistics, Virginia Tech (2025 - Present)
• Assistant Professor, Department of Statistics, NC State University (2023-2024)

Awards and Honors

• ISBA Savage Award Finalist (2023) 
• Shewell Award for presentation at Fall Technical Conference (2023) 
• Mary G. and Joseph Natrella Scholarship (2022) 
• ASA Physical and Engineering Sciences Section Student Paper Competition Winner (2022) 
• ISBA Best Student/Postdoc Contributed Paper Award (2021) 
• ISBA Industrial Statistics Student Presentation Award, Honorable Mention (2021) 
• Virgina Tech Myers Award for excellence in linear models and design of experiments (2019) 
• Virginia Tech Boyd Harshbarger Award for excellence as a first-year graduate student (2019) 
• Virginia Tech Jean D. Gibbons Fellowship (2018-2023)

• STAT 4714: Probability and Statistics for Electrical Engineers
• STAT 3615: Biological Statistics

Bayesian statistics, surrogate modeling, statistical computing, design of experiments, uncertainty quantification, optimization, calibration, reliability. With applications to computer experiments.

A complete and updated list of publications is available on my homepage:

https://www.anniesbooth.com/publications/

Booth, A. S., Renganathan, S. A., & Gramacy, R. B. (2024). Contour location for reliability in airfoil simulation experiments using deep Gaussian processes. Annals of Applied Statistics, to appear. arXiv:2308.04420 

Sauer, A., Cooper, A., & Gramacy, R. B. (2023). Vecchia-approximated deep Gaussian processes for computer experiments. Journal of Computational and Graphical Statistics, 32(3), 824-837. arXiv:2204.02904 

Gramacy, R. B., Sauer, A., & Wycoff, N. (2022). Triangulation candidates for Bayesian optimization. Advances in Neural Information Processing Systems (NeurIPS), 35, 35933-35945. arXiv:2112.07457 

Sauer, A., Gramacy, R. B., & Higdon, D. (2021). Active learning for deep Gaussian process surrogates. Technometrics, 65(1), 4-18. arXiv:2012.08015