Publications
This page contains a complete list of my publications with relevant links. You may also refer to my CV and my Google scholar page.
2024
Hybrid Monte Carlo for failure probability estimation with Gaussian process surrogates
with Ashwin Renganathan. preprint on arXiv:2410.04496
Voronoi candidates for Bayesian optimization
with Nathan Wycoff, John W. Smith, and Robert B. Gramacy. preprint on arXiv:2402.04922
Monotonic warpings for additive and deep Gaussian processes
with Steven Barnett, Lauren J. Beesley, Robert B. Gramacy, and Dave Osthus. preprint on arXiv:2408.01540
Nonstationary Gaussian process surrogates
with Andrew Cooper and Robert B. Gramacy. Handbook of Uncertainty Quantification, to appear. preprint on arXiv:2305.19242
Contour location for reliability in airfoil simulation experiments using deep Gaussian processes
with Ashwin Renganathan and Robert B. Gramacy. Annals of Applied Statistics. preprint on arXiv:2308.04420
Actively learning deep Gaussian process models for failure contour and reliability estimation
with Ashwin Renganathan and Robert B. Gramacy. In AIAA Scitech 2024 Forum.
Bayesian deep process convolutions: An application in cosmology
with Kelly Moran, Richard Payne, Earl Lawrence, David Higdon, Stephen Walsh, Juliana Kwan, Amber Day, Salman Habib, and Katrin Heitmann. preprint on arXiv:2411.14747
2023
Deep Gaussian process surrogates for computer experiments
Ph.D. Thesis, Virginia Polytechnic Institute and State University
Vecchia-approximated deep Gaussian processes for computer experiments
with Andrew Cooper and Robert B. Gramacy. Journal of Computational and Graphical Statistics. arXiv:2204.02904
2022
Triangulation candidates for Bayesian optimization
with Robert B. Gramacy and Nathan Wycoff. Advances in Neural Information Processing Systems (NeurIPS). arXiv:2112.07457
deepgp: An R-package for Bayesian deep Gaussian processes
ISBA Bulletin, Software Highlight.
Invited discussion of “Deep Gaussian processes for calibration of computer models” by Marmin and Filippone
with Robert B. Gramacy. Bayesian Analysis.
Gradient-enhanced reliability analysis of transonic aeroelastic flutter
with Bret Stanford, Kevin Jacobson, and James Warner. In AIAA Scitech 2022 Forum.
2021
Active learning for deep Gaussian process surrogates
with Robert B. Gramacy and David Higdon. Technometrics. arXiv:2012.08015