Education
Australian National University, Canberra, Australia
Australian National University, Canberra, Australia
Experience
Sandia National Laboratories, Computer Science Research Institute — May 2020 – Present
Lead research in scientific machine learning and uncertainty quantification for high-consequence engineering and national-security applications. Founding developer of PyApprox. Principal investigator on multiple funded projects supported by DARPA, DOE Office of Science (ASCR), and Sandia LDRD.
Sandia National Laboratories, Computer Science Research Institute — Oct 2014 – May 2020
Developed foundational surrogate-modeling and sparse-approximation methods (polynomial chaos, adaptive sparse grids, compressed sensing) and data-consistent inversion, now widely used and cited in UQ research and practice.
Sandia National Laboratories — Jan 2012 – Oct 2014
Purdue University, then Statistical and Applied Mathematical Sciences Institute (SAMSI; hosted by Duke University) — Feb – Dec 2011
Leadership & Service
Associate Editor, Journal of Machine Learning for Modeling and Computing (Begell House)
Organizing Committee, SIAM Conference on Uncertainty Quantification (UQ26), 2026
Chair, DOE ASCR Basic Research Needs Workshop on Inverse Problems; lead author of the resulting report (2025)
Invited tutorials: IPAM Multi-Fidelity Methods for Fusion Energy (UCLA, 2026); AIAA Workshop on Multifidelity Methods for Design & UQ (2026); SIAM Conference on Uncertainty Quantification (2024)
Publications
See the full list on the Publications page.