Welcome! I am a computational statistician at Argonne National Laboratory, where I work on developing and applying statistical and machine learning methods to solve complex scientific problems.
My research focuses on uncertainty quantification, calibration of computer models, and developing scalable computational methods for large-scale scientific applications. I received my Ph.D. in Statistics from Virginia Tech and have been at Argonne since 2019.
Introduces Scaled Block Vecchia for Gaussian process emulation on distributed GPUs, combining anisotropic input scaling with block approximation, MPI, and batched linear algebra. Demonstrates near-linear scaling on up to 512 GPUs and workloads of up to 2.56 billion points.
IEEE Transactions on Parallel and Distributed Systems, 2026
A tutorial on calibrating stochastic simulators through trajectory-oriented optimization with the open-source adaptive_ts Python package, accompanied by worked examples.
arXiv preprint, 2026
Introduces a data-driven, uncertainty-aware decision support (DDUADS) workflow that combines sensitivity screening, Bayesian calibration, and multi-surrogate integration for stochastic simulators under tight compute budgets. Demonstrated on an HIV agent-based model in Chicago to evaluate ART and PrEP disruption scenarios.
medRxiv preprint, 2026
Presents MetaRVM, an open-source R package for stratified metapopulation modeling co-developed with the Chicago Department of Public Health. Uses structured mixing and SEIR-style dynamics to give richer subpopulation detail than compartmental models while remaining cheap enough for routine operational use, demonstrated on Chicago influenza.
Proceedings of the 2025 Winter Simulation Conference (WSC), 2025
Proposes an adaptive grid-based Thompson sampling strategy for trajectory-oriented Bayesian optimization of stochastic simulators, balancing global exploration with local refinement. Discovers higher-quality trajectories with fewer expensive evaluations than fixed-grid alternatives, supporting faster uncertainty-aware calibration.
arXiv preprint, 2025
Introduces SACOMA, an unsupervised pipeline that identifies co-methylated regions in DNA methylation arrays by combining genomic proximity and methylation similarity through spatially constrained hierarchical clustering. A tunable mixing parameter avoids rigid threshold choices while maintaining strong sensitivity and false-positive control.
bioRxiv preprint, 2025
Trying my hands on espresso and latte art.
My daily driver, I call it Groot.
It's the time of the year when I strap my feet to a piece of board.
Hiking in the Blue Ridge Mountains whenever I can get to the trails.