Building trustworthy AI systems at the intersection of Bayesian inference, high-performance computing, and real-world data challenges, from astronomical imaging to national security analytics.
From Bayesian inference to CT reconstruction, research that drives real decisions.
Active year-round at Sandia National Laboratories, mentoring applied data science teams at Rice's D2K Lab, and advancing PhD thesis work in trustworthy probabilistic ML.
Seven end-to-end data science projects mentored across healthcare, public safety, computer vision, and industry analytics at Rice University's D2K Lab since 2023, earning three showcase awards.
View all projects →A scalable probabilistic framework that addresses uncertainty underestimation in standard VI, producing better-calibrated models under high-dimensional misspecification.
Explore research →GPU-accelerated, end-to-end CT reconstruction pipelines for large-scale nondestructive inspection, including anomaly detection and validation for mission-critical systems.
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