About
My training in numerical analysis, causal machine learning, computational biology, and biostatistics now supports a focused question: when should a model be trusted for a scientific or clinical decision?
The arc
Foundations. I began in applied mathematics, studying numerical methods for nonlinear dynamical systems, solver design, convergence, and stability. My Ph.D. at West Virginia University focused on ensemble Bayesian inference of heterocellular networks in cancer.
Biomedical machine learning. At the WVU Cancer Institute, I worked on generative models, Bayesian networks, and deep learning for biological signal decomposition. An NSF fellowship in AI and machine learning for digital health supported the causal-network work that became BaMANI.
Clinical and translational work. At Duke, I moved into multiscale scientific machine learning, optimal-transport alignment, and NIH-supported congenital CMV modeling. My current MIT and Duke work focuses on patient-level clinical AI reliability and epilepsy-surgery outcomes.
Appointments & education
Clinical research training
I completed CITI training in Human Subjects Research, Good Clinical Practice, HIPAA, and Clinical Data Privacy through Duke Health in 2026. I am also credentialed for restricted PhysioNet datasets.
Research communities
I collaborate with the Duke Center for Human Systems Immunology and the Multiscale Immune Systems Modeling Center of Excellence. My professional affiliations include SITC, AACR, the Society for Mathematical Biology, and the American Mathematical Society.