Bayesian methods & computation
Inference and computation for complex processes with incomplete or noisy observations.
About Me
I am an Assistant Professor of Biostatistics at the University of Michigan. My research develops Bayesian methodology and computational tools for spatio-temporal processes, infectious disease models, observational health data, and other complex or large-scale settings.
Before joining Michigan, I was a postdoctoral research fellow at UCLA working with Dr. Marc Suchard. I received my Ph.D. in Statistics from Duke University, where I was advised by Dr. Alexander Volfovsky.
Assistant Professor in Biostatistics
University of Michigan
Infectious disease modeling
Mechanistic models for epidemic dynamics, disease transmission, and individual heterogeneity.
Observational health data
Comparative effectiveness and bias-aware methods for federated health data analytics.
Select recent publications
- Comparative Cardiovascular Effectiveness of Individual Glucagon-Like Peptide 1 Receptor Agonists and Sodium-Glucose Cotransporter-2 Inhibitors as Second-Line Type 2 Diabetes Mellitus Treatments
- Neural Posterior Estimation for Stochastic Epidemic Modeling
- Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity
- Inferring HIV Transmission Patterns from Viral Deep-Sequence Data via Latent Spatial Poisson Processes
- Bayesian Safety Surveillance with Adaptive Bias Correction
- Likelihood-based Inference for Partially Observed Epidemics on Dynamic Networks