Welcome!
📣 News
- Jan 2026: Our work on the Racial, Ethnic, and Sex Differences in Social Risks and Social Needs Concordance Among Veterans was accepted by JAMA Network Open! Manuscript will be available online soon.
- Jan 2026: Our paper on Quantification and cross-fitting inference of asymmetric relations under generative exposure mapping models was accepted by Statistica Sinica! Read more here.
- October 2025 New publication in JAMA Network Open! Read more about the Health-Related Social Needs Among LGB+ Veterans!
- Aug-Dec 2025: Taught BIOST 2155: Introductory Statistical Learning for Health Sciences as the primary instructor.
- Jul 2025 New publication in BMJ Open! Read more about the Determinants of enrolment rate in 397 clinical trials for healing diabetic foot ulcers: a systematic review!
🙋 About me
I am an Assistant Professor in the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health. I received my Ph.D. in Biostatistics from the University of Michigan, Ann Arbor in 2024 under the supervision of Peter Song. My dissertation committee members were Bhramar Mukherjee, Gongjun Xu, and Walter Dempsey. During my time at Michigan, I worked as a research assistant for Goncalo Abecasis (2019-20) and Cathie Spino (2020-23).
In addition to my appointment at Pitt, I serve as a Research Biostatistician at the Center for Healthcare Evaluation, Research, and Promotion (CHERP) within the VA Pittsburgh Healthcare System. CHERP is a Center of Innovation (COIN) dedicated to understanding and improving health and healthcare outcomes to support the VA in providing excellent care and service to all Veterans.
📖 Research
My research agenda focuses on developing scalable and flexible statistical models for the design and analysis of data linked to biomedical as well as social studies and health policy. I leverage both Bayesian methods and classical semi- and non-parametric approaches to navigate complex data structures. I also maintain a strong focus on statistical computing, developing efficient software packages (such as fastMI and SEIRfansy) to make robust methodological frameworks accessible for applied analysis.
My current research interests include:
- Information-theoretic framework for association and causality: Development of statistical methods for discovering causal structure and quantifying associations without relying on rigid parametric assumptions. My work utilizes information theory to generate directional hypotheses in observational data.
- Spatiotemporal modeling for infectious disease: Development of forecasting techniques and compartmental models to study the transmission and fallout of infectious diseases. This includes extensions of the classical SIR framework to handle issues such as false negative rates and symptom-based testing, with a specific emphasis on COVID-19.
- Health Services Research: Application of rigorous data science to investigate health equity and outcomes. My current work at the VA examines health-related social needs, social determinants of health, and medication initiation patterns among Veterans.
For a complete list of my publications and software, please visit my Google Scholar profile.