Statistics · Biostatistics · Data Science
Postdoctoral researcher at Yale University developing trustworthy statistical learning under dependence, heterogeneity, and decentralization. Fields of Interest: Mental Health, EHR
Research
My work builds statistical frameworks that quantify how dependence, feature mismatch, and decentralization distort inference—and establishes principles that keep learning reliable, interpretable, and private.
A unified framework for network time series and causal peer-effect inference that embeds latent homophily into autoregressive dynamics, with asymptotic theory for finite- and infinite-time regimes. Published in JMLR.
Transfer across datasets with non-overlapping feature spaces via proxy-domain mappings and discrepancy correction—with finite-sample bounds and applications in oncology survival and multi-omics integration.
Personalized federated learning that treats client estimators as noisy draws from an unknown prior, estimated via nonparametric maximum likelihood, with oracle denoising inequalities for privacy-preserving information exchange.
Selected papers
Journal of Machine Learning Research, 27(111):1–72, 2026.
arXiv preprint.
Journal of Econometrics. Revise and resubmit.
arXiv preprint.
Path
Yale University · Faculty mentor: Dr. Yize Zhao
Biostatistics and mental health; continuing work on structured statistical learning.
The Ohio State University
Dissertation: Methodological Advances in Network Inference and Information Transfer. Advisors: Dr. Subhadeep Paul and Dr. Arnab Auddy.
Konkuk University, Seoul
Recognition
Selected distinctions for research excellence and support to present work across statistics, biostatistics, and machine learning venues.
Among top 5 of 20 · The 11th Workshop on Biostatistics and Bioinformatics
American Statistical Association (ASA) Georgia Chapter · Georgia State University
Department of Statistics, The Ohio State University
Recognizing outstanding research contributions as a graduate research associate.
Conferences and workshops across the United States
Teaching
I treat statistical thinking as a language for reasoning about evidence—helping students move from intuition to formality through visuals, simulation, and reproducible computation before asymptotics.
At OSU I led recitations in mathematical statistics and statistical inference for over a hundred students each semester, and I am prepared to teach probability, regression, computing, and modern machine learning, including electives on network time series and spectral methods.