I am a graduate student in the Department of Biostatistics at the University of Michigan. In the fall of 2023 I started my fifth (and final 🤞) year here in Ann Arbor.

📣 News

📖 Research

Broadly, I am interested in scaleable and flexible statistical models for design and analysis of biomedical studies and their applications to medical or social science and public policy. The research that I work on include Bayesian methods as well as classical semi- and non-parametric approaches. I love challenges in statistical computing and spend most of my time rubber-ducking my code to myself.

Over the years I have had extensive experience with C++, Python, R and UNIX. Through my research and internships I have learnt to combine computational expertise with statistical knowledge (e.g. data mining, regression, clustering, decision trees, factor analysis, neural networks) to handle domain-specific problems. I am proficient in working with large data sets using strategies like parallelization, MapReduce, and memory-efficient data.