- I am on the job market now for positions starting in Fall, 2024!
- 2023.08.08 - Received the 2023 - 24 Rackham Conference Travel Grant to present my research at the 2023 Joint Statistical Meetings (JSM)!
- 2023.07.31 - My paper on fastMI: a fast and consistent copula-based nonparametric estimator of mutual information is accepted for publication by the Journal of Multivariate Analysis!
- 2023.06.20 - Received the 2023 WNAR Best Student Paper Award (written category) for my work on asymmetric predictability in causal discovery.
- 2023.05.08 - Received the 2022 - 23 Rackham Conference Travel Grant to present my research at the 2023 Annual WNAR/IMS meeting!
- 2023.03.27 - Awarded the 2023 Rising Star Award by the University of Michigan for my work with STATCOM!\
- 2023.03.18 - Selected as a recipient of a travel award for the CBMS Conference Foundations of Causal Graphical Models and Structure Discovery supported by the National Science Foundation (NSF DMS-2227849) and Texas A&M Institute for Applied Mathematics and Computational Science (IAMCS).
- 2023.02.27 - Awarded the Rackham Predoctoral Fellowship for 2023-24!
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.