Hi!

I am Joowon Lee, a Ph.D. student in the Department of Statistics at the University of Wisconsin-Madison.

I am interested in the fields of causal inference and machine learning. More specifically, my research interest is in developing individual treatment rules which recommend optimal treatment according to individual characteristics. I seek methods that can give interpretable results so that they can be widely used and communicated with professionals in broad areas.

As a former nurse, I love to help individual patients to improve their health conditions. However, my ultimate goal is to develop novel statistical methods for medical and public health studies, aiming for the overall improvement of public health status.

I earned my B.S. in Statistics and Nursing, and M.S. in Statistics at Seoul National Unversity in South Korea. Before joining UW-Madison, I worked as an emergency room nurse at Hyundai Asan medical center in South Korea and participated in various projects such as finding susceptible genes related to pancreatic cancer by dealing with clinical data, microarray, and sequencing data.

Here are some of my current and previous projects:

1. Joowon Lee, Jared Huling, and Guanhua Chen,
   "On the principles behind robust and effective individualized treatment rule estimation" (submitted to Biometrics)

   • Investigated and proposed several key components to improve the robustness and performance of privacy-preserving individual treatment rule estimation with multi-category treatments.

2. Joowon Lee, Hanbaek Lyu, and Weixin Yao,
   "Exponentially Convergent Algorithm for Supervised Dictionary Learning and Application in Identification of Oncogene Clusters" (To appear in NeurIPS 2023)(arXiv 2022, 61 pp, GitHub)

   • Developed a novel method for finding class-discriminative low-rank latent factors, achieving exponential convergence to global minimizer by formulating a low-rank matrix estimation problem through matrix lifting. Analysis of microarray data for breast cancer showed enhanced accuracy as well as identified significant cancer-associated gene groups.

3. Joowon Lee, Seungyeoun Lee, Jin-Young Jang, and Taesung Park,
   "Exact association test for small size sequencing data"(Journal 2018, 12 pp)

   • Proposed a new exact association test for sequencing data that does not require a large sample approximation based on the Generalized Cochran-Mantel-Haenszel (GCMH) statistic. It is applicable to most sequencing data which is generally restricted to a small sample size due to its-still high cost.