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.

Education

• (Present) Ph.D., Statistics, University of Wisconsin-Madison
• Formal Doctoral student., Applied Statistics, University of California, Riverside
• M.S., Statistics, Seoul National University
• B.S., Statistics and Nursing (Cum Laude), Seoul National University

Publications

1. J. Lee, J. Huling, and G. Chen, An effective framework for estimating individualized treatment rules, (To appear in NeurIPS 2024)
2. J. Lee, H. Lyu, and W. Yao, Supervised Matrix Factorization: Local Landscape Analysis and Applications, ICML 2024. (Paper, GitHub)
3. J. Lee, H. Lyu, and W. Yao, Exponentially convergent algorithms for supervised matrix factorization, NeurIPS 2023. (Paper, GitHub)
4. J. Lee, H. Lyu, and W. Yao, Interpretable Feature Extraction by Supervised Dictionary Learning for Identification of Cancer-Associated Gene Clusters, ICML Workshop on Computational Biology 2023. (Paper, Poster)
5. J. Lee, S. Lee, J. Jang, and T. Park, “Exact association test for small size sequencing data”, BMC medical genomics 2018. (Paper)

Work Experience

• Biostatistics Intern, Biogen (June 2024 - August 2024)
• Statistical Consultant, Graduate Quantitative Methods Center (GradQuant) (October 2019 - June 2020)
• Emergency Room Registered Nurse, Hyundai Asan Medical Center (June 2013 - July 2014)

Invited Talks / Academic Services

• Exponentially Convergent Algorithms for Supervised Matrix Factorization, Institute for Mathematical and Statistical Innovation, Chicago, Dec 2023
• Supervised Dictionary Learning: Algorithms and Applications, International Society for Business and Industrial Statistics, Statistics and Data Science in Business and Industry, University of Naples Federico II, Italy, June 20-21, 2022
• Identification of genes related to pancreatic cancer through targeted sequencing data analysis, IEEE International Conference on Bioinformatics and Biomedicine, Dec 15, 2016
• Reviewer for NeurIPS 2024
• Invited Reviewer for AISTAT 2025
• Invited Reviewer for ICML 2025

Awards

• ICML 2024 Financial Aid (ICML, 2024)
• WiSH Student Research Grant Competition Winner (UW-Madison, 2023 and 2022)
• Outstanding Statistical Consultation Award and Recognition (UC Riverside, 2021)
• Dean’s Distinguished Fellowship Award (UC Riverside, 2019-2020)