Two of Statistics students named among the three IMS Lawrence D. Brown PhD Student Awards

Our PhD students, Chan Park and Rungang Han, received the Institute of Mathematical Statistics (IMS) Lawrence D. Brown Ph.D. Student Awards. This inaugural award was given to three PhD students among the entire IMS community. We are excited to learn that all three recipients this year are our students/alumnus: Chan Park is a final-year PhD student advisor by Prof. Hyunseung Kang, Rungang Han is a final-year PhD student advisor by Prof. Anru Zhang, and Rong Ma (the third recipient, now postdoc at Stanford) was our MS alumnus in 2016.

“I am so grateful for this once-in-a-lifetime experience. I would want to express my gratitude to the UW-Madison and the faculties/staffs of the statistics department for providing numerous research opportunities and related supports. In particular, I am very thankful for my *perfect* advisor Prof. Hyunseung Kang who has helped me since 2018 and the other two mentors, Profs. Guanhua Chen and Menggang Yu, for valuable advice. Lastly, I am grateful to my beloved wife and my family for their moral support. I will consider this achievement as a motivation for future research that can contribute to statistics and related fields of study. I am planning to graduate in May 2022 and am looking for a post-doctoral position. Congratulation to Rungang Han as well!” — Chan Park.

Chan’s broad research interest is to develop flexible, nonparametric methods to infer causal effects in dependent and/or clustered data and to show the optimality of these methods.
His award-winning paper is “Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects’’, a joint work with Prof. Hyunseung Kang. The work proposes a new bound-based method that uses pre-treatment covariates, classification algorithms, and a linear program to obtain sharp bounds of the network causal effects that are not point-identified under the presence of interference and noncompliance in cluster randomized trials.

“I have been staying at UW-Madison for four years for my Ph.D. study and I would like to thank the faculties, staff, and cohorts in the statistics department for their various support. In particular, I would like to express my gratitude to my advisor Prof. Anru Zhang who introduced me to the area of high-dimensional statistics and tensor data analysis and provided me with countless advice and encouragement in the past few years. I defended my dissertation this summer and am currently a postdoc associate at Duke University.’’ — Rungang Han.

Rungang is broadly interested in methodology and theory in high-dimensional statistics, machine learning and optimization. His recent interest focuses on challenges in large-scale statistical matrix/tensor inference. His award-wining paper is “Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit”, a joint work with PhD student Yuetian Luo, Profs. Miaoyan Wang and Anru Zhang. The work introduces a tensor block model and the computationally efficient methods for high-order clustering tasks. The authors establish the convergence of the proposed procedure and provide the complete characterization for the statistical-computational trade-off in high-order clustering problems.

Chan Park and Rungang Han have been good friends since they started the PhD program in 2017. Both of them have the career aspiration of becoming a Professor. They congratulate each other for the achievement in the academic journey. Chan Park is also the recipient for ASA Biometrics Section JSM Paper Award (2021) and ENAR Distinguished Student Paper Award (2020). Rungang Han is also the recipient for ASA Statistical Learning and Data Science Section JSM Paper Award (2021). We are proud of you!