Heejong Bong
Research Fellow. Department of Statistics, University of Michigan, Ann Arbor.
317-B West Hall
University of Michigan
1085 S University Ave
Ann Arbor, MI 48109
I am currently a postdoctoral research fellow in the Department of Statistics at the University of Michigan, Ann Arbor, working under the mentorship of Professors Liza Levina and Ji Zhu. My research focuses on causal inference on networks, a challenging and rapidly evolving area in statistics.
Previously, I have explored a range of topics, including spatiotemporal methods, graphical models, causal inference on time-series data, high-dimensional central limit theorems, and ranking from pairwise comparisons. For more details on my current and past research, please visit my projects page.
I earned my Ph.D. in Statistics from Carnegie Mellon University (CMU), where I was advised by Professors Robert E. Kass and Valérie Ventura. My doctoral work centered on developing and rigorously testing statistical methods to decode communication between brain regions using complex neural recordings. To address the challenges posed by high-dimensional data, I introduced latent factor time-series models and matrix-variate graphical models.
Following my Ph.D., I spent an additional year as a postdoctoral researcher at CMU, during which I collaborated on three significant projects with Professors Robert E. Kass, Valérie Ventura, Larry Wasserman, Zhao Ren, Alessandro Rinaldo, and Arun Kuchibhotla.
news
Dec 17, 2023 | I presented about “Tight concentration inequality for sub-Weibull random variables with variance constraints” in an invited session of CFE-CMStatistics 2023. The slides are available here. |
---|---|
Sep 29, 2023 | I presented about “Dual Induction CLT for High-dimensional m-dependent Data” in the poster session of Michael Woodroofe Memorial Conference. The poster is available here. |
Aug 25, 2023 | I started a postdoctoral research fellow position in the Department of Statistics at the University of Michigan, Ann Arbor. |
Jul 6, 2022 | I defended my thesis “Discovery of Functional Predictivity across Brain Regions from Local Field Potentials!” The thesis document is available at KiltHub or here. |
May 15, 2022 | My submission to ICML2022 about “Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model” is accepted for one of 118 long presentations. |
selected publications
-
- JRSSBFrequentist Inference for Semi-Mechanistic Epidemic Models with InterventionsForthcoming in the Journal of the Royal Statistical Society Series B: Statistical Methodology 2024
- Unraveling Heterogeneous Treatment Effects in Networks: A Non-Parametric Approach Based on Node ConnectivityarXiv preprint 2024