Jungbin Jun

I am an undergraduate student in Statistics at Inha University and an undergraduate researcher in the Theoretical Statistics Lab under the supervision of Prof. Ilsang Ohn.

My academic background is primarily in statistics, with additional coursework in mathematics. I am interested in reliability and uncertainty in modern AI, especially in problems related to trustworthy AI. My current work lies at the intersection of online learning, conformal inference, and adaptive statistical inference.

Email  /  CV  /  Github  /  Blog

Education
Inha University
B.S. in Statistics
Mar 2022 - Sep 2026 (Expected)
  • GPA: 4.42 / 4.50 (Major: 4.47 / 4.50)
  • Early Graduation (3 years)
  • Rank 1/41 in the department

Publications
Online Conformal Inference with Retrospective Adjustment for Faster Adaptation to Distribution Shift
Jungbin Jun, Ilsang Ohn
arXiv preprint, 2025
paper / code

RetroAdj is an online conformal method that dynamically updates \(\alpha_t\) and retrospectively adjusts the prediction interval \(\hat C_t(\alpha_t)\) over time. It adapts quickly under distribution shift while remaining efficient and long-run valid.

Early-stopped Aggregation: Adaptive Inference with Computational Efficiency
Ilsang Ohn, Shitao Fan, Jungbin Jun, Lizhen Lin
In preparation, 2026

ESA is a variational Bayes aggregation method that constructs model-specific posteriors via a variational free energy and aggregates only promising models, avoiding unnecessary exploration of overly large models.

Reading Notes

Online/Sequential Learning

Statistics

Scholarships and Awards
Excellence Scholarship
Inha University
2023, 2025
Hanjin Group Scholarship
Inha University
2026

Website template adapted from Jon Barron.