About Me

I am a Ph.D. student in Department of Computer Sciences at the University of Wisconsin-Madison, advised by Prof. Sharon Yixuan Li. Before joining Sharon’s group, I obtained my MS degree in Artificial Intelligence at the University of Seoul under supervision of Prof. Kyungwoo Song and Prof. Jiyoung Jung. I had the privilege of working with Zhi-Qi Cheng, Alexander Hauptmann, and David Mortensen during visiting at Carnegie Mellon University, and internship at NAVER AI Lab allows me to be advised by Dongyoon Han and Sangdoo Yun.

I am broadly interested in machine learning fundamentals and trustworthy AI. Recently, I have been focusing on understanding and improving the robustness of multimodal LLMs under distribution shifts and uncertainty quantification of LLM agents.

Selected Publications and Preprints

(* denotes equal contribution)
Refer to the Google Scholar and CV for the full publication list.

• Understanding Language Prior of LVLMs by Contrasting Chain-of-Embedding
  Lin Long*, Changdae Oh*, Seongheon Park, Yixuan Li
  [paper] [code]
  ArXiv preprint Sep. 2025

• General Exploratory Bonus for Optimistic Exploration in RLHF
  Wendi Li, Changdae Oh, Yixuan Li
  [paper] [code]
  ArXiv preprint Sep. 2025

• Visual Instruction Bottleneck Tuning
  Changdae Oh, Jiatong Li, Shawn Im, Yixuan Li
  [paper] [code]
  NeurIPS 2025
  ICML 2025, Workshop on Reliable and Responsible Foundation Models (Oral Presentation; 6/176=3.4%)

• Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach
  Changdae Oh, Zhen Fang, Shawn Im, Xuefeng Du, Yixuan Li
  [paper] [code]
  ICML 2025
  ICLR 2025, QUESTION Workshop (Oral Presentation)

• DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation
  [paper][code]
  Changdae Oh, Yixuan Li, Kyungwoo Song, Sangdoo Yun, Dongyoon Han
  ICLR 2025
  NeurIPS 2024, Workshop on Adaptive Foundation Models

• Towards Calibrated Robust Fine-Tuning of Vision-Language Models
  Changdae Oh*, Hyesu Lim*, Mijoo Kim, Dongyoon Han, Sangdoo Yun, Jaegul Choo, Alexander Hauptmann, Zhi-Qi Cheng, Kyungwoo Song
  [paper] [code]
  NeurIPS 2024
  NeurIPS 2023, Workshop on Distribution Shifts

• Geodesic Multi-Modal Mixup for Robust Fine-tuning
  Changdae Oh*, Junhyuk So*, YongTaek Lim, Hoyoon Byun, Minchul Shin, Jong-June Jeon, Kyungwoo Song
  [paper] [code]
  NeurIPS 2023

• BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
  [paper] [code]
  Changdae Oh, Hyeji Hwang, Hee-young Lee, YongTaek Lim, Geunyoung Jung, Jiyoung Jung, Hosik Choi, Kyungwoo Song
  CVPR 2023

• Learning Fair Representation via Distributional Contrastive Disentanglement
  [paper] [code]
  Changdae Oh, Heeji Won, Junhyuk So, Taero Kim, Yewon Kim, Hosik Choi, Kyungwoo Song
  KDD 2022

Education

• Ph.D. in Computer Science, University of Wisconsin-Madison
  advisor: Prof. Sharon Yixuan Li
  Sep. 2024 ~ Present

• M.S. in Artificial Intelligence, University of Seoul
  advisor: Prof. Kyungwoo Song and Prof. Jiyoung Jung
  Mar. 2022 - Aug. 2024

• B.S. in Statistics, University of Seoul
  Mar. 2016 - Feb. 2022

Experience

• Research Intern, NAVER AI Lab
  Mentor: Dongyoon Han and Sangdoo Yun, Apr. 2023 ~ Aug. 2024
  • DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation, ICLR 2025
• Visiting Scholar / Research Collaboration, Carnegie Mellon University
  Mentor: Zhi-Qi Cheng, Sep. 2023 ~ Feb. 2024
  
  • Towards Calibrated Robust Fine-Tuning of Vision-Language Model, NeurIPS 2024
  • Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer, EMNLP 2024 Workshop

Academic Services

• Conference Reviewer:
  • NeurIPS 2025, 2024
  • ICML 2025 (Top Reviewer)
  • ICLR 2026, ICLR 2025
  • AAAI 2025
  • AISTATS 2026
  • CVPR 2024
• Conference Volunteer: NeurIPS’24, KDD’22
• Journal Reviewer: TMLR, Neural Network