Jungwon Choi

최중원
Towards robust representation learning for real-world AI

I am a Ph.D. student at KAIST AI, working in the Statistical Inference and Machine Learning Lab (SIML) under the supervision of Prof. Juho Lee. Previously, I received my M.S. from KAIST EE, advised by Prof. Dae-Shik Kim, and my B.S. from Inha University.

My research focuses on robust representation learning for real-world multimodal AI. I am broadly interested in multimodal foundation models, self-supervised learning, and generative modeling, especially for learning reliable representations from heterogeneous, incomplete, or distribution-shifted data. My recent work spans full-duplex multimodal interaction in omni-modal foundation models, time-series analysis, and healthcare AI.

Multimodal Learning Foundation Models Generative Models Self-supervised Learning Medical AI

I am open to research internship opportunities in industry. Feel free to reach out if you'd like to collaborate!

Jungwon Choi

News

Mar 2026 Two papers accepted to ICLR 2026 Workshops (Re-Align; Trustworthy AI)!
Jan 2026 Two papers accepted to ICLR 2026!
Dec 2025 One paper accepted to WACV 2026 LFMBio Workshop (Oral)!
Dec 2024 Excellence Award – First Place, Seoul AI Hub (AI+Healthcare)!
Sep 2024 One paper accepted to NeurIPS 2024!
Feb 2024 One paper accepted to ISBI 2024 (Oral)!
Oct 2023 Two papers accepted to NeurIPS 2023 TGL Workshop!
Dec 2022 One paper accepted to AAAI 2023 Bridge Program (Oral)!
Sep 2022 One paper accepted to NeurIPS 2022!

Publications

2026

  1. ICLR
    ForestPersons
    ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection
    Deokyun Kim*, Jeongjun Lee*, Jungwon Choi*, Jonggeon Park*, Giyoung Lee, Yookyung Kim, Myungseok Ki, Juho Lee, Jihun Cha
    ICLR 2026
    OpenReview
  2. ICLR
    fMRI SOC
    Stochastic Optimal Control for Continuous-Time fMRI Representation Learning
    Joonhyeong Park*, Byoungwoo Park*, Chang-Bae Bang, Jungwon Choi, Hyungjin Chung, Byung-Hoon Kim, Juho Lee
    ICLR 2026
    OpenReview arXiv
  3. ICLR
    Re-Align
    Workshop
    CEPA: Context-Conditional Alignment
    Preventing Representation Collapse in Latent Prediction via Context-Conditional Alignment under Missing Modalities
    Jungwon Choi, Juho Lee
    ICLR 2026 Workshop on Representational Alignment (Re-Align)
  4. ICLR
    Trustworthy AI
    Workshop
    Bridging the Missing-Modality Gap
    Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models
    Mingyeong Kim, Jungwon Choi, Chaeyun Jang, Juho Lee
    ICLR 2026 Workshop on Principled Design for Trustworthy AI
  5. WACV Oral
    LFMBio
    Workshop
    ReaMIL overview
    ReaMIL: Reasoning- and Evidence-Aware Multiple Instance Learning for Whole-Slide Histopathology
    Hyundo Jung, Jungwon Choi, Hwiyoung Kim
    WACV 2026 Workshop on Large Foundation Models in Biology and Biomedicine (LFMBio) (Oral)

2024

  1. NeurIPS
    DBFS diffusion bridge
    Stochastic Optimal Control for Diffusion Bridges in Function Spaces
    Byoungwoo Park, Jungwon Choi, Sungbin Lim, Juho Lee
    NeurIPS 2024
    arXiv Code
  2. Preprint
    STJEMA overview
    Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain
    Jungwon Choi, Hyungi Lee, Byung-Hoon Kim, Juho Lee
    Preprint
    arXiv
  3. ISBI Oral
    TENET overview
    Learning Dynamic Brain Connectome with Graph Transformers for Psychiatric Diagnosis Classification
    Byung-Hoon Kim*, Jungwon Choi*, EungGu Yun, Kyungsang Kim, Xiang Li, Juho Lee
    ISBI 2024 (Oral)
    IEEE

2023

  1. NeurIPS
    TGL
    Workshop
    STMAE overview
    A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data
    Jungwon Choi, Seongho Keum, EungGu Yun, Byung-Hoon Kim, Juho Lee
    NeurIPS 2023 TGL Workshop
    Paper arXiv
  2. NeurIPS
    TGL
    Workshop
    TENET embedding
    Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
    Byung-Hoon Kim*, Jungwon Choi*, EungGu Yun, Kyungsang Kim, Xiang Li, Juho Lee
    NeurIPS 2023 TGL Workshop
    Paper arXiv
  3. AAAI Oral
    Uplift modeling
    Modeling Uplift from Observational Time-Series in Continual Scenarios
    Sanghyun Kim, Jungwon Choi, NamHee Kim, Jaesung Ryu, Juho Lee
    AAAI 2023 Bridge Program (Oral)
    Paper Code

2022

  1. NeurIPS
    BPC divergences
    On Divergence Measures for Bayesian Pseudocoresets
    Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee
    NeurIPS 2022
    Paper arXiv Code

2021

  1. Thesis
    MS thesis
    Robust Deepfake Detection via Invariant Feature Learning
    Jungwon Choi
    Master Thesis, KAIST, 2021
    Paper
  2. Archive
    STADAS result
    STADAS: Semi-Supervised Traffic Anomaly Detection with Ambiguous Samples
    Dongho Lim, Jungwon Choi, Wooyeong Cho, Dae-Shik Kim
    Internal Archive, 2021
    Paper

2020

  1. Archive
    Face pareidolia
    Differential Representation of Face Pareidolia in Human Brain and Deep CNN
    Minseon Kim, Jungwon Choi, Sunhyeok Lee, Dae-Shik Kim
    Internal Archive, 2020
    Paper

Honors & Awards

Academic Services

Journal Reviewer
  • Machine Learning (Springer Nature)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • IEEE Open Journal of the Computer Society
Conference Reviewer
  • International Conference on Learning Representations (ICLR)2025–2026
  • International Conference on Machine Learning (ICML)2025–2026
  • Conference on Neural Information Processing Systems (NeurIPS)2026
  • European Conference on Computer Vision (ECCV)2026

Education