Sungjun Lim

Trustworthy AI,Reliable AI,Reasoning Models,Uncertainty Quantification,Robustness,Mechanistic Interpretability,Probabilistic Machine Learning

About Me

Sungjun Lim

Sungjun Lim

Ph.D. Student, Yonsei University

Statistics and Data Science

Hello! I am Sungjun Lim, a PhD student at Yonsei University's Statistics and Data Science department.

My ultimate goal is to build AI systems that remain trustworthy under uncertainty: models that can recognize what they do not know, reason over alternatives, adapt beyond training conditions, and make their decisions understandable to humans.

Research Philosophy

Five research axes toward one goal

Ultimate Goal Trustworthy AI under uncertainty
Axis 01
Uncertainty as a Signal

Use uncertainty not only to measure confidence, but to guide prediction, selection, and exploration.

News

Category
Year

2026

Career
Started an internship at RIKEN AIP

Joined the Adaptive Bayesian Intelligence Team at RIKEN AIP as an intern.

Career Team
Etc.
Joined the organizing committee for the Mechanistic Interpretability Workshop at ICML 2026

The workshop will be held at ICML 2026 in Seoul, South Korea.

Workshop
Publications
Uncertainty-driven Embedding Convolution accepted to ICLR 2026

Our work on uncertainty-aware embedding ensembles was accepted to the ICLR 2026.

arXiv Project page Publications
Publications
Semi-Supervised Preference Optimization with Limited Feedbacks accepted to ICLR 2026

The paper was accepted to ICLR 2026 as an oral presentation.

arXiv Publications

2025

Career
Visited Australian National University as a visiting researcher

Worked with Lexing Xie in Computer Science at ANU.

Career Advisor
Publications
Flat Posterior Does Matter For Bayesian Model Averaging accepted to UAI 2025

Our paper on flat posterior behavior in Bayesian model averaging was accepted to UAI 2025.

arXiv Publications

No news in this category yet.

Career

Mar. 2017 – Aug. 2022
B.S. in Statistics

University of Seoul

Jun. 2021 – Aug. 2022
Undergraduate Research Assistant

MLAI Lab, University of Seoul

Sep. 2022 – Feb. 2024
M.S. in Artificial Intelligence

University of Seoul

Advisor: Kyungwoo Song

Mar. 2024 – Present
Ph.D. Student

Statistics and Data Science, Yonsei University

Advisor: Kyungwoo Song

Jul. 2025 – Aug. 2025
Visiting Researcher

Computer Science, Australian National University

Advisor: Lexing Xie

May. 2026 – Nov. 2026 (expected end)
Internship

RIKEN AIP, Adaptive Bayesian Intelligence Team

Advisor: Emtiyaz Khan

Publications

Google Scholar

📘 Peer Reviewed

Year
Type
Theme
  1. Language model-guided student performance prediction with multimodal auxiliary information
    Applications
    • Changdae Oh, Minhoi Park, Sungjun Lim, Kyungwoo Song
    • Expert Systems with Applications (ESWA) 2024
  2. GFML: Gravity Function for Metric Learning
    Robustness
    • Hoyoon Byun, Sungjun Lim, Kyungwoo Song
    • Engineering Applications of Artificial Intelligence (EAAI) 2025
  3. Robust Optimization for PPG-based Blood Pressure Estimation
    Robustness Applications
    • Sungjun Lim, Taero Kim, Hyeonjeong Lee, Yewon Kim, Minhoi Park, Kwang-Yong Kim, Minseong Kim, Kyu Hyung Kim, Jiyoung Jung, Kyungwoo Song
    • Biomedical Signal Processing and Control (BSPC) 2025
  4. Brain-inspired Lp-Convolution benefits large kernels and aligns better with visual cortex
    • Jae Kwon, Sungjun Lim, Kyungwoo Song, C. Justin Lee
    • International Conference on Learning Representations (ICLR) 2025
  5. Sufficient Invariant Learning for Distribution Shift
    Robustness
    • Taero Kim, Subeen Park, Sungjun Lim, Yonghan Jung, Krikamol Muandet, Kyungwoo Song
    • Computer Vision and Pattern Recognition (CVPR) 2025
  6. Flat Posterior Does Matter For Bayesian Model Averaging
    Uncertainty Probabilistic ML
    • Sungjun Lim, Jeyoon Yeom, Sooyon Kim, Hoyoon Byun, Jinho Kang, Yohan Jung, Jiyoung Jung, Kyungwoo Song
    • Uncertainty in Artificial Intelligence (UAI) 2025
  7. Uncertainty Aware Contrastive Decoding
    Uncertainty
    • Hakyung Lee, Subeen Park, Joowang Kim, Sungjun Lim, Kyungwoo Song
    • Association for Computational Linguistics (ACL) 2025 Findings
  8. COVID-19 Prediction with Doubly Multi-task Gaussian Process
    Probabilistic ML Applications
    • Sooyon Kim, Yongtaek Lim, Sungjun Lim, Gyeongdeok Seo, Jihee Kim, Hojun Park, Jeahun Jung, Kyungwoo Song
    • Journal of Biomedical Informatics 2025
  9. Causal Effect Variational Transformer for Public Health Measures and COVID-19 Infection Cluster Analysis
    Probabilistic ML Applications
    • Jinho Kang, Sungjun Lim, Kyungwoo Song
    • Conference on Information and Knowledge Management (CIKM) 2025
  10. Data Adaptive Stochastic Ensemble Net: Optimizing Infection Predictions for COVID-19 Cluster Analysis
    Uncertainty Applications
    • Sungjun Lim, Yongtaek Lim, Hojun Park, Junggu Lee, Jaehun Jung, Kyungwoo Song
    • IEEE Journal of Biomedical and Health Informatics 2025
  11. RAILL : Retrieval-Augment and Instruction Tuning for Low-resource Language Model Training
    Efficiency
    • Youngjun Choi, Sungjun Lim, Minhoi Park, Jaekyeong Jung, TaeKyung Kim, Hosik Choi, Kyungwoo Song
    • IEEE Big data 2025 (Short)
  12. Semi-Supervised Preference Optimization with Limited Feedbacks
    • Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
    • International Conference on Learning Representations (ICLR) 2026
    • Oral Presentation
  13. Uncertainty-driven Embedding Convolution
    Uncertainty Probabilistic ML
    • Sungjun Lim, Kangjun Noh, Youngjun Choi, Heeyoung Lee, Kyungwoo Song
    • International Conference on Learning Representations (ICLR) 2026
  14. Eigen-Value : Efficient Domain-Robust Data Valuation via eigenvalue-Based Approach
    Robustness Efficiency
    • Youngjun Choi, Junseong Kang, Sungjun Lim, Kyungwoo Song
    • Computer Vision and Pattern Recognition (CVPR) 2026 Findings

No peer-reviewed publications match the selected filters.

📝 Under-Review

  1. DDRL: A Diffusion-Driven Reinforcement Learning Approach for Enhanced TSP Solutions
    • Joowang Kim, Jeyoon Yeom, Gyeongdeok Seo, Sungjun Lim, Jae Ha Kwak, Heejun Ahn, Gyeong-moon Park, Kyungwoo Song
  2. RRD: Routing-and-Residual Distillation for Efficient MoE Recovery in Large Language Models
    • Hoyoon Byun, Kangjun Noh, SoMin Kim, Heedong Kim, Jaeyoon Shim, Sungjun Lim, Youngjun Choi, Kyungwoo Song
  3. Geometry-Adaptive Explainer for Faithful Dictionary-Based Interpretability under Distribution Shift
    Interpretability Robustness
    • Sungjun Lim, Heedong Kim, Andrew Lee, Kyungwoo Song
  4. SPHERE: Signal-Aware Prototype Harmonization for Continual Specific Emitter Identification
    Applications
    • Sungjun Lim, Sumin Park, Gyungmin Kim, Kyuha Song, Kyungwoo Song

🎓 Workshop

  1. Sufficient Invariant Learning for Distribution Shift
    Robustness
    • Taero Kim, Sungjun Lim, Kyungwoo Song
    • The Sixth Data Science Meets Optimisation (DSO) Workshop at IJCAI 2024
  2. Sequential Treatment Effect Estimation with Variational Transformers: Application to COVID-19 Infection Clusters
    Probabilistic ML Applications
    • Jinho Kang, Sungjun Lim, Kyungwoo Song
    • Artificial Intelligence for Time Series Analysis (AI4TS) at IJCAI 2024
  3. Flat Posterior For Bayesian Model Averaging
    Uncertainty Probabilistic ML
    • Sungjun Lim, Jeyoon Yeom, Sooyon Kim, Hoyoon Byun, Jinho Kang, Yohan Jung, Jiyoung Jung, Kyungwoo Song
    • Frontiers in Probabilistic Inference Workshop at ICLR 2025
  4. A Double-Edged Sword: Benchmarking the Trade-off Between Bias Mitigation and Helpfulness of LLM Guardrails in Finance
    Applications
    • Sungjun Lim, Hoyoon Byun, Jihee Kim, Kyungwoo Song
    • Advances in Financial AI: Innovations, Risk, and Responsibility in the Era of LLMs at CIKM 2025
  5. Geometry-Adaptive Explainer for Faithful Dictionary-Based Interpretability under Distribution Shift
    Interpretability Robustness
    • Sungjun Lim, Heedong Kim, Andrew Lee, Kyungwoo Song
    • Mechanistic Interpretability Workshop at ICML 2026

MLAI Projects

MLAI@Yonsei

Explainable AI for Blood Pressure Estimation

  • Funded by ETRI
  • Deal with Uncertainty about Estimation of BP from AI

    • Causality Covid-19

  • Funded by Ministry of Food and Drug Safety
  • Infection prediction based on causal graph

    • Directional GNN for Infection Prediction

  • Funded by Ministry of Food and Drug Safety
  • Infection prediction based on graphical information

    • Infection Prediction Based on Gaussian Process

  • Funded by Ministry of Food and Drug Safety
  • Infection prediction based on robabilistic model

    • Educational Content Relationship Analysis

  • Funded by TIPS
  • Analyze educational content relationship via LLMs and RAG

    • Signal Processing

  • Funded by ADS
  • Develop class incremental algorithm to classify aviation object