Sungjun Lim

Bayesian Deep Learning,Probabilistic Models,Uncertainty Quantification,Large Language Models

About Me

Name: Sungjun, Lim

Birth: Sep. 11, 1998

Department: Statistics and Data Science, Yonsei University

Email: lsj9862@gmail.com

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

My research focuses on Bayesian Deep Learning and Probabilistic Models, with a particular interest in developing robust and interpretable AI systems.

I am currently working on uncertainty quantification in large-scale models and their applications in real-world scenarios.

Career

B.S.

Statistics, University of Seoul

Mar. 2017 – Aug. 2022

Undergraduate Research Assistant

MLAI Lab, University of Seoul

Jun. 2021 – Aug. 2022

M.S.

Artificial Intelligence, University of Seoul

Advisor: Kyungwoo Song

Sep. 2022 – Feb. 2024

Ph.D. Student

Statistics and Data Science, Yonsei University

Advisor: Kyungwoo Song

Mar. 2024 – Present

Visiting Researcher

Computer Science, Australian National University

Advisor: Lexing Xie

Jul. 2025 – Aug. 2025

Publications

Google Scholar

📘 Peer Reviewed

  1. Language model-guided student performance prediction with multimodal auxiliary information
    • Changdae Oh, Minhoi Park, Sungjun Lim, Kyungwoo Song
    • Expert Systems with Applications (ESWA) 2024

  2. GFML: Gravity Function for Metric Learning
    • Hoyoon Byun, Sungjun Lim, Kyungwoo Song
    • Engineering Applications of Artificial Intelligence (EAAI) 2025

  3. Robust Optimization for PPG-based Blood Pressure Estimation
    • 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
    • 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
    • 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
    • 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
    • Sooyon Kim, Yongtaek Lim, Sungjun Lim, Gyeongdeok Seo, Jihee Kim, Hojun Park, Jeahun Jung, Kyungwoo Song
    • Journal of Biomedical Informatics 2025

📝 Under-Review

  1. Data Adaptive Stochastic Ensemble Net: Optimizing Infection Predictions for COVID-19 Cluster Analysis
    • Sungjun Lim, Yongtaek Lim, Hojun Park, Junggu Lee, Jaehun Jung, Kyungwoo Song
    • IEEE Journal of Biomedical and Health Informatics 2025

  2. Causal Effect Variational Transformer for Public Health Measures and COVID-19 Infection Cluster Analysis
    • Jinho Kang, Sungjun Lim, Kyungwoo Song

  3. 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

  4. RAILL : Retrieval-Augment and Instruction Tuning for Low-resource Language Model Training
    • Youngjun Choi, Sungjun Lim, Minhoi Park, Jaekyeong Jung, TaeKyung Kim, Hosik Choi, Kyungwoo Song

  5. Eigen-Value : Efficient Domain-Robust Data Valuation via eigenvalue-Based Approach
    • Youngjun Choi, Junseong Kang, Sungjun Lim, Kyungwoo Song

  6. Semi-Supervised Preference Optimization with Limited Feedbacks
    • Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song

  7. Uncertainty-driven Embedding Convolution
    • Sungjun Lim, Kangjun Noh, Youngjun Choi, Heeyoung Lee, Kyungwoo Song

🎓 Workshop

  1. Sufficient Invariant Learning for Distribution Shift
    • 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
    • Jinho Kang, Sungjun Lim, Kyungwoo Song
    • Artificial Intelligence for Time Series Analysis (AI4TS) at IJCAI 2024

  3. Flat Posterior For Bayesian Model Averaging
    • Sungjun Lim, Jeyoon Yeom, Sooyon Kim, Hoyoon Byun, Jinho Kang, Yohan Jung, Jiyoung Jung, Kyungwoo Song
    • Frontiers in Probabilistic Inference Workshop at ICLR 2025

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