Professor: Kyungsu Kim, PhD

School of Transdisciplinary Innovations, Seoul National University, Seoul, South Korea (서울대 첨단융합학부, 의료 & 코어 인공지능)

• • URL for my introduction

  • URL for personal interview

  • URL for our major interview

Department of Biomedical Science, College of Medicine, Seoul National University, Seoul, South Korea (서울대 의과대학 의과학과 대학원)

• URL for my Introduction 

Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea (서울대 인공지능 협동과정 대학원, 과기부 지정 서울대학교 인공지능 대학원)

• • • URL for my Introduction 

Special Lectures on Reinforcement Learning and Stochastic Control [2026-VSF] (LINK)

• [Lecture material 1] Mathematical Foundations of Reinforcement Learning (Shiyu Zhao)

• [Lecture material 2] Abstract Dynamic Programming (Dimitri P. Bertsekas)

• [Lecture material 3] Reinforcement Learning: An Overview (Kevin P. Murphy)

• [Lecture material 4] Multi-Agent Reinforcement Learning: Foundations and Modern Approaches (Stefano V. Albrecht)

• [Lecture material 5] Bandit Algorithms (Tor Lattimore)

• [Lecture material 6] Stochastic Controls: Hamiltonian Systems and HJB Equations, (Jiongmin Yong)

• Other materials (please send me email if you would like to ask)

Stochastic Theory and Biomedical Informatics Applications with Generative and Inference Modeling [2025-Fall] (LINK)

• [Lecture material 1] Understanding Diffusion Models - A Unified Perspective (Calvin Luo)

• [Lecture material 2] Tutorial on Diffusion Models for Imaging and Vision (Stanley H. Chan)

• [Lecture material 3] Flow Matching Guide and Code (Yaron Lipman)

• [Lecture material 4] An Introduction to Flow Matching and Diffusion Models (Peter Holderrieth)

• [Lecture material 5] Stochastic Interpolants - A Unifying Framework for Flows and Diffusions (Michael S. Albergo)

• [Lecture material 6] Conditional Stochastic Interpolation for Generative Learning (Ding Huang)

• [Lecture material 7] The Principles of Diffusion Models - From Origins to Advances (Chieh-Hsin Lai)

• [Lecture material 8] Foundations of Schrödinger Bridges for Generative Modeling (Sophia Tang)

• Other materials (please send me email if you would like to ask)

Data Mining and Machine Learning [2026-Spring] (LINK)

• [Lecture material 1] Understanding Machine Learning from Theory to Algorithms (Shai Shalev-Shwartz)

• [Lecture material 2] Pattern Recognition and Machine Learning (Christopher M. Bishop)

• [Lecture material 3] ML Machine Learning - A Probabilistic Perspective (Kevin P. Murphy)

• [Lecture material 4] Mathematical Foundations of Reinforcement Learning (Shiyu Zhao)

• [Lecture material 5] Reinforcement Learning - An Overview (Kevin P. Murphy) 

• Other materials (please send me email if you would like to ask) 

Introduction to Data Science [2025-Spring, 2026-Spring] (LINK)

• [Lecture material 1] Mathematics for Machine Learning (Marc Peter Deisenroth)

• [Lecture material 2] Introduction to Mathematical Statistics-Pearson (Robert V. Hogg)

• [Lecture material 3] Linear Algebra (Stephen H. Friedberg)

• [Lecture material 4] Data Science and Machine Learning (Dirk P. Kroese)

• Other materials (please send me email if you would like to ask)

Fundamentals of AI and Machine Learning for Biomedical Data [2024-Fall] (LINK)

• [Lecture material 1] Probabilistic Machine Learning: Introduction (Kevin Patrick Murphy)

• [Lecture material 2] Data Science and Machine Learning (Zdravko Botev)

• [Lecture material 3] Understanding Deep Learning (Simon J.D. Prince)

• [Lecture material 4] Dive into Deep Learning (Aston Zhang)

• Other materials (please send me email if you would like to ask)

Kyungsu Kim received his B.S. (with distinction) and M.S. degrees in Electrical and Computer Engineering from Seoul National University (SNU) in 2011 and 2013, respectively, where he was awarded the Distinguished M.S. Dissertation Award, and earned his Ph.D. in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 2019. He subsequently served as a Postdoctoral Researcher at the Graduate School of Artificial Intelligence at KAIST (2019–2020), a Principal Investigator (PI) Researcher at the Medical AI Research Center at Samsung Medical Center (2020–2022), and concurrently as an Adjunct Research Professor at the Sungkyunkwan University School of Medicine (2021–2023). From 2023 to 2024, he was a Postdoctoral Research Fellow in AI/ML at Massachusetts General Hospital and Harvard Medical School, with joint affiliation to the Kemper Research Institute at Harvard University. Since 2024, he has been an Assistant Professor at Seoul National University in the College of Transdisciplinary Innovations, with joint affiliations to the College of Medicine (Department of Biomedical Science) and the College of Engineering (Interdisciplinary Program in Artificial Intelligence) as an Adjunct Professor. He serves on the organizing committees and as an Area Chair at leading AI and machine learning conferences, including NeurIPS, ICLR, ICML, AAAI, and etc. He currently serves as an AI Advisor to several leading AI-focused global corporations, including OGQ-GYN, ASTERMORPH, CALICI, DTCRO, SAMSUNG, LG, DONG-A, etc. 

• B.S. Electrical and Computer Engineering, Seoul National University, Republic of Korea

• M.S. Electrical and Computer Engineering, Seoul National University, Republic of Korea

• Ph.D. Electrical Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea

• Postdoctoral Research Fellow / Sep. 2019 to July 2020

• Graduate School of AI, KAIST, Republic of Korea

• PI Researcher / Aug. 2020 to Dec. 2022

• AI Research Center, Samsung Medical Center, Republic of Korea

• Affiliated Research Professor / Aug. 2021 to June 2023

• School of Medicine, Sungkyunkwan University, Republic of Korea

• Postdoctoral Research Associate / Jan. 2023  to Aug. 2024

• Massachusetts General Hospital and Harvard Medical School, USA

• Kempner Institute, Harvard University, USA

• Assistant Professor / Sep. 2024 ~ Present

• School of Transdisciplinary Innovations and School of Medicine, Seoul National University, Republic of Korea

Core AI

• Multimodal AI 

• Generative/Diffusion AI 

• 3D/4D Reconstruction & Inverse Problem AI 

• Causality/Biomarker Discovery AI 

• Debiasing & Fairness AI 

• Neural Kernel & Bayesian Theory-based AI (Generalizability & Uncertainty Quantification)

• Natural Language AI  

• Unsupervised Anomaly Localization AI 

• Hardware-efficient Distillation AI

Biomedical AI 

• Molecular & Protein Engineering AI 

• Pathology & Genomic AI 

• Radiology AI 

• Ultrasound AI 

• Medical Language Model & Medical Human Computer Interaction AI

• College of Medicine & Hospital, Seoul National University, Seoul, South Korea

• College of Pharmacy, Seoul National University, Seoul, South Korea

• Massachusetts General Hospital and Harvard Medical School, Boston, USA

• Mclean Hospital and Harvard Medical School, Boston, USA

• Mayo Clinic, Floria, USA

• Johns Hopkins Department of Biomedical Engineering, Baltimore, USA

• Samsung Electronics DS R&D Center, San Jose, California, USA 

• Samsung Electronics AI Center, Seoul, South Korea 

• Stanford University, California, USA

• Samsung Medical Center, Seoul, South Korea 

• KAIST AI Research Institute, Daejeon & Seoul, South Korea

• Calici, Houston, Texas, USA

• OGQ-GYN, Seoul, South Korea

• JPI Healthcare, Seoul, South Korea 

• Tomocube, Daejeon, South Korea

• Asteromorph, Seoul, South Korea 

• DONGA-ST, Seoul, South Korea 

Advanced AI Analysis Equipment

• NVIDIA A100 / PRO6000 / A6000 / ETC

IT/Network Equipment

• NAS