Jaewon Kim

Welcome! I am an undergraduate student in Semiconductor Engineering at Korea University and an undergraduate researcher at the Complex Data Analytics Lab (CDAL), advised by Prof. Junhee Seok.

My research focuses on machine learning methods with strong mathematical foundations. I am particularly interested in how concepts from probability, differential equations, and physics can be used to design more reliable and expressive learning algorithms. My current work spans generative models and deep reinforcement learning, with a broader interest in applying these methods to problems in science and engineering.

Research Interests

AI for Science
Generative Models
Deep Reinforcement Learning
Physics-Informed Machine Learning

news

Jun 18, 2026	Our paper `Fokker–Planck Soft Actor–Critic` has been accepted to `IEEE TNNLS`
Mar 27, 2025	Started `mandatory military service` as a Social Service Agent, continuing research activities during off-hours.
Oct 23, 2024	Presented `OpticalGAN` at the 12th SK Hynix Academic Conference, receiving the `Outstanding Poster Award`
Feb 20, 2024	I started an `undergraudate research internship` at CDAL

selected publications

TNNLS
Fokker–Planck Soft Actor–Critic

Hyo-Seok Hwang, Jaewon Kim, and Junhee Seok

IEEE Transactions on Neural Networks and Learning Systems, 2026

Abs Bib HTML

Learning expressive and multimodal policies is essential for solving complex continuous control tasks. We propose FP-SAC, which reinterprets soft policy improvement through stochastic differential equations and derives a distribution-level objective from the corresponding Fokker–Planck equation. By minimizing the PDE residual with physics-informed learning and leveraging normalizing flows, FP-SAC enables stable and accurate learning of multimodal policies, achieving higher performance and greater stability on multigoal, MuJoCo, and Meta-World benchmarks.
@article{hwang2026fpsac, title = {Fokker--Planck Soft Actor--Critic}, author = {Hwang, Hyo-Seok and Kim, Jaewon and Seok, Junhee}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, year = {2026}, publisher = {IEEE}, doi = {10.1109/TNNLS.2026.3699399}, }