Yeoneung Kim (PI)

I'm an assistant professor at the Department of Applied Artificial Intelligence at Seoul National University of Science and Technology and PI of Mathematical Machine Learning (MML) lab.

I obtained my Ph.D in mathematics from the University of Wisconsin-Madison under the supervision of Hung Vinh Tran (http://math.wisc.edu/~hung) and did my undergraduate at Pohang University of Science and Technology (POSTECH).

My research interest lies in partial differential equations, optimal control, and machine learning.

Contact

Address

Department of Applied Artificial Intelligence, 232, Gongneung-ro, Nowon-gu, Seoul, Republic of Korea

yeoneung at seoultech.ac.kr

I'm an assistant professor at the Department of Applied Artificial Intelligence at Seoul National University of Science and Technology and PI of Mathematical Machine Learning (MML) lab.

My research interest lies in partial differential equations, optimal control, and machine learning.

Contact

Address

Department of Applied Artificial Intelligence, 232, Gongneung-ro, Nowon-gu, Seoul, Republic of Korea

yeoneung at seoultech.ac.kr

Publication

(with T. Costa, J. Cummings, M. Jenkinson, J. Martinez, N. Olivares, A. Sezginer) Fast calculation of diffraction by photomasks, University of Minnesota. IMA, 2014
Constrained Hamilton--Jacobi equations and further applications via optimal control theory, Ph.D thesis, 2019
Well-posedness for constrained Hamilton--Jacobi equations, Acta Applicandae Mathematicae, 2020
On uniqueness for one-dimensional constrained Hamilton--Jacobi equation, Minimax Theory and its Applications, 2020
(with H. V. Tran and S. N.T. Tu) State-constraint static Hamilton-Jacobi equations in nested domains, SIAM Journal on Mathematical Analysis, 2020
(with K. Jun, I. Yang) Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs, NeurIPS, 2022
(with J. Shin, A. Hakobyan, M. Park, G. Kim, I. Yang) Infusing model predictive control into meta-reinforcement learning for mobile robots in dynamic environments, IEEE Robotics and Automation Letters, 2022
(with I. Yang) On Representation Formulas for Optimal Control: A Lagrangian Perspective, IET Control Theory & Applications, 2022
(with K. Kim, I. Yang) On concentration bounds for Bayesian identification of linear non-Gaussian systems, Proceedings of the 62th IEEE Conference on Decision and Control (CDC), 2023
(with K. Kim, I. Yang) Approximate Thompson sampling for learning linear quadratic regulators with O(\sqrt{T}) regret, Leraning for Control and Decision Conference (L4DC), 2025
(with J. Jang) On a minimum eradication time for the SIR model with time-dependent coefficients, Proceedings of the AMS, 2025
(with Y. Park, M. Kim) Acceleration of grokking in learning arithmetic operations via Kolmogorov-Arnold representation, Neurocomputing, 2025
(with Y. Choi, K. Park) Deep reinforcement learning for optimal design of compliant mechanisms based on digitized cell structures, Engineering Applications of Artificial Intelligence, 2025
(with Y. Kim, M. Kim) Physics-Informed Neural Networks for optimal vaccination plan in SIR epidemic models, Mathematical Biosciences and Engineering, 2025
(with J. Lee) Hamilton--Jacobi based policy-iteration via deep operator learning, Neurocomputing, 2025
(with Y. Kim. K. Jun) Instance-dependent fixed-budget pure exploration in reinforcement Learning, ICML EXAIT Workshop, 2025
(with N. Cho, Y. Kim, K. Kim) Physics-informed approach for exploratory Hamilton--Jacobi--Bellman equations via policy iterations, AAAI, 2026
(with N. Cho) On the stability of Lipschitz continuous control problems and its application to reinforcement learning, under review
(with M. Gim, H. Yang) Solving nonconvex Hamilton--Jacobi--Isaacs equations with PINN-based policy iteration, under review
(with S. Choi, K. Kim) A diffusion-based generative model for financial time-series via geometric Brownian motion, under review
(with N. Cho, Y. Kim) Neural policy iteration for stochastic optimal control: A physics-informed approach, under review
(with K. Park, E. Kim) Human-in-the-loop diffusion for AI-driven topology optimization, under review

(For the prospective students or potential collaborators) The following is a list of projects I have been working on.

Preprints and projects in progress

(with D. Kwon, G. Montufar, I. Yang) Training Wasserstein GANs without gradient penalties
(with D. Lee, M. Kim, S. Son) Physics-informed approach for solving the space-homogeneous Landau equation
(with K. Park, Y. Choi) Flexible functional graded lattice structure via reinforcement learning
(with S. Choi, K. Kim) Generation of financial time-series via CEV process
(with N. Cho, M. Kim) Physics-informed neural network for model-based reinforcement learning
(with Y. Park, J. Kim) Heterogeneous controlled SIR model
(with M. Gim, H. Yang) Stochastic reachability via neural policy iteration
(with M. Gim, H. Yang) FDE-PINN for stochastic differential game and Hamilton--Jacobi--Iassacs equations
(with Y. Kim, N. Cho) Physics-informed approach for solving state-constraint Hamilton--Jacobi equations
(with M. Kim, Y. Kim) Physics-informed approach for solving the time-varying minimum eradication problem of SIR model
(with M. Yoo) TBA

Education

Ph.D. Mathematics (minor in computer science), 2011.9 - 2019.5 (military service 2013.12 - 2016.12)

University of Wisconsin-Madison, Madison, WI, USA

B.S. Mathematics, Summa Cum Laude, 2008.3 - 2011.2

POSTECH (Pohang University of Science and Technology), Pohang, Korea

Expereience

Assistant Professor, Department of Applied Artificial Intelligence, SeoulTech (2023.6 - present)

Assistant Professor, Department of Financial Mathematics, Gachon University (2022.3 - 2023.5)

BK21 Post-Doc, Seoul National University (2021.6 - 2022.2)

Staff Engineer, Samsung Electronics (2019.5 - 2021. 5)

Researcher, National Institute for Mathematical Sciences (NIMS) (2013.12 - 2016. 12)

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