(with T. Costa, J. Cummings, M. Jenkinson, J. Martinez, N. Olivares, A. Sezginer) Fast calculation of diffraction by photomasks, Univeristy 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 D. Kwon, G. Montufar, I. Yang) Training Wasserstein GANs without gradient penalties, submitted

(with K. Jun, I. Yang) Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs, Advances in Neural Information Processing Systems (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 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 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 J. Jang) On a minimum eradication time for SIR model with time-dependent coefficients,  Proceedings of the AMS, 2025

(with N. Cho) On the stability of Lipschitz continuous control problems and its application to reinforcement learning, submitted

(with Y. Park, M. Kim) Acceleration of grokking in learning arithmetic operations via Kolmogorov-Arnold representation, submitted

(with J. Lee) Hamilton–Jacobi Based Policy-Iteration via Deep Operator Learning, submitted

(with Y. Choi, K. Park) Deep Reinforcement Learning for the Design of Metamaterial Mechanisms with Functional Compliance Control, Engineering Applications of Artificial Intelligence, 2025

(with N. Cho) Physics-Informed Neural Network for model-based reinforcement learning, submitted

(with Y. Kim, M. Kim) Physics-Informed Neural Networks for optimal vaccination plan in SIR epidemic models, submitted