학력
2011. 9 ~ 2019. 5, Ph.D. in Mathematics (minor in computer science), University of Wisconsin-Madison, USA
2008. 3 ~ 2011. 2, B.S. in Mathematics, POSTECH, Korea
주요 경력
2023.6. ~ current Assistant Professor, SeoulTech, Korea
2022.3. ~ 2023.5. Assistant Professor, Gachon University, Korea
2021.6. ~ 2022.2. BK21 postdoctoral Researcher, Seoul National University, Korea
2019.6. ~ 2021.5. Staff Engineer, Samsung Electronics, Korea
2013.12. ~ 2016.12. Researcher, National Institute of Mathematical Sciences (NIMS), Korea
주요논문 및 저서
Well-posedness for constrained Hamilton-Jacobi equations, Acta Applicandae Mathematicae, 2020
(with H. V. Tran, S. N.T. Tu) State-constraint static Hamilton-Jacobi equations in nested domains, SIAM Journal on Mathematical Analysis, 2020
(with I. Yang) On Representation Formulas for Optimal Control: A Lagrangian Perspective, IET Control Theory & Applications, 2022
(with I. Yang, K. Jun) Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs, Advances in Neural Information Processing Systems (NeurIPS), 2022
저널 논문
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, S. N.T. Tu) State-constraint static Hamilton-Jacobi equations in nested domains, SIAM Journal on Mathematical Analysis, 2020
(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 I. Yang, K. Jun) Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs, Advances in Neural Information Processing Systems (NeurIPS), 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, selected for oral presentation), 2025
(with D. Kwon, G. Montufar, I. Yang) Training Wasserstein GANs without gradient penalties, preprint
(with J. Jang) On a minimum eradication time for SIR model with time-dependent coefficients, Proceedings of the AMS, 2025
(with J. Lee) Hamilton-Jacobi Based Policy-Iteration via DeepOnet, preprint
(with N. Cho) On the stability of Lipschitz continuous control problems and its application to reinforcement learning, preprint
(with Y. Park, M. Kim) Acceleration of grokking in learning arithmetic operations via Kolmogorov-Arnold representation, submitted
(with Y. Choi, K. Park) Deep Reinforcement Learning for the Design of Metamaterial Mechanisms with Functional Compliance Control, Engineering Applications of Artificial Intelligence (EAAI), 2025
(with Y. Kim, M. Kim) Physics-Informed Neural Networks for optimal vaccination plan in SIR epidemic models, submitted
◾ 비가우시안 시스템에서의 베이지안 계수 추정, CDC 프로시딩, 2023김연응
학술대회
◾ 최예준, 김연응, 양희진, 박근, 심층강화학습 기반 유연 구동 그리퍼 메커니즘 설계, 2024년 대한기계학회 학술대회 논문집, 제주국제컨벤션센터, 2024김연응
◾ 이재용, 김연응, 해밀톤 야코비 방정식과 심층 작용소 학습에 기반한 정책 추정, 대한수학회:학술대회논문집, 성균관대학교, 2024김연응
◾ 김연응, 김기훈, 양인순, 톰슨 샘플링을 활용한 선형제어 시스템 학습, 톰슨 샘플링을 활용한 선형제어 시스템 학습, 캐나다, UBC, 2024김연응
◾ 최예준, 박근, 김연응, 강화학습을 활용한 메타메커니즘 디자인, SFF 심포지움 프로시딩, 텍사스 오스틴 힐튼호텔, 2024김연응
◾ Yeoneung Kim, Gihun Kim, Insoon Yang, 비가우시안 시스템에서의 베이지안 계수 추정, Proceeding of 62nd IEEE Conference on Decision and Control, 싱가폴 마리나베이 호텔, 2023김연응
연구프로젝트
선도센터(SRC), 참여연구원, 2023-2030
생애첫연구 (NRF), PI, 2023-2026
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