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 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, Neurocomputing, 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 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, Mathematical Biosciences and Engineering (MBE), 2025
(with Y. Kim. K. Jun) Instance-dependent fixed-budget pure exploration in reinforcement Learning, ICLR, 2026 (ICML 2025 EXAIT Workshop)
(with N. Cho, Y. 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, submitted
(with M. Gim, H. Yang) Solving nonconvex Hamilton--Jacobi--Isaacs equations with PINN-based policy iteration, submitted
(with S. Choi, K. Kim) A diffusion-based generative model for financial time-series via geometric Brownian motion, submitted
(with N. Cho, M. Kim, Y. Kim) Neural policy iteration for stochastic optimal control: A physics-informed approach, submitted
(with K. Park, E. Kim) Human-in-the-loop diffusion for AI-driven topology optimization, submitted
(with J. Ahn, K. Lee, K. Lee) Discrete vs. continuous: Analyzing the impact of action space on reinforcement learning for facility layout planning, submitted
(with D. Lee, M. Kim, S. Son) A physics-informed, global-in-time neural particle method for the spatially homogeneous Landau equation, submitted
(with M. Gim, H. Yang) Hamilton--Jacobi--Isaacs formulation of probabilistic reachable sets via mesh-free policy iteration, submitted
(with K. Lee) How diffusion shapes greedy updates: A semigroup perspective, submitted
(with S. Jeong, J. Huh) Neural policy iteration for dynamic portfolio choice with control-dependent diffusion, submitted
(with N. Cho) Policy iteration for stationary discounted Hamilton--Jacobi--Bellman equations: A viscosity approach, submitted
(with D. Kwon, G. Montufar, I. Yang) Training Wasserstein GANs without gradient penalties, preprint
(with K. Park, Y. Choi) Flexible functional graded lattice structure via reinforcement learning, preprint
◾ Physics-Informed Approach for Exploratory Hamilton–Jacobi–Bellman Equations via Policy Iterations, AAAI, 2026김연응
◾ Hamilton-Jacobi based policy-iteration via deep operator learning, NEUROCOMPUTING, vol.646, 2025김연응
◾ Acceleration of grokking in learning arithmetic operations via Kolmogorov-Arnold representation, NEUROCOMPUTING, vol.640, 2025김연응
◾ Deep reinforcement learning for optimal design of compliant mechanisms based on digitized cell structures, ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol.151, 2025김연응
◾ On a minimum eradication time for the SIR model with time-dependent coefficients, Proceedings of the American Mathematical Society, 2025김연응
◾ 비가우시안 시스템에서의 베이지안 계수 추정, CDC 프로시딩, 2023김연응
학술대회
◾ 최예준,김연응,박근, 심층강화학습 기반 유연구동 메타 메커니즘 최적설계, 대한기계학회 창립 80주년 기념 학술대회 논문집, 하이원 그랜드 호텔, 2025김연응
◾ 김의현, 김연응,박근, 인간 피드백 결합 검열 샘플링 확산 모델을 이용한 위상 최적 설계, 한국정밀공학회 2025년도 추계학술대회 논문집, 여수 EXPO, 2025김연응
◾ 김연응, 김기훈, 박지환, 양인순, Approximate Thompson Sampling for Learning Linear Quadratic Regulators with $O(\sqrt{T})$ Regret, The proceedings of L4DC 2025, University of Michigan, 2025김연응
◾ 김민석, 김영종, 김연응, Physics-informed neural networks for optimal vaccination plan in SIR epidemic models, 대한수학회 2025 봄 연구발표회 논문집, 카이스트, 2025김연응
◾ 최예준,김연응,박근, Optimal design of soft gripper mechanisms combining finite element analysis and machine learning,, Proceedings of the 40th Annual Meeting of the Polymer Processing Society, 오클랜드 (뉴질랜드), 2025김연응
◾ Jae Yong Lee, Yeoneung Kim, 해밀톤 야코비 방정식에 기반한 작용소 학습, NA, Vietnam Academy of Science and Technology, 2025김연응
◾ 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
저역서
◾ 김연응, 행렬로 배우는 데이터 마이닝과 머신러닝, 역서, 9791125104896, 교우사, 2026김연응
연구프로젝트
우수신진연구 NRF), 책임연구원, 2026-2031
수리기계학습 선도연구센터(SRC), 참여연구원, 2023-2030
생애첫연구 (NRF), PI, 2023-2026
기타(학회활동 등)
대한수학회(KMS) 수학혁신전략위원회 (2025~)
한국응용수학회(KSIAM) 최적화 및 최적제어 분과 운영위원회
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