Insoon Yang Associate Professor Electrical and Computer Engineering (ECE) Seoul National University (SNU) Room 708, Bldg 301 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea Phone: 82-2-880-8489 insoonyang at snu.ac.kr Research Group: Control and Optimization Research (CORE) Lab

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Academic Appointments

• Associate Professor (ECE), Seoul National University (SNU), 2020-
  
• Assistant Professor (ECE), Seoul National University (SNU), 2018-2020
  
• Assistant Professor (EE), University of Southern California (USC), 2016-2018
  
• Postdoctoral Associate (LIDS), Massachusetts Institute of Technology (MIT), 2015-2016
  

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Education

• Ph.D. (EECS), UC Berkeley, 2015 (advisor: Prof. Claire Tomlin)
  
• M.A. (Math), UC Berkeley, 2013 (advisor: Prof. Lawrence Craig Evans)
  
• M.S. (EECS), UC Berkeley, 2012
  
• B.S. (Math & MAE), Seoul National University, 2009
  

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Areas of Research

• Stochastic control and reinforcement learning
  
• Safe autonomy and risk management
  
• Optimization
  

Acknowledgement: Supported by Samsung Electronics DIT (2022-23), IITP RL (2022-26), IITP Competency-Aware ML (2022-26), SK Hynix AICC (2020-21), IITP Cloud Robot AI (2020-23), NRF Early Career (2020-24), Samsung Electronics DS (2018-23), NRF Basic Research Lab (2018-24), Sindoh (2018-2021), SNU Creative-Pioneering (2018-21), Seoul Digital Foundation (2018-19), NSF ECCS (2017-20), and NSF CNS (2017-19).

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Awards and Honors

• CoE Teaching Award, College of Engineering, Seoul National University, 2022
  
• ECE Teaching Award, ECE Dept., Seoul National University, 2020
  
• Creative-Pioneering Researcher, Seoul National University, 2018
  
• Best Student Paper Award Finalist, The 55th IEEE Conference on Decision and Control (CDC), 2016
  
• Eli Jury Award (Outstanding contribution to Systems, Communications, and Control), UC Berkeley, 2015
  
• University Outstanding Graduate Student Instructor Award, UC Berkeley, 2013
  
• EECS Distinguished Student Teaching Award, UC Berkeley, 2013
  
• Summa cum laude, Seoul National University, 2009
  
• Special Prize in Mathematics Olympiad for University Students, Korean Mathematical Society, (twice) 2003, 2004
  

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Publications

Journal Publications


1. Wasserstein distributionally robust control of partially observable linear stochastic systems
   Astghik Hakobyan, and Insoon Yang
   IEEE Transactions on Automatic Control, accepted.
   
   
2. Risk-aware Wasserstein distributionally robust control of vessels in natural waterways
   Juan Moreno Nadales, Astghik Hakobyan, David Munoz de la Pena, Daniel Limon, and Insoon Yang
   IEEE Transactions on Control Systems Technology, accepted.
   
   
3. Control of fab lifters via deep reinforcement learning: A semi-MDP approach
   Giho Kim, Jaeuk Shin, Gihun Kim, Joonrak Kim, and Insoon Yang
   IEEE Transactions on Automation Science and Engineering, accepted.
   
   
4. Anderson acceleration for partially observable Markov decision processes: A maximum entropy approach
   Mingyu Park, Jaeuk Shin, and Insoon Yang
   Automatica, 163:111557, 2024.
   
   
5. Distributionally robust differential dynamic programming with Wasserstein distance
   Astghik Hakobyan, and Insoon Yang
   IEEE Control Systems Letters, 7:2329-2334, 2023.
   (Selected for presentation at CDC 23)
   
   
6. Maximum entropy optimal control of continuous-time dynamical systems
   Jeongho Kim, and Insoon Yang
   IEEE Transactions on Automatic Control, 68(4):2018-2033, 2023.
   
   
7. Distributional robustness in minimax linear quadratic control with Wasserstein distance
   Kihyun Kim, and Insoon Yang
   SIAM Journal on Control and Optimization, 61(2):458-483, 2023.
   
   
8. Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approach
   Astghik Hakobyan, and Insoon Yang
   IEEE Transactions on Robotics, 39(1):718-737, 2023.
   
   
9. Risk-sensitive safety analysis using conditional value-at-risk
   Margaret P. Chapman, Riccardo Bonalli, Kevin M. Smith, Insoon Yang, Marco Pavone, and Claire J. Tomlin
   IEEE Transactions on Automatic Control, 67(12):6521-6536, 2022.
   
   
10. On representation formulas for optimal control: A Lagrangian perspective
    Yeoneung Kim, and Insoon Yang
    IET Control Theory & Applications (CTA), 16(16):1633-1644, 2022.
    
    
11. Infusing model predictive control into meta-reinforcement learning for mobile robots in dynamic environments
    Jaeuk Shin, Astghik Hakobyan, Mingyu Park, Yeoneung Kim, Gihun Kim, and Insoon Yang
    IEEE Robotics and Automation Letters, 7(2):10065-10072, 2022.
    (Selected for presentation at IROS 22)
    
    
12. Wasserstein distributionally robust motion control for collision avoidance using conditional value-at-risk
    Astghik Hakobyan, and Insoon Yang
    IEEE Transactions on Robotics, 38(2):939-957, 2022.
    
    
13. Stochastic consensus dynamics for nonconvex optimization on the Stiefel manifold: Mean-field limit and convergence
    Seung-Yeal Ha, Myeongju Kang, Dohyun Kim, Jeongho Kim, and Insoon Yang
    Mathematical Models and Methods in Applied Sciences (M3AS), 32(3):533-617, 2022.
    
    
14. Hamilton-Jacobi deep Q-learning for deterministic continuous-time systems with Lipschitz continuous controls
    Jeongho Kim, Jaeuk Shin, and Insoon Yang
    Journal of Machine Learning Research (JMLR), 22(206):1-34, 2021.
    
    
15. Wasserstein distributionally robust stochastic control: A data-driven approach
    Insoon Yang
    IEEE Transactions on Automatic Control, 66(8):3863-3870, 2021.
    
    
16. Appropriate smart factory for SMEs: Concept, application and perspective
    Woo-Kyun Jung, Dong-Ryul Kim, Hyunsu Lee, Tae-Hun Lee, Insoon Yang, Byeng D. Youn, Daniel Zontar, Matthias Brockmann, Christian Brecher, and Sung-Hoon Ahn
    International Journal of Precision Engineering and Manufacturing, 22:201-215, 2021.
    
    
17. A convex optimization approach to dynamic programming in continuous state and action spaces
    Insoon Yang
    Journal of Optimization Theory and Applications (JOTA), 187:133-157, 2020.
    
    
18. Multi-objective predictive taxi dispatch via network flow optimization
    Beomjun Kim, Jeongho Kim, Subin Huh, Seungil You, and Insoon Yang
    IEEE Access, 8:21437-21452, 2020.
    
    
19. Data-driven distributionally robust stochastic control of energy storage for wind power ramp management using the Wasserstein metric
    Insoon Yang
    Energies, 12(23):4577, 2019.
    
    
20. Risk-aware motion planning and control using CVaR-constrained optimization
    Astghik Hakobyan, Gyeong Chan Kim, and Insoon Yang
    IEEE Robotics and Automation Letters, 4(4):3924-3931, 2019.
    (Selected for presentation at IROS 19)
    
    
21. Sample efficient home power anomaly detection in real time using semi-supervised learning
    Xinlin Wang, Insoon Yang, and Sung-Hoon Ahn
    IEEE Access, 7:139712-139725, 2019.
    
    
22. Submodularity of storage placement optimization in power networks
    Junjie Qin, Insoon Yang, and Ram Rajagopal
    IEEE Transactions on Automatic Control, 64(8):3268-3283, 2019.
    (Conference version selected as Best Student Paper Award finalist at CDC 16)
    
    
23. A dynamic game approach to distributionally robust safety specifications for stochastic systems
    Insoon Yang
    Automatica, 94:94-101, 2018.
    
    
24. Smart machining process using machine learning: A review and perspective on machining industry
    Dong-Hyeon Kim, Thomas J. Y. Kim, Xinlin Wang, Mincheol Kim, Ying-Jun Quan, Jin Woo Oh, Soo-Hong Min, Hyungjung Kim, Binayak Bhandari, Insoon Yang, and Sung-Hoon Ahn
    International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4):555-568, 2018.
    (IJPEM-GT Most Cited Article Award, 2020)
    
    
25. A convex optimization approach to distributionally robust Markov decision processes with Wasserstein distance
    Insoon Yang
    IEEE Control Systems Letters, 1(1):164-169, 2017.
    (Selected for presentation at CDC 17)
    
    
26. Optimal control of conditional value-at-risk in continuous time
    Christopher W. Miller, and Insoon Yang
    SIAM Journal on Control and Optimization, 55(2):856-884, 2017.
    
    
27. Variance-constrained risk sharing in stochastic systems
    Insoon Yang, Duncan S. Callaway, and Claire J. Tomlin
    IEEE Transactions on Automatic Control, 62(4):1865-1879, 2017.
    
    
28. Approximation algorithms for optimization of combinatorial dynamical systems
    Insoon Yang, Samuel A. Burden, Ram Rajagopal, S. Shankar Sastry, and Claire J. Tomlin
    IEEE Transactions on Automatic Control, 61(9):2644-2649, 2016.
    
    
29. Reaction-diffusion systems in protein networks: global existence and identification
    Insoon Yang, and Claire J. Tomlin
    Systems & Control Letters, 74:50-57, 2014.
    
    
30. Micro ECM with ultrasonic vibrations using a semi-cylindrical tool
    Insoon Yang, Min Su Park, and Chong Nam Chu
    International Journal of Precision Engineering and Manufacturing, 10(2):5-10, 2009.
    


Conference Publications


1. On task-relevant loss functions in meta-reinforcement learning and online LQR
   Jaeuk Shin, Giho Kim, Howon Lee, Joonho Han, and Insoon Yang
   Learning for Dynamics and Control (L4DC), 2024.
   
   
2. Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels
   Jungbin Kim, and Insoon Yang
   Advances in Neural Information Processing Systems (NeurIPS), 2023.
   
   
3. Data-driven stochastic optimization using upper confidence bounds: Performance guarantees and distributional robustness
   Youngchae Cho, and Insoon Yang
   IEEE Conference on Decision and Control (CDC), 2023.
   
   
4. On concentration bounds for Bayesian Identification of linear non-Gaussian systems
   Yeoneung Kim, Gihun Kim, and Insoon Yang
   IEEE Conference on Decision and Control (CDC), 2023.
   
   
5. Unifying Nesterov's accelerated gradient methods for convex and strongly convex objective functions
   Jungbin Kim, and Insoon Yang
   International Conference on Machine Learning (ICML), 2023. (Oral, acceptance rate: 2.4%)
   
   
6. Distributionally robust optimization with unscented transform for learning-based motion control in dynamic environments
   Astghik Hakobyan, and Insoon Yang
   IEEE International Conference on Robotics and Automation (ICRA), 2023.
   
   
7. Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture MDPs
   Yeoneung Kim, Insoon Yang, and Kwang-Sung Jun
   Advances in Neural Information Processing Systems (NeurIPS), 2022.
   
   
8. Wasserstein distributionally robust control of partially observable linear systems: Tractable approximation and performance guarantee
   Astghik Hakobyan, and Insoon Yang
   IEEE Conference on Decision and Control (CDC), 2022.
   
   
9. On affine policies for Wasserstein distributionally robust unit commitment
   Youngchae Cho, and Insoon Yang
   IEEE Conference on Decision and Control (CDC), 2022.
   
   
10. Accelerated gradient methods for geodesically convex optimization: Tractable algorithms and convergence analysis
    Jungbin Kim, and Insoon Yang
    International Conference on Machine Learning (ICML), 2022.
    
    
11. On Anderson acceleration for partially observable Markov decision processes
    Melike Ermis, Mingyu Park, and Insoon Yang
    IEEE Conference on Decision and Control (CDC), 2021.
    
    
12. Toward Improving the distributional robustness of risk-aware controllers in learning-enabled environments
    Astghik Hakobyan, and Insoon Yang
    IEEE Conference on Decision and Control (CDC), 2021.
    
    
13. Minimax control of ambiguous linear stochastic systems using the Wasserstein metric
    Kihyun Kim, and Insoon Yang
    IEEE Conference on Decision and Control (CDC), 2020.
    
    
14. A stochastic consensus method for nonconvex optimization on the Stiefel manifold
    Jeongho Kim, Myeongju Kang, Dohyun Kim, Seung-Yeal Ha, and Insoon Yang
    IEEE Conference on Decision and Control (CDC), 2020.
    
    
15. A3DQN: Adaptive Anderson acceleration for deep Q-networks
    Melike Ermis, and Insoon Yang
    IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2020.
    
    
16. Learning-based distributionally robust motion control with Gaussian processes
    Astghik Hakobyan, and Insoon Yang
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.
    
    
17. Hamilton-Jacobi-Bellman equations for Q-learning in continuous time
    Jeongho Kim, and Insoon Yang
    Learning for Dynamics and Control (L4DC), 2020.
    
    
18. Wasserstein distributionally robust motion planning and control with safety constraints using conditional value-at-risk
    Astghik Hakobyan, and Insoon Yang
    IEEE International Conference on Robotics and Automation (ICRA), 2020.
    
    
19. Stochastic subgradient methods for dynamic programming in continuous state and action spaces
    Sunho Jang, and Insoon Yang
    IEEE Conference on Decision and Control (CDC), 2019.
    
    
20. On improving the robustness of reinforcement learning-based controllers using disturbance observer
    Jeong Woo Kim, Hyungbo Shim, and Insoon Yang
    IEEE Conference on Decision and Control (CDC), 2019.
    
    
21. Safety-aware optimal control of stochastic systems using conditional value-at-risk
    Samantha Samuelson, and Insoon Yang
    American Control Conference (ACC), 2018. (Extended version)
    
    
22. Distributionally robust stochastic control with conic confidence sets
    Insoon Yang
    IEEE Conference on Decision and Control (CDC), 2017.
    
    
23. Data-driven distributionally robust control of energy storage to manage wind power fluctuations
    Samantha Samuelson, and Insoon Yang
    IEEE Conference on Control Technology and Applications (CCTA), 2017.
    
    
24. Online combinatorial optimization for Interconnected refrigeration systems: linear approximation and submodularity
    Insoon Yang
    IFAC World Congress (WC), 2017.
    
    
25. Submodularity of energy storage placement in power networks
    Junjie Qin, Insoon Yang, and Ram Rajagopal
    IEEE Conference on Decision and Control (CDC), 2016.
    (Best Student Paper Award finalist)
    
    
26. Reducing electricity price volatility via stochastic storage control
    Insoon Yang, and Asuman E. Ozdaglar
    American Control Conference (ACC), 2016.
    
    
27. Indirect load control for financial risk management in electricity markets via risk-limiting dynamic contracts
    Insoon Yang, Duncan S. Callaway, and Claire J. Tomlin
    American Control Conference (ACC), 2015.
    
    
28. Utility learning model predictive control for personal electric loads
    Insoon Yang, Melanie N. Zeilinger, and Claire J. Tomlin
    IEEE Conference on Decision and Control (CDC), 2014.
    
    
29. Direct load control for electricity market risk management via risk-limiting dynamic contracts
    Insoon Yang, Duncan S. Callaway, and Claire J. Tomlin
    Allerton Conference on Communication, Control, and Computing, 2014.
    
    
30. Path integral formulation of stochastic optimal control with generalized costs
    Insoon Yang, Matthias Morzfeld, Claire J. Tomlin, and Alexandre J. Chorin
    IFAC World Congress (WC), 2014.
    
    
31. Dynamic contracts with partial observations: application to indirect load control
    Insoon Yang, Duncan S. Callaway, and Claire J. Tomlin
    American Control Conference (ACC), 2014.
    
    
32. Infinitesimal interconnection variation in nonlinear networked systems
    Insoon Yang, Samuel A. Burden, S. Shankar Sastry, and Claire J. Tomlin
    IEEE Conference on Decision and Control (CDC), 2013.
    
    
33. Regularization-based identification for level set equations
    Insoon Yang, and Claire J. Tomlin
    IEEE Conference on Decision and Control (CDC), 2013.
    
    
34. Identification of surface tension in mean curvature flow
    Insoon Yang, and Claire J. Tomlin
    American Control Conference (ACC), 2013.
    
    
35. One-shot computation of reachable sets for differential games
    Insoon Yang, Sabine Becker-Weimann, Mina J. Bissell, and Claire J. Tomlin
    ACM International Conference on Hybrid Systems: Computation and Control (HSCC), 2013.
    


Book Chapters


1. STAR: Spatio-temporal prediction of air quality using a multimodal approach
   Tien-Cuong Bui, Joonyoung Kim, Taewoo Kang, Donghyeon Lee, Junyoung Choi, Insoon Yang, Kyomin Jung, and Sang Kyun Cha
   In: Intelligent Systems and Applications, vol. 1251, pp. 389-406, Springer, 2021.
   

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Teaching

• Stochastic Control and Reinforcement Learning, Sp18, Sp20, Sp21, Sp22, Sp23 (SNU)
• Optimization Theory and Applications, Fa19, Fa20, Sp21, Sp22 (SNU)
• Deep Reinforcement Learning, Su19, Su20, Su21, Su22, Su23 (Samsung)
• Advanced Control, Fa20, Fa21, Fa22 (SNU)
• Feedback Control Systems, Fa19 (SNU)
• Introduction to Robotics and Autonomous Systems, Sp19 (SNU)
• Optimal Control Theory, Fa18 (SNU)
• Linear Algebra for Engineering (EE510), Fa17 (USC)
• Linear System Theory (EE585), Fa16 (USC)

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Brief Biography

Insoon Yang is an Associate Professor of Electrical and Computer Engineering at Seoul National University. He received B.S. degrees in Mathematics and in Mechanical Engineering (summa cum laude) from Seoul National University in 2009; and an M.S. in EECS, an M.A. in Mathematics and a Ph.D. in EECS from UC Berkeley in 2012, 2013 and 2015, respectively. He was an Assistant Professor of Electrical Engineering at University of Southern California from 2016 to 2018, and a Postdoctoral Associate with the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology from 2015 to 2016. His research interests are in stochastic control and optimization, and reinforcement learning, with applications in safe autonomy and risk management. He is a recipient of the 2015 Eli Jury Award and a finalist for the Best Student Paper Award at the 55th IEEE Conference on Decision and Control 2016.

He is an associate editor for the IEEE Open Journal of Control Systems, IEEE Control Systems Letters, and IEEE CSS Conference Editorial Board, and a vice-chair of the IFAC Technical Committee on Stochastic Systems.

Erdös number: 3 (S. Shankar Sastry - Béla Bollobás - Paul Erdös)

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