Welcome to the
Autonomous Control for Stochastic Systems Lab
About Us

Overview | 요약

Stochastic processes and stochastic dynamical systems are widely used in many fields to model real-world systems affected by random phenomena (e.g., inherent uncertainties, external disturbances, measurement noise). Optimal control and estimation in the presence of these random phenomena pose many unique challenges that are absent from deterministic systems. Thus, stochastic systems remains a topic of high interest in the engineering community.

Recently, artificial intelligence (AI) has been immensely useful in the development of tools for stochastic control and estimation, including data-driven control policies and black-box modeling of highly complex dynamics. However, two well-known issues with AI-based methods (specifically, model-free methods) are its reliance on the availability of good-quality data and lengthy training time. While model-based methods, on the other hand, do not suffer from these issues, they often only work for systems under simple or restrictive assumptions, e.g., Gaussian white noise. This suggests that examining the tradeoff between model-free and model-based methods gives to us a wide spectrum of methods for handling stochastic systems.

Our Goal | 목적

We develop intelligent methods for stochastic control, estimation, and decision-making based on efficient learning. Our research spans a broad range of applications, including traffic management of unmanned (aerial) vehicles, path-planning, robotic systems and reinforcement learning, and distributed sensor networks.

See our individual projects for more.

Research | 연구

Click on each + to view the project in detail. | 해당 프로젝트에 각 + 를 누르면 자세히 보기 가능.

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Fault-Tolerant Routing in Dynamic Environments
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Biology-Inspired Distributed Target-Tracking
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Large-Scale Traffic Congestion Control
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Topology of Multi-Agent Systems
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Heterogeneous Memory for Decision-Making
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Controller Architectures via Learning Patterns

Selected Publications | 선정 논문

  1. SooJean Han, Minwoo Kim, Ieun Choo, "A Stochastic Robust Adaptive Systems Level Approach to Stabilizing Large-Scale Uncertain Markovian Jump Linear Systems." To Appear, IEEE Conference on Decision and Control (CDC), Sep 2024.
  2. Mukul Chodhary, Kevin Octavian, SooJean Han, "Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control." To Appear, IEEE Intelligent Transportation Systems Conference (ITSC), Jul 2024.
  3. SooJean Han, Soon-Jo Chung, John C. Doyle, "Predictive Control of Linear Discrete-Time Markovian Jump Systems by Learning Recurrent Patterns." Automatica, May 2023. [Link] [PDF]
  4. SooJean Han, Soon-Jo Chung, "Incremental Nonlinear Stability Analysis for Stochastic Systems Perturbed by Lévy Noise." International Journal of Robust and Nonlinear Control, vol. 32, no. 12, p. 7174-7201, Aug 2022. [Link] [PDF]
  5. SooJean Han, Michelle Effros, Richard Murray, "OUTformation: Distributed Data Gathering with Feedback under Unknown Environment and Communication Delay Constraints." ArXiv, Aug 2022. [PDF]
  6. SooJean Han, "Localized Learning of Robust Controllers for Networked Systems with Dynamic Topology." Learning for Dynamics and Control Conference (L4DC), Jun 2020. [PDF]

PI | 교수

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SooJean Han, Ph.D. (한수진)

Personal Website (개인 홈페이지): [Link]

MS Students | 석사과정

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Jeongyong Yang (양정용)

Jan 2024-

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Sanghun Park (박상훈)

Feb 2024-

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Eun-Woo Sung (성은우)

Feb 2024-

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Hojin Ju (주호진)

Jan 2024-

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Jeonghyeon Noh (노정현)

Incoming Fall 2024.

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Seunghwan Jang (장승환)

Incoming Fall 2024.

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Kwang Bin Lee (이광빈)

Incoming Fall 2024.

Research Interns | 연구인턴

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Hyeongmin Choe
(최형민)
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Jungyo Jung
(정준교)
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Donggyu Kim (김동규)

[LinkedIn]

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Nijat Abbasov

[LinkedIn]

Past Affiliates

  • Doyoung Heo (허도영). Undergraduate Intern.
  • Sanghyun Lee (이상현). Undergraduate Intern.
  • Yejun Jang (장예준). Undergraduate Intern. [LinkedIn]
  • Jerry Song (송제리). Undergraduate Intern.
  • Seongjun Mun (문성준). Undergraduate Intern.

If you are interested in joining our team, please email me at soojean@kaist.ac.kr
연구실에 관심있는 박사/석사/학부생들은 이메일을 보내주세요: soojean@kaist.ac.kr