Welcome to the
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Research 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 theory and algorithms for stochastic control, filtering & estimation, and autonomous decision-making by combining traditional model-based methods with modern data-driven AI methods.
Our research spans a broad range of applications, including motion-planning, fault-tolerance, robotics, reinforcement learning, multi-agent systems, sensor networks, and embodied AI.

See our individual projects for more.

Research | 연구

The research undertaken by ACSS is roughly distinguished by 3 major overlapping categories.
Click on each + to view the projects within each category.

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AUTONOMOUS DECISION-MAKING UNDER SYSTEM & ENVIRONMENT UNCERTAINTIES
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FAST, ANTI-REDUNDANT SITUATIONAL AWARENESS FOR LARGE ENVIRONMENTS
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SAMPLE- & MEMORY-EFFICIENT LEARNING FOR MULTI-TASK DECISION-MAKING

Selected Publications | 논문

  1. Jeongyong Yang, KwangBin Lee, SooJean Han, "Hybrid Conformal Prediction-based Risk-Aware Model Predictive Planning in Dense, Uncertain Environments." Under review, IEEE Conference on Decision and Control (CDC), Mar 2025.

  2. Eunwoo Sung, SooJean Han, "Distributed Kalman-IMM Cooperative Estimator Synthesis for Large-Scale Networked Switching Systems." Under review, IEEE Conference on Decision and Control (CDC), Mar 2025.

  3. Hyeongmin Choe, SooJean Han, "Advantages of Feedback in Distributed Data-Gathering for Accurate and Power-Efficient State-Estimation." Under review, IEEE Conference on Decision and Control (CDC), Mar 2025.

  4. Jeongyong Yang, Hojin Ju, SooJean Han, "Curvature and Energy-based Trajectory Optimization in Unstructured Environments." (in Korean.) Korean Robotics Conference (KRoC), 2025.

  5. Seunghwan Jang, SooJean Han, "Comparison of Compression Codec Algorithms to Image Datasets in 3D Gaussian Splatting Models." (in Korean.) Workshop on Image Processing and Image Understanding (IPIU), 2025.

  6. SooJean Han*, Minwoo Kim*, Ieun Choo, "A Stochastic Robust Adaptive Systems Level Approach to Stabilizing Large-Scale Uncertain Markovian Jump Linear Systems." IEEE Conference on Decision and Control (CDC), Sep 2024. [PDF]

  7. Mukul Chodhary, Kevin Octavian, SooJean Han, "Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control." IEEE Intelligent Transportation Systems Conference (ITSC), Jul 2024. [PDF]

  8. 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]

  9. 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]

PI | 교수

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SooJean Han, Ph.D. (한수진)

Personal Website (개인 홈페이지): [Link]

PhD Students | 박사과정

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Minseok Jeong (정민석)

Incoming Spring 2025

MS Students | 석사과정

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Jeongyong Yang (양정용)

Jan 2024-

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Hojin Ju (주호진)

Jan 2024-

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Eun-Woo Sung (성은우)

Feb 2024-

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Sanghun Park (박상훈)

Feb 2024-

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Jeonghyeon Noh (노정현)

Aug 2024-

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Seunghwan Jang (장승환)

Aug 2024-

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Kwang Bin Lee (이광빈)

Aug 2024-

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Jungyo Jung (정준교)

Feb 2025-

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Hyeongmin Choe
(최형민)

Feb 2025-

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Hyewon Choi (최혜원)

Nov 2024-

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Donggyu Kim (김동규)

Feb 2025-
[LinkedIn]

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Yechan Lee (이예찬)

Feb 2025-

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Nakgyu Yang (양낙규)

Feb 2025-

Research Interns | 연구인턴

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Nijat Abbasov

KAIST URP Fellow
[LinkedIn]

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Doyoung Heo (허도영)