Machine Learning and Control Systems Laboratory
Vision
We strive to solve challenging societal problems by exploiting powerful modern computational and mathematical techniques from machine learning, systems and control, system identification, and Bayesian statistics. Our laboratory has been conducting, and continuously seeks for intriguing and multidisciplinary projects with biologists, scientists, medical clinicians, and researchers from robotic and game industries, including building artificial intelligence for self-driving and manipulating mobile robots interacting with people in dynamical and uncertain environments; Bayesian prediction and adaptive sampling algorithms for multiagent systems (e.g., unmanned ariel vehicles, i.e., UAVs) interacting with uncertain random fields; medical machine vision and data-driven, patient-specific calibration of computational models; and intent-analysis and physical human-robot interaction (pHRI) for gauging and rehabilitating human motor control systems. As results, our solutions are creative and successful with intellectual merits, and happen to synergistically combine recent advances in machine learning techniques such as deep learning, reinforcement learning, inverse reinforcement learning, and Gaussian process regression with engineering, mathematical, and/or statistical techniques such as convex optimization-based control synthesis, system identification, and Bayesian inferential methods.
NEWS
- MLCS's 20 new publications.
- Professor Choi serves
- as a Guest Editor with a two-year term (2021 and 2022) for IEEE/ASME TMECH/AIM Focused Section on Emerging Topics.
- as an Associate Editor for 2021 IEEE International Conference on Robotics and Automation.
- IEEE Robotics and Automation Letters. Review timeline is given here. RAS Conference/Journal Management System.
- ASME Journal of Dynamic Systems, Measurement and Control. ASME Journal Management System.
- International Journal of Precision Engineering and Manufacturing. IJPEM Journal Management System.

