Welcome to Jongeun's homepage.

Jongeun Choi received his Ph.D. and M.S. degrees in Mechanical Engineering from the University of California at Berkeley in 2006 and 2002 respectively. He also received a B.S. degree in Mechanical Design and Production Engineering from Yonsei University at Seoul, Republic of Korea in 1998. He is currently an Associate Professor with the Departments of Mechanical Engineering and Electrical and Computer Engineering at the Michigan State University.

His current research interests include systems and control, system identification, and Bayesian methods, with applications to mobile robotic sensors, environmental adaptive sampling, engine control, neuromusculoskeletal systems, and biomedical problems. His papers were finalists for the Best Student Paper Award at the 24th American Control Conference (ACC) 2005 and the Dynamic System and Control Conference (DSCC) 2011 and 2012. He is a recipient of an NSF CAREER Award in 2009. Dr. Choi is a member of ASME.

News

Andrew White has been nominated (by the Dept. of Mechanical Engineering) for the 2012 Fitch Beach Award. He will give a brief presentaion at the Engineering Graduate Research Symposium on Nov. 9th 2012 in the MSU Breslin Center.
Profs. Baek (PI) and Choi receive an NIH R01 grant with a project title: Growth and Remodeling Model of Abdominal Aortic Aneurysm: Toward Clinical Applications.

See more details in ME news.

Prof. Choi and his student will focus on making prediction and gauage its uncertainty in the growith and remodeling model from observations and various uncertainties such as measurement noise, estimation errors, and biological variability.

Mahdi Jadaliha's paper has been selected as a finalist for the Best Student Paper Award in 2012 Dynamic System and Conrtol Conference (DSCC). See more details here.
Prof. Choi along with his Ph.D. student Mahdi Jadaliha and RET teacher Alexander Robinson presented the outcomes of the CAREER project in the 18th the Coalition for National Science Funding (CNSF) in Capitol Hill and advocated for NSF funding, May 15, 2012.
Yunfei Xu has graduated with a Ph.D. degree under the supervision of Prof. Choi. He is currently a postdoctoral researcher working with Prof. Choi at Michigan State University.
Yunfei Xu's paper has been selected as a finalist for the Best Student Paper Award in 2011 Dynamic System and Control Conference (DSCC).

The selected paper is: Yunfei Xu and Jongeun Choi, "Spatial Prediction with Mobile Sensor Networks using Gaussian Markov Random Fields," in the Proceedings of ASME Dynamic Systems and Control Conference (DSCC), October 31-November 2, 2011, Arlington, VA.

Yunfei Xu has been awarded a Ph.D. Dissertation Completion Fellowship (DCF) from the College of Engineering, Michigan State University for Fall 2011.
Profs. Choi, Radcliffe from College of Engineering, and Profs. Cholewicki and Reeves from College of Osteopathic Medicine, have been awared an NIH center grant, "Systems Science Center for Musculoskeletal CAM Therapies."

See more information at Center for Orthopedic Research.
See also MSU news.
See ME Bulletin article about the NIH research.

Associate Editor 2008-2011 for ASME Dynamics Systems and Control Conference.
Associate Editor 2009-2011 for American Control Conference.
Dr. Choi received a National Science Foundation (NSF) Faculty Early Career Development (CAREER) Award in 2009.

NSF abstract: CAREER: Multi-Agent Systems and Gaussian Processes: Applications in Environmental Sciences.

College of Engieerning news, Mechanical Engineering news.

Co-Chairpersons for 2010 Inverse Problems Symposium and 2009 Inverse Problems Symposium.
See Unstable Seat Balancer Robot.

Research Activities

Distributed learning and cooperative control of multi-agent systems: Currently, we are developing a network of collaboratively learning mobile sensing vehicles for achieving a global goal in an unknown changing environment. Mobile vehicles need to locally learn the uncertain environment collaboratively with neighboring vehicles to achieve a global goal. We are developing environmental adaptive sampling algorithms for mobile sensor networks to predict scalar fields of interest using Gaussian processes and kernel regression techniques. The pilot research was supported by Intramural Research Grants Program in Michigan State University. This project has been funded by an NSF CAREER Award. (See some simulation pictures).

Modeling and control of musculoskeletal systems: The goal is to apply concepts from modern control theory to model and analyze the neuromuschular control systems in order to generate useful information for the treatments. This project has been funded by an NIH center grant, "Systems Science Center for Musculoskeletal CAM Therapies." See more information at Center for Orthopedic Research. See also MSU news. Another topic is to study the evolution and adaptation of biological systems under various environments.

Patient-specific calibration of computer models via a Bayesian method: The goal of this project is to make prediction and gauage its uncertainty in the computer models for a patient (such as a growith and remodeling model) from observations and various uncertainties such as measurement noise, estimation errors, and biological variability. This research has been supported by an NIH R01 grant with a project title: Growth and Remodeling Model of Abdominal Aortic Aneurysm: Toward Clinical Applications. See more information in ME news.

Energy-efficient engine control technology: In this project, we obtain an event-based sampled discrete-time linear system to represent a port-fuelinjection process based on wall-wetting dynamics, and formulate it as a linear parameter varying (LPV) system. The system parameters used in the engine fuel system model are engine speed, temperature, and load. These system parameters can be measured in real-time through physical or virtual sensors. Gain-scheduling controllers for the obtained LPV system have been designed based on the numerically efficient convex optimization or linear matrix inequality (LMI) technique. The simulation results demonstrate the effectiveness of the proposed energy-efficient engine technology.

Parameter estimation and system identification: Parameter reduction techniques have been developed to simplify robust and adaptive controller design. Parameter estimation and system id techniques have been applied to various biomedical and NDE problems such as mechanical behavior of arteries, MRI for skin microcirculation, and wavelet transformed ultrasonic signals.