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
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,
and biomedical problems. He is an Associate Editor for Journal of
Dynamic Systems, Measurement and Control. 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.
a member of ASME.
- A new
book proposal coauthored by Yunfei Xu, Jongeun Choi,
Sarat Dass, and Tapabrata Maiti, entitled “Bayesian Prediction
Sampling Algorithms for Mobile Sensor Networks,” is accepted for
SpringerBriefs in Control,
Automation and Robotics, Springer, 2013.
and Choi are co-organizing a Special Issue on
Stochastic Models, Control, and Algorithms in Robotics in Journal
of Dynamic Systems, Measurement and Control.
- Mahdi Jadaliha recieved his Ph.D. degree, Fall 2013.
- Ahsan Ijaz
recieved his M.S. degree, Fall 2013.
- Andrey Maslennikov recieved his M.S. degree, Fall 2013.
- A new
Control for Engineering Applications,” by Andrew White,
Guoming Zhu and Jongeun Choi, SpringerBriefs in
Control, Automation and Robotics, Springer, ISBN 978-1-4471-5039-8,
- Prof. Choi serves as an Associate Editor for Journal
of Dynamic Systems, Measurement and Control from January 1, 2013.
- 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
changing environment. Mobile vehicles need to locally learn the
environment collaboratively with neighboring vehicles to achieve a
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
- 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 gauge its
uncertainty in the computer models for a patient (such as a growth 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
- Energy-efficient engine control
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
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.
I work with highly motivated Ph.D.
students with strong background in control theory, system identification, convex
optimization, and machine learning.
- ME/ECE 851
Linear Systems and Control:
- ME/ECE 859
Nonlinear Systems and
- ME 451 Control
- ME 451 Control
design project, a faculty advisor for a capstone design team (a
group of four senior undergraduate students)
Outreach and International Exchange Student Programs
[Curriculum Vitae] [Publications] [Collaborators]