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Environmental Adaptive Sampling using Mobile Sensor Networks

Environmental Adaptive Sampling using Mobile Sensor Networks

Dr. Jongeun Choi
Assistant professor
Department of Mechanical Engineering and
Department of Electrical and Computer Engineering
Michigan State University

This seminar presents recent activities in my research group on environmental adaptive sampling using mobile sensor networks. Here a measurable spatio-temporal field represents the collection of scalar quantities of interest (such as chemical concentration or biomass of algal blooms) transported via physical processes. To deal with complexity and practicality, phenomenological and statistical modeling techniques are used to make inferences from observations collected by mobile sensors. However, the statistical models need to be carefully tailored such that they can be usable for mobile sensor networks with limited resources. A variety of parametric and nonparametric models, ranging from radial basis function networks to Gaussian processes and Gaussian Markov random fields (GMRFs), along with their prediction and adaptive sampling algorithms will be discussed. Recent effort on fully Bayesian approaches in order to take into account all uncertainties, while minimizing the computational complexity will be also discussed. This work has been supported, in part, by Intramural Research Grants Program (IRGP) from Michigan State University and the National Science Foundation through CAREER Award.

Speaker bio:

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, the Republic of Korea in 1998. He is currently an Assistant Professor with the Departments of Mechanical Engineering and Electrical and Computer Engineering at the Michigan State University.  His research interests include adaptive, distributed and robust control and statistical learning algorithms, 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. He is a recipient of an NSF CAREER Award in 2009. Dr. Choi is a co-PI of the NIH center: “Systems Science Center for Musculoskeletal CAM Therapies.”