On the Move: Dynamical Systems for modeling, measurement and inference in sparse signal models
Thursday, February 06, 2014|
4:00pm - 5:00pm
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About the Event
Data acquisition and processing systems often involve three major components: a low-dimensional model for the data of interest, a measurement process, and an inference algorithm. In particular, in many modern signal processing systems these components have been tailored to exploit the fact that the data is sparse in some appropriate basis to achieve state-of-the-art results. In this seminar I will give an overview of some of our recent results where modeling, measurement and inference for sparse signals intersects with the field of dynamical systems. Specifically, I will address four main questions. Can the principles of Kalman filtering be applied for effective dynamic filtering to track time-varying sparse signals? Can signal recovery be performed when the measurement system itself is a dynamical system? Can dynamical systems be used as platforms to build novel ultra-efficient high performance computing devices for sparse signal inference? Can arbitrary measurement systems preserve information when the signal of interest is the attractor of a dynamical systems?
Christopher J. Rozell received a B.S.E. degree in Computer Engineering and a B.F.A. degree in Music (Performing Arts Technology) in 2000 from the University of Michigan. He attended graduate school at Rice University, receiving the M.S. and Ph.D. degrees in Electrical Engineering in 2002 and 2007, respectively. Following graduate school he joined the Redwood Center for Theoretical Neuroscience at the University of California, Berkeley as a postdoctoral scholar. In 2008 Dr. Rozell joined the faculty at the Georgia Institute of Technology where he is currently an Assistant Professor and holds the Demetrius T. Paris Junior Professorship in Electrical and Computer Engineering. His research interests live at the intersection of signal processing, machine learning and computational neuroscience. Specifically, his lab uses tools from modern data analysis to improve our understanding of neural systems and insight from modern neuroscience to build more effective computational systems, with applications ranging from biotechnology to remote sensing. His research lab is affiliated with both the Center for Signal and Information Processing and the Laboratory for Neuroengineering. Dr. Rozell received the National Science Foundation CAREER Award in 2014, and previously was the recipient of the Texas Instruments Distinguished Graduate Fellowship at Rice University. In addition to his research activity, Dr. Rozell was awarded the CETL/BP Junior Faculty Teaching Excellence Award at Georgia Tech in 2013.
Contact: Ann Pace
Sponsor(s): University of Michigan, Department of Electrical Engineering & Computer Science
Open to: Public