On the Move: Dynamical Systems for modeling, measurement and inference in sparse signal models

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.