The recent technological advances in measuring and manipulating neural systems have the potential to significantly impact many areas, including future generations of machine learning systems and the treatment of neurologic disorders. While exciting, these advances are presenting challenges that require new computational neuroengineering approaches, including models of neural computation as well as algorithms for interacting with these systems (sometimes in real time). Across these different needs, a common emerging theme is the importance of computational approaches at the intersection of dynamical systems and dimensionality reduction. In this seminar I will discuss our recent work in this area in several directions. First, I will discuss our work developing dynamical systems models of neural computation in recurrent networks to perform dimensionality reduction, including sparse coding and manifold learning in vision and memory. Second, I will present out work developing analysis and algorithms for performing estimation and tracking in measured dynamical systems that are governed by low-dimensional structure such as attractors or sparsity constraints.
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. Dr. Rozell is currently a Professor in Electrical and Computer Engineering at the Georgia Institute of Technology, where he previously held the Demetrius T. Paris Junior Professorship.
His research interests live at the intersection of machine learning, signal processing, complex systems, computational neuroscience and biotechnology. Dr. Rozell's lab is affiliated with the Center for Signal and Information Processing, the Neural Engineering Center and the Institute for Robotics and Intelligent Machines. In 2014, Dr. Rozell was one of six international recipients of the Scholar Award in Studying Complex Systems from the James S. McDonnell Foundation 21st Century Science Initiative, as well as receiving a National Science Foundation CAREER Award and a Sigma Xi Young Faculty Research Award. In addition to his research activity, Dr. Rozell was awarded the CETL/BP Junior Faculty Teaching Excellence Award at Georgia Tech in 2013 and the Outstanding Junior ECE Faculty Member Award in 2018.