Closing the Loop Between Mind and Machine: Building Algorithms to Interface With Brains at Multiple Scales
Featuring faculty and student presentations on data science research across the University
Oral and poster presentations on
· Theoretical foundations of data science
· Data science methodology
· Data science applications in any research domain
· Social impact of data science research
Industry engagement workshop
· Adrian Fortino, Partner, Mercury Fund
· Mike Psarouthakis, Director, U-M Venture Center
· Kevyn Collins-Thompson, Associate Professor, U-M School of Information
· Mike Cafarella, Associate Professor, U-M Computer Science and Engineering
Presentations on data science infrastructure and consulting services
· Brock Palen, Director of Advanced Research Computing – Technology Services
· Kerby Shedden, Director of Consulting for Statistics, Computing and Analytics Research
For more and to register: midas.umich.edu/forum
New technologies are rapidly developing for interfacing with the brain across multiple scales including cells, circuits, networks and systems. While there has been much discussion about the emerging "big data" problems that will arise from high-resolution measurement technologies, there are a number of other new data science challenges also emerging. In particular, now more than ever, we have the ability and desire to build closed-loop systems that are a combination of biology and technology working together in real time for scientific discovery and clinical therapies. Innovations in interfacing technology require parallel advances in algorithmic technology to determine what to do with these new tools to maximize their effectiveness. These system require data science approaches that operate online, are designed for closed-loop processing in real-time, are robust to the reality of "small data" in many applications, and are fully informed about both the biology and modern neurotechnology being used at the interface. In this talk we will survey recent examples of data science problems we are working on as we build closed-loop interfacing systems for the brain in health and disease. These problems will span scales from single-cell electrophysiology up to novel brain-machine interfaces for controlling complex 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. Dr. Rozell is currently an Associate 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 is currently the Associate Director of the Neural Engineering Center at Georgia Tech, where his lab is also affiliated with the Center for Signal and Information Processing 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.