My research interests are briefly summarized below, followed by more detailed descriptions of specific research projects I have been involved in.
I am interested in both core machine learning (theoretical analysis of computation involved in statistical learning and design of new algorithms and model classes) and applied machine learning, where the primary goal is to provide a practitioner in a particular problem domain with suitable tools for the problem at hand. I believe that theory and applications of learning greatly benefit from each other. In particular, a challenging practical problem can often provide inspiration for a new way of thinking which in turn leads to conceptually new models or algorithms.
My current work at Brown with Michael Black focuses on developing mathematical methods for decoding neural (cortical) code for movement and using it for direct cortical control of motor activity in artificial systems. The primary application for this is in neuro-motor prosthetics for patients whose motor cortex is intact but who have lost control control of motor function due to injury or decease. We would like to bypass the damaged neural pathways by means of computation: decode the "commands" issued by the brain and translate them into commands to, say, a robotic manipulator. Of course, such understanding of the neural code would also have great scientific implications. We are working in collaboration with Donoghue Lab and Cyberkinetics, Inc.
Much of my past work has been on problems in computer vision. I remain very interested in this area, in particular the following topics.