Event Location: Rashid Auditorium – Gates and Hillman Centers 4401
Bio: I am an Assistant Professor in the computer graphics group at Carnegie Mellon University. I received my PhD under Zoran Popovic in the computer graphics group at the University of Washington, and was a postdoc in the Baker Group under Zoran Popovic and David Baker. I was one of the creators of Foldit, the computer game where users contribute to science by folding proteins. I also pursue research in the simulation and animation of very high-dimensional nonlinear phenomena like animal morphology, human motion, and large fluid systems. One thread of my research addresses the complexity of such systems by developing model reduction tools that generate compact representations. A complementary thread seeks to control such systems, which means learning to set inputs to produce desired effects. While I seek theoretical advances, I am also deeply interested in the implications for science and engineering of these techniques, from fluid dynamics to laying down a joint cognitive and biomechanical basis for animal motion.

Abstract: Complex phenomena such as animal morphology, human motion, and large fluid systems challenge even our most sophisticated simulation and control techniques. My overarching research goal has been to develop fundamentally new methods to approach such high-dimensional and nonlinear problems. This talk presents my work solving these problems across a wide range of phenomena, including a new model-reduction approach to fluids that is orders-of-magnitude faster than standard simulation methods and enables interactive high-resolution fluid simulation for the first time. Another example is a continuum approach to crowd dynamics which efficiently reproduces empirical aspects of large crowd behavior that would be difficult or impossible to achieve with traditional agent models. The talk will also cover work on several other phenomena including human animation, animal morphology, and protein folding. Such new algorithmic approaches advance not only our ability to simulate and control complex systems but also our
understanding of the systems themselves.