Bio
Hanspeter Pfister is the Academic Dean of Computational Sciences and Engineering and An Wang Professor of Computer Science in the School of Engineering and Applied Sciences. He is an affiliate faculty member of the Harvard Center for Brain Science and served as director of the Institute for Applied Computational Science 2013-17. His research in visual computing lies at the intersection of visualization, computer graphics, and computer vision and spans a wide range of topics, including biomedical visualization, image and video analysis, machine learning, and data science. Pfister has a Ph.D. in Computer Science from Stony Brook University, New York, and an M.Sc. in Electrical Engineering from ETH Zurich, Switzerland. Before joining Harvard, he worked for over a decade at Mitsubishi Electric Research Laboratories, where he was Associate Director and Senior Research Scientist. He was the chief architect of VolumePro, Mitsubishi Electric’s award-winning real-time volume rendering graphics card, for which he received the Mitsubishi Electric President’s Award in 2000. Pfister was elected as an ACM Fellow in 2019 and as an IEEE Fellow in 2023. He is the recipient of the 2010 IEEE Visualization Technical Achievement Award, the 2009 IEEE Meritorious Service Award, and the 2009 Petra T. Shattuck Excellence in Teaching Award. Pfister is a member of the ACM SIGGRAPH Academy, the IEEE Visualization Academy, and a director of the IEEE Visualization and Graphics Technical Committee and the ACM SIGGRAPH Executive Committee.
Abstract
Our modern ability to acquire and generate huge amounts of data can potentially enable rapid progress in science and engineering, but we may not live up that promise if our ability to create data outstrips our ability to make sense of that data. Visual computing tools are essential to gain insights into data by combining computational and statistical analysis with the power of the human perceptual and cognitive system and enabling data exploration through interactive visualizations. In this talk I will present our work on visual computing in Connectomics, a new field in neuroscience that aims to apply biology and computer science to the grand challenge of determining the detailed neural circuitry of the brain. I will give an overview of the computational challenges and describe visual computing approaches that we developed to discover and analyze the brain's neural network. The key to our methods is to keep the user in the loop, either for providing input to our fully-automatic reconstruction methods, for validation and corrections of the reconstructed neural structures, or for visual analytics of the resulting complex networks. The main challenges we face are how to analyze petabytes of image data in an efficient and scalable way, how to automatically reconstruct very large and dense neural circuits from nanoscale-resolution electron micrographs, and how to analyze the brain's neural network once we have discovered it.