Leonidas Guibas: 2014 Networks of Shapes, Images, and Programs: The Power of Joint Data Analysis Workshop


Wednesday, April 16, 2014
Location: Fisher Conference Center, Arrillaga Alumni Center

"Workshop Overview: The Network Viewpoint"
9:00am - 9:15am


The workshop presents a set of techniques for making relationships or correspondences between data sets first-class citizens -- so that the relationships themselves become explicit, algebraic, storable and searchable objects. Networks of such relations can interconnect data sets into societies where network analysis methods can enable the "wisdom of the collection" to be exploited in performing operations on individual data sets robustly, or in further assessing relationships between them. In this way, by creating societies of data sets and their associations in a globally consistent way, we enable a certain joint understanding that provides the powers of abstraction, analogy, compression, error correction, and summarization.

Example applications to be discussed will include image and shape segmentation, variability analysis in shape collections, shape affordances, and the evaluation of programming assignments in MOOCs.


Leonidas Guibas obtained his Ph.D. from Stanford under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, DEC/SRC, MIT, and Stanford. He is currently the Paul Pigott Professor of Computer Science (and by courtesy, Electrical Engineering) at Stanford University. He heads the Geometric Computation group and is part of the Graphics Laboratory, the AI Laboratory, the Bio-X Program, and the Institute for Computational and Mathematical Engineering. Professor Guibas' interests span geometric data analysis, computational geometry, geometric modeling, computer graphics, computer vision, robotics, ad hoc communication and sensor networks, and discrete algorithms. Some well-known past accomplishments include the analysis of double hashing, red-black trees, the quad-edge data structure, Voronoi-Delaunay algorithms, the Earth Mover's distance, Kinetic Data Structures (KDS), Metropolis light transport, and the Heat-Kernel Signature. Professor Guibas is an ACM Fellow, an IEEE Fellow and winner of the ACM Allen Newell award.