Leonidas Guibas is a Computer Scientist recognized for his work in algorithms, data structures, and network architectures for geometric data analysis or synthesis, geometric computing and learning — and applications of these methods to 3D computer vision, computer graphics, and robotics. Guibas was born and grew up in Athens, Greece. He obtained BS and MS degrees in Mathematics from the California Institute of Technology in 1971, and a PhD from Stanford in Computer Science in 1976. His main subsequent employers were Xerox PARC, DEC/SRC, MIT, and Stanford, where he has been since 1984. He currently the Paul Pigott Professor of Engineering at Stanford University in the Computer Science (and, by courtesy, Electrical Engineering) departments. Guibas is a member US National Academies of Sciences and of Engineering, as well as of the American Academy of Arts and Sciences, and has been elected an ACM Fellow and an IEEE Fellow.

Research Interests

Leonidas Guibas has a long record of theoretical and experimental work in computer science and applied mathematics on disciplines that relate to geometric computation. He has been a founder of the field of Computational Geometry with many accomplishments that define the field today, including algorithms for the computation of Voronoi and Delaunay diagrams, the quad-edge data structure, and methods such as topological sweeps, fractional cascading, snap-rounding, or kinetic data structures. He has pursued the use of geometric ideas across many other areas as well. His work on the Earth Mover's Distance (EMD) for feature distributions has found wide applicability in many computer vision tasks. His path tracing and Metropolis light transport papers in computer graphics made possible practical global illumination algorithms. He is currently a world leader in the area of geometry and topology processing, involving the analysis of massive geometric data sets coming from sensors or simulations. Recent accomplishments include algorithms for shape and scene segmentation, the estimation of maps and correspondences between geometric data sets, and the detection of symmetries and other regular patterns. He has pioneered deep learning architectures for processing irregular geometric data, such as point clouds, and developed techniques for joint analysis and learning across multiple related data sets by exploiting networks of maps and correspondences between them ? benefiting from the "wisdom of the collection."

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Primary Section

Section 34: Computer and Information Sciences

Secondary Section

Section 32: Applied Mathematical Sciences