Leonidas J. Guibas

Leonidas J. Guibas
Leonidas Guibas
Born1949 (age 76–77)
Athens, Greece
Alma materCalifornia Institute of Technology (BS, MS)
Stanford University (PhD)
AwardsACM - AAAI Allen Newell Award (2007)
ICCV Helmholtz Prize (2013)
DoD Vannevar Bush Faculty Fellowship
Scientific career
FieldsComputer science, computational geometry, computer vision, computer graphics
InstitutionsStanford University
Doctoral advisorDonald Knuth
Doctoral studentsJie Gao

Leonidas John Guibas (Λεωνίδας Γκίμπας; born 1949) is a Greek-American computer scientist and the Paul Pigott Professor of Computer Science (and, by courtesy, Electrical Engineering) at Stanford University, where he heads the Geometric Computation Group.[1] His research spans computational geometry, computer graphics, computer vision, machine learning, and robotics, with contributions including foundational data structures, the earth mover's distance for image retrieval, Metropolis light transport, and the PointNet architecture for deep learning on point clouds.

Guibas is a member of the National Academy of Sciences,[2] the National Academy of Engineering,[3] and the American Academy of Arts and Sciences,[4] and a Fellow of the ACM[5] and the IEEE.[6]

Education

Guibas was born and grew up in Athens, Greece.[7] He received his B.S. and M.S. in mathematics from the California Institute of Technology in 1971, and his Ph.D. in computer science from Stanford University in 1976 under the supervision of Donald Knuth.[7][8]

Career

After completing his doctorate, Guibas worked at Xerox PARC, DEC SRC, and MIT before joining the Stanford faculty in 1984.[7] He has also served as acting director of the Stanford Artificial Intelligence Laboratory.[9] He was program chair for the ACM Symposium on Computational Geometry in 1996.[10]

Research

Algorithms and data structures

Guibas's early work contributed several widely used data structures and algorithms in computational geometry. With Robert Sedgewick, he introduced red–black trees, a form of self-balancing binary search tree.[11] Other contributions from this period include finger trees, fractional cascading, an optimal data structure for point location, the quad-edge data structure for representing planar subdivisions, and the Guibas–Stolfi algorithm for Delaunay triangulation. He also developed kinetic data structures for tracking objects in motion.

Computer graphics and vision

In computer graphics, Guibas co-authored work on Metropolis light transport, which enabled practical global illumination algorithms for photorealistic rendering.[7] In computer vision, he co-developed the earth mover's distance (EMD) with Yossi Rubner and Carlo Tomasi, a metric for comparing distributions that has been widely adopted in image retrieval and related tasks.[12] The EMD paper received the ICCV Helmholtz Prize in 2013, recognizing work with fundamental impact on computer vision.[13]

Deep learning on point clouds and 3D geometry

More recently, Guibas's group has been a leader in applying deep learning to irregular geometric data such as point clouds and voxels. With Charles R. Qi, Hao Su, and others, he co-developed PointNet (2017), a neural network architecture that directly consumes raw point clouds for tasks including 3D object classification, part segmentation, and scene semantic parsing, without requiring conversion to voxel grids or image projections.[14] The follow-up PointNet++ introduced hierarchical feature learning that captures local geometric structure at multiple scales.[15] These architectures have been applied to problems in autonomous driving, robotics, and computational fluid dynamics.[16]

His group has also developed methods for functional maps between shapes,[17] 3D object detection in point clouds, shape generation, and deformation-aware 3D model analysis.

Awards and honors

Guibas has an Erdős number of 2, through collaborations with Boris Aronov, Andrew Odlyzko, János Pach, Richard M. Pollack, Endre Szemerédi, and Frances Yao.[19]

References

  1. ^ "Guibas Lab". geometry.stanford.edu. Retrieved 2025-04-30.
  2. ^ 2022 National Academy of Sciences Elects Members and International Members, retrieved 2022-05-04.
  3. ^ National Academy of Engineering Elects 84 Members and 22 Foreign Members, February 8, 2017, retrieved 2017-05-02.
  4. ^ 2018 Fellows and International Honorary Members, retrieved 2018-05-17.
  5. ^ ACM Fellow award citation Archived 2007-12-14 at the Wayback Machine.
  6. ^ 2012 Newly Elevated Fellows, IEEE, accessed 2011-12-10.
  7. ^ a b c d "Leonidas J. Guibas". National Academy of Sciences. Retrieved 2025-02-06.
  8. ^ "Leonidas J. Guibas biography". geometry.stanford.edu. Retrieved 17 May 2022.
  9. ^ a b "Leonidas Guibas". Stanford HAI. Retrieved 2025-02-06.
  10. ^ Program Committees from the Symposium on Computational Geometry Archived 2023-11-19 at the Wayback Machine, Computational Geometry Steering Committee.
  11. ^ Guibas, Leo J.; Sedgewick, Robert (1978). "A dichromatic framework for balanced trees". 19th Annual Symposium on Foundations of Computer Science: 8–21. doi:10.1109/SFCS.1978.3.
  12. ^ Rubner, Yossi; Tomasi, Carlo; Guibas, Leonidas J. (2000). "The Earth Mover's Distance as a Metric for Image Retrieval". International Journal of Computer Vision. 40: 99–121. doi:10.1023/A:1026543900054.
  13. ^ "ICCV Helmholtz Prize". IEEE Computer Society TC-PAMI. Retrieved 2025-02-06.
  14. ^ Qi, Charles R.; Su, Hao; Mo, Kaichun; Guibas, Leonidas J. (2017). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 652–660. doi:10.1109/CVPR.2017.16.
  15. ^ Qi, Charles R.; Yi, Li; Su, Hao; Guibas, Leonidas J. (2017). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Advances in Neural Information Processing Systems (NeurIPS).
  16. ^ Kashefi, Ali; Rempe, Davis; Guibas, Leonidas J. (2021). "A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries". Physics of Fluids. 33 (2): 027104. arXiv:2010.09469. doi:10.1063/5.0033376.
  17. ^ Ovsjanikov, Maks; Ben-Chen, Mirela; Solomon, Justin; Butscher, Adrian; Guibas, Leonidas (2012). "Functional Maps: A Flexible Representation of Maps Between Shapes". ACM Transactions on Graphics. 31 (4). doi:10.1145/2185520.2185526.
  18. ^ ACM/AAAI Allen Newell Award Recognizes Leonidas Guibas for Algorithms Advancing CS Fields Archived 2008-12-12 at the Wayback Machine, ACM, 2008.
  19. ^ Erdős number project.
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