Guillermo Sapiro

Guillermo Sapiro
  • James B. Duke Professor of Electrical and Computer Engineering
  • Professor of Electrical and Computer Engineering
  • Professor of Mathematics (Secondary)
  • Faculty Network Member of the Duke Institute for Brain Sciences
Internal office address: Campus Box 90984, 140 Science Drive - 325 Gross Hall, Durham, NC 27708
Office Hours: 

By appointment. Contact via e-mail.

Guillermo Sapiro received his B.Sc. (summa cum laude), M.Sc., and Ph.D. from the Department of Electrical Engineering at the Technion, Israel Institute of Technology, in 1989, 1991, and 1993 respectively. After post-doctoral research at MIT, Dr. Sapiro became Member of Technical Staff at the research facilities of HP Labs in Palo Alto, California. He was with the Department of Electrical and Computer Engineering at the University of Minnesota, where he held the position of Distinguished McKnight University Professor and Vincentine Hermes-Luh Chair in Electrical and Computer Engineering. Currently he is the Edmund T. Pratt, Jr. School Professor with Duke University.

G. Sapiro works on theory and applications in computer vision, computer graphics, medical imaging, image analysis, and machine learning. He has authored and co-authored over 300 papers in these areas and has written a book published by Cambridge University Press, January 2001.

G. Sapiro was awarded the Gutwirth Scholarship for Special Excellence in Graduate Studies in 1991,  the Ollendorff Fellowship for Excellence in Vision and Image Understanding Work in 1992,  the Rothschild Fellowship for Post-Doctoral Studies in 1993, the Office of Naval Research Young Investigator Award in 1998,  the Presidential Early Career Awards for Scientist and Engineers (PECASE) in 1998, the National Science Foundation Career Award in 1999, and the National Security Science and Engineering Faculty Fellowship in 2010. He received the test of time award at ICCV 2011. He was elected to the American Academy of Arts and Sciences on 2018.

G. Sapiro is a Fellow of IEEE and SIAM.

G. Sapiro was the founding Editor-in-Chief of the SIAM Journal on Imaging Sciences.

Education & Training
  • D.Sc., Israel Institute of Technology 1993

Sprechmann, P., et al. “Learnable low rank sparse models for speech denoising.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing  Proceedings, Oct. 2013, pp. 136–40. Scopus, doi:10.1109/ICASSP.2013.6637624. Full Text

Cetingul, H. E., et al. “Importance sampling spherical harmonics to improve probabilistic tractography.” Proceedings  2013 3rd International Workshop on Pattern Recognition in Neuroimaging, Prni 2013, Oct. 2013, pp. 46–49. Scopus, doi:10.1109/PRNI.2013.21. Full Text

Sotiropoulos, Stamatios N., et al. “Advances in diffusion MRI acquisition and processing in the Human Connectome Project..” Neuroimage, vol. 80, Oct. 2013, pp. 125–43. Epmc, doi:10.1016/j.neuroimage.2013.05.057. Full Text

Uğurbil, Kamil, et al. “Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project..” Neuroimage, vol. 80, Oct. 2013, pp. 80–104. Epmc, doi:10.1016/j.neuroimage.2013.05.012. Full Text

Chen, Bo, et al. “Deep learning with hierarchical convolutional factor analysis..” Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, Aug. 2013, pp. 1887–901. Epmc, doi:10.1109/TPAMI.2013.19. Full Text

Caruyer, Emmanuel, et al. “Design of multishell sampling schemes with uniform coverage in diffusion MRI..” Magnetic Resonance in Medicine, vol. 69, no. 6, June 2013, pp. 1534–40. Epmc, doi:10.1002/mrm.24736. Full Text

Su, Shu, et al. “Geometric computation of human gyrification indexes from magnetic resonance images..” Human Brain Mapping, vol. 34, no. 5, May 2013, pp. 1230–44. Epmc, doi:10.1002/hbm.21510. Full Text

Llull, Patrick, et al. “Coded aperture compressive temporal imaging..” Optics Express, vol. 21, no. 9, May 2013, pp. 10526–45. Epmc, doi:10.1364/oe.21.010526. Full Text Open Access Copy

Harris, Audray K., et al. “Structure and accessibility of HA trimers on intact 2009 H1N1 pandemic influenza virus to stem region-specific neutralizing antibodies..” Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 12, Mar. 2013, pp. 4592–97. Epmc, doi:10.1073/pnas.1214913110. Full Text

Kuybeda, Oleg, et al. “A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography..” Journal of Structural Biology, vol. 181, no. 2, Feb. 2013, pp. 116–27. Epmc, doi:10.1016/j.jsb.2012.10.010. Full Text

Pages

Breen, D., et al. “Level set and PDE methods for computer graphics.” Acm Siggraph 2004 Course Notes, Siggraph 2004, 2004. Scopus, doi:10.1145/1103900.1103928. Full Text

Sole, A., et al. “Morse description and geometric encoding of digital elevation maps.” Free Boundary Problems: Theory and Applications, vol. 147, 2004, pp. 303–12.

Bertalmió, M., et al. “Variational problems and PDEs on implicit surfaces.” Proceedings  Ieee Workshop on Variational and Level Set Methods in Computer Vision, Vlsm 2001, 2001, pp. 186–93. Scopus, doi:10.1109/VLSM.2001.938899. Full Text

Sapiro, G. “Harmonic map flows and image processing.” Foundations of Computational Mathematics, vol. 284, 2001, pp. 299–322.

Betelu, S., et al. “Noise-resistant affine skeletons of planar curves.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1842, 2000, pp. 742–54.

Giblin, P. J., and G. Sapiro. “Affine versions of the symmetry set.” Real and Complex Singularities, vol. 412, 2000, pp. 173–87.

Bertalmio, M., et al. “Morphing active contours.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1682, 1999, pp. 46–53.

Bertalmio, M., et al. “Region tracking on surfaces deforming via level-sets methods.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1682, 1999, pp. 330–38.

Black, M. J., and G. Sapiro. “Edges as outliers: Anisotropic smoothing using local image statistics.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1682, 1999, pp. 259–70.

Teo, P. C., et al. “Anisotropic smoothing of posterior probabilities.” Dynamical Systems, Control, Coding, Computer Vision, vol. 25, 1999, pp. 419–32.

Pages