- 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
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.
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
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
Zhan, Liang, et al. “Magnetic resonance field strength effects on diffusion measures and brain connectivity networks..” Brain Connectivity, vol. 3, no. 1, Jan. 2013, pp. 72–86. Epmc, doi:10.1089/brain.2012.0114. Full Text
Duarte-Carvajalino, J. M., et al. “Task-driven adaptive statistical compressive sensing of gaussian mixture models.” Ieee Transactions on Signal Processing, vol. 61, no. 3, Jan. 2013, pp. 585–600. Scopus, doi:10.1109/TSP.2012.2225054. Full Text Open Access Copy
Qiu, Qiang, et al. “Domain-invariant Face Recognition using Learned Low-rank Transformation..” Corr, vol. abs/1308.0275, 2013.
Fiori, M., et al. “Robust multimodal graph matching: Sparse coding meets graph matching.” Advances in Neural Information Processing Systems, Jan. 2013.
Llull, P., et al. “Compressive sensing for video using a passive coding element.” Optics Infobase Conference Papers, Jan. 2013.
Sprechmann, P., et al. “Efficient supervised sparse analysis and synthesis operators.” Advances in Neural Information Processing Systems, Jan. 2013.
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.
Chung, D. H., and G. Sapiro. “A windows-based user friendly system for image analysis with partial differential equations.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1682, 1999, pp. 453–58.
Black, M. J., et al. “Robust anisotropic diffusion: Connections between robust statistics, line processing, and anisotropic diffusion.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1252, 1997, pp. 323–26. Scopus, doi:10.1007/3-540-63167-4_27. Full Text
Black, Michael J., et al. “Robust Anisotropic Diffusion: Connections Between Robust Statistics, Line Processing, and Anisotropic Diffusion..” Scale Space, edited by Bart M ter Haar Romeny et al., vol. 1252, Springer, 1997, pp. 323–26.
Caselles, V., et al. “Three dimensional object modeling via minimal surfaces.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1064, 1996, pp. 97–106.
Sapiro, Guillermo, and Vicent Caselles. “Simultaneous contrast improvement and denoising via diffusion-related equations.” Vision Geometry Iv, SPIE, 1995. Crossref, doi:10.1117/12.216427. Full Text
Sapiro, Guillermo, et al. “Object detection and measurements in medical images via geodesic deformable contours.” Vision Geometry Iv, SPIE, 1995. Crossref, doi:10.1117/12.216429. Full Text