- James B. Duke Distinguished Professor of Electrical and Computer Engineering
- Professor of Electrical and Computer Engineering
- Professor of Mathematics (Secondary)
- Professor of Computer Science (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.
Tepper, Mariano, and Guillermo Sapiro. “L1 Splines for Robust, Simple, and Fast Smoothing of Grid Data.” Corr, vol. abs/1208.2292, 2012.
Zhou, M., et al. “Dependent hierarchical beta process for image interpolation and denoising.” Journal of Machine Learning Research, vol. 15, Dec. 2011, pp. 883–91.
Yu, G., and G. Sapiro. “Statistical compressed sensing of Gaussian mixture models.” Ieee Transactions on Signal Processing, vol. 59, no. 12, Dec. 2011, pp. 5842–58. Scopus, doi:10.1109/TSP.2011.2168521. Full Text
Bar, L., and G. Sapiro. “Hierarchical invariant sparse modeling for image analysis.” Proceedings International Conference on Image Processing, Icip, Dec. 2011, pp. 2397–400. Scopus, doi:10.1109/ICIP.2011.6116125. Full Text
Prasad, G., et al. “Atlas-based fiber clustering for multi-subject analysis of high angular resolution diffusion imaging tractography.” Proceedings International Symposium on Biomedical Imaging, Nov. 2011, pp. 276–80. Scopus, doi:10.1109/ISBI.2011.5872405. Full Text
Zhan, L., et al. “Differential information content in staggered multiple shell hardi measured by the tensor distribution function.” Proceedings International Symposium on Biomedical Imaging, Nov. 2011, pp. 305–09. Scopus, doi:10.1109/ISBI.2011.5872411. Full Text
Jahanshad, N., et al. “Sex differences in the human connectome: 4-Tesla high angular resolution diffusion imaging (HARDI) tractography in 234 young adult twins.” Proceedings International Symposium on Biomedical Imaging, Nov. 2011, pp. 939–43. Scopus, doi:10.1109/ISBI.2011.5872558. Full Text
Caruyer, E., et al. “Online motion detection in high angular resolution diffusion imaging.” Proceedings International Symposium on Biomedical Imaging, Nov. 2011, pp. 516–19. Scopus, doi:10.1109/ISBI.2011.5872458. Full Text
Jin, Y., et al. “3D elastic registration improves HARDI-derived fiber alignment and automated tract clustering.” Proceedings International Symposium on Biomedical Imaging, Nov. 2011, pp. 822–26. Scopus, doi:10.1109/ISBI.2011.5872531. Full Text
Castrodad, A., et al. “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery.” Ieee Transactions on Geoscience and Remote Sensing, vol. 49, no. 11 PART 1, Nov. 2011, pp. 4263–81. Scopus, doi:10.1109/TGRS.2011.2163822. Full Text