- 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.
Fiori, M., et al. “Topology constraints in graphical models.” Advances in Neural Information Processing Systems, vol. 1, Dec. 2012, pp. 791–99.
Elhamifar, E., et al. “Finding exemplars from pairwise dissimilarities via simultaneous sparse recovery.” Advances in Neural Information Processing Systems, vol. 1, Dec. 2012, pp. 19–27.
Bartesaghi, Alberto, et al. “Protein secondary structure determination by constrained single-particle cryo-electron tomography.” Structure (London, England : 1993), vol. 20, no. 12, Dec. 2012, pp. 2003–13. Epmc, doi:10.1016/j.str.2012.10.016. Full Text
Duarte-Carvajalino, J. M., et al. “Adapted statistical compressive sensing: Learning to sense gaussian mixture models.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, Oct. 2012, pp. 3653–56. Scopus, doi:10.1109/ICASSP.2012.6288708. Full Text
Sprechmann, P., et al. “Gaussian mixture models for score-informed instrument separation.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, Oct. 2012, pp. 49–52. Scopus, doi:10.1109/ICASSP.2012.6287814. Full Text
Michaeli, T., et al. “Semi-supervised multi-domain regression with distinct training sets.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, Oct. 2012, pp. 2145–48. Scopus, doi:10.1109/ICASSP.2012.6288336. Full Text
Ramírez, I., and G. Sapiro. “LOw-rank data modeling via the minimum description length principle.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, Oct. 2012, pp. 2165–68. Scopus, doi:10.1109/ICASSP.2012.6288341. Full Text
Sprechmann, P., et al. “Learning efficient structured sparse models.” Proceedings of the 29th International Conference on Machine Learning, Icml 2012, vol. 1, Oct. 2012, pp. 615–22.
Castrodad, A., and G. Sapiro. “Sparse modeling of human actions from motion imagery.” International Journal of Computer Vision, vol. 100, no. 1, Oct. 2012, pp. 1–15. Scopus, doi:10.1007/s11263-012-0534-7. Full Text
Elhamifar, E., et al. “See all by looking at a few: Sparse modeling for finding representative objects.” Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, Oct. 2012, pp. 1600–07. Scopus, doi:10.1109/CVPR.2012.6247852. Full Text