- 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.
Raḿrez, I., et al. “Universal priors for sparse modeling.” Camsap 2009 2009 3rd Ieee International Workshop on Computational Advances in Multi Sensor Adaptive Processing, Dec. 2009, pp. 197–200. Scopus, doi:10.1109/CAMSAP.2009.5413302. Full Text
Zhou, M., et al. “Non-parametric Bayesian dictionary learning for sparse image representations.” Advances in Neural Information Processing Systems 22 Proceedings of the 2009 Conference, Dec. 2009, pp. 2295–303.
Mairal, J., et al. “Non-local sparse models for image restoration.” Proceedings of the Ieee International Conference on Computer Vision, Dec. 2009, pp. 2272–79. Scopus, doi:10.1109/ICCV.2009.5459452. Full Text
Szlam, A., and G. Sapiro. “Discriminative k metrics and the Chan-Vese model for object detection and segmentation.” Proceedings of Spie the International Society for Optical Engineering, vol. 7446, Nov. 2009. Scopus, doi:10.1117/12.825800. Full Text
Facciolo, G., et al. “Exemplar-based interpolation of sparsely sampled images.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5681 LNCS, Nov. 2009, pp. 331–44. Scopus, doi:10.1007/978-3-642-03641-5_25. Full Text
Wirth, B., et al. “Geodesics in shape space via variational time discretization.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5681 LNCS, Nov. 2009, pp. 288–302. Scopus, doi:10.1007/978-3-642-03641-5_22. Full Text
Arias, P., et al. “A variational framework for non-local image inpainting.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5681 LNCS, Nov. 2009, pp. 345–58. Scopus, doi:10.1007/978-3-642-03641-5_26. Full Text
Aganj, Iman, et al. “Measurement of cortical thickness from MRI by minimum line integrals on soft-classified tissue.” Human Brain Mapping, vol. 30, no. 10, Oct. 2009, pp. 3188–99. Epmc, doi:10.1002/hbm.20740. Full Text