- 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.
Sapiro, Gisèle. “Autonomy Revisited: The Question of Mediations and its Methodological Implications.” Paragraph, vol. 35, no. 1, Edinburgh University Press, Mar. 2012, pp. 30–48. Crossref, doi:10.3366/para.2012.0040. Full Text
Xing, Z., et al. “Dictionary learning for noisy and incomplete hyperspectral images.” Siam Journal on Imaging Sciences, vol. 5, no. 1, Feb. 2012, pp. 33–56. Scopus, doi:10.1137/110837486. Full Text
Duarte-Carvajalino, Julio M., et al. “Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship.” Neuroimage, vol. 59, no. 4, Feb. 2012, pp. 3784–804. Epmc, doi:10.1016/j.neuroimage.2011.10.096. Full Text
Lenglet, Christophe, et al. “Comprehensive in vivo mapping of the human basal ganglia and thalamic connectome in individuals using 7T MRI.” Plos One, vol. 7, no. 1, Jan. 2012, p. e29153. Epmc, doi:10.1371/journal.pone.0029153. Full Text
Tran, Erin E. H., et al. “Structural mechanism of trimeric HIV-1 envelope glycoprotein activation.” Plos Pathog, vol. 8, no. 7, 2012, p. e1002797. Pubmed, doi:10.1371/journal.ppat.1002797. Full Text
Duchin, Yuval, et al. “Feasibility of using ultra-high field (7 T) MRI for clinical surgical targeting.” Plos One, vol. 7, no. 5, Jan. 2012, p. e37328. Epmc, doi:10.1371/journal.pone.0037328. Full Text
Cetingul, H. Ertan, et al. “Simultaneous ODF estimation and tractography in HARDI.” Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference, vol. 2012, Jan. 2012, pp. 86–89. Epmc, doi:10.1109/embc.2012.6345877. Full Text
Tong, M., et al. “A VARIATIONAL MODEL FOR DENOISING HIGH ANGULAR RESOLUTION DIFFUSION IMAGING.” Proceedings. Ieee International Symposium on Biomedical Imaging, Jan. 2012, pp. 530–33. Epmc, doi:10.1109/isbi.2012.6235602. Full Text
Tepper, Mariano, and Guillermo Sapiro. “L1 Splines for Robust, Simple, and Fast Smoothing of Grid Data.” Corr, vol. abs/1208.2292, 2012.
Michaeli, T., et al. “Semi-supervised single- And multi-domain regression with multi-domain training.” Information and Inference, vol. 1, no. 1, Jan. 2012, pp. 68–97. Scopus, doi:10.1093/imaiai/ias003. Full Text