- 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.
Duarte-Carvajalino, Julio M., et al. “Estimation of the CSA-ODF using Bayesian compressed sensing of multi-shell HARDI.” Magnetic Resonance in Medicine, vol. 72, no. 5, Nov. 2014, pp. 1471–85. Epmc, doi:10.1002/mrm.25046. Full Text
Kim, Jinyoung, et al. “Semiautomatic segmentation of brain subcortical structures from high-field MRI.” Ieee Journal of Biomedical and Health Informatics, vol. 18, no. 5, Sept. 2014, pp. 1678–95. Epmc, doi:10.1109/jbhi.2013.2292858. Full Text
Prasad, Gautam, et al. “Automatic clustering and population analysis of white matter tracts using maximum density paths.” Neuroimage, vol. 97, Aug. 2014, pp. 284–95. Epmc, doi:10.1016/j.neuroimage.2014.04.033. Full Text
Harrison, Benjamin D., et al. “A tetraploid intermediate precedes aneuploid formation in yeasts exposed to fluconazole.” Plos Biology, vol. 12, no. 3, Mar. 2014, p. e1001815. Epmc, doi:10.1371/journal.pbio.1001815. Full Text
Sprechmann, P., et al. “Supervised non-euclidean sparse NMF via bilevel optimization with applications to speech enhancement.” 2014 4th Joint Workshop on Hands Free Speech Communication and Microphone Arrays, Hscma 2014, Jan. 2014, pp. 11–15. Scopus, doi:10.1109/HSCMA.2014.6843241. Full Text
Carpenter, K., et al. “Questionnaire simplification for fast risk analysis of children's mental health.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, Jan. 2014, pp. 6009–13. Scopus, doi:10.1109/ICASSP.2014.6854757. Full Text
Tepper, M., and G. Sapiro. “All for one, one for all: Consensus community detection in networks.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, Jan. 2014, pp. 1075–79. Scopus, doi:10.1109/ICASSP.2014.6853762. Full Text
Qiu, Q., and G. Sapiro. “Learning transformations for classification forests.” 2nd International Conference on Learning Representations, Iclr 2014 Conference Track Proceedings, Jan. 2014.
Fiori, M., et al. “A complete system for candidate polyps detection in virtual colonoscopy.” International Journal of Pattern Recognition and Artificial Intelligence, vol. 28, no. 7, Jan. 2014. Scopus, doi:10.1142/S0218001414600143. Full Text
Hashemi, Jordan, et al. “Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants.” Autism Res Treat, vol. 2014, 2014, p. 935686. Pubmed, doi:10.1155/2014/935686. Full Text Open Access Copy
Sprechmann, Pablo, et al. “Collaborative sources identification in mixed signals via hierarchical sparse modeling.” Icassp, IEEE, 2011, pp. 5816–19.
Yu, Guoshen, and Guillermo Sapiro. “Statistical compressive sensing of Gaussian mixture models.” Icassp, IEEE, 2011, pp. 3728–31.
Shema-Didi, Lilach, et al. “The Beneficial Effects of One Year Pomegranate Juice Consumption on Traditional and Nontraditional Risk Factors for Cardiovascular Diseases.” Free Radical Biology and Medicine, vol. 49, Elsevier BV, 2010, pp. S198–S198. Crossref, doi:10.1016/j.freeradbiomed.2010.10.572. Full Text
Sprechmann, Pablo, et al. “Collaborative hierarchical sparse modeling.” Ciss, IEEE, 2010, pp. 1–6.
Rother, Diego, and Guillermo Sapiro. “Seeing 3D objects in a single 2D image.” 2009 Ieee 12th International Conference on Computer Vision, IEEE, 2009. Crossref, doi:10.1109/iccv.2009.5459405. Full Text
Mairal, Julien, et al. “Supervised Dictionary Learning.” Nips, edited by Daphne Koller et al., Curran Associates, Inc., 2008, pp. 1033–40.
Bar, Leath, et al. “GENERALIZED NEWTON METHODS FOR ENERGY FORMULATIONS IN IMAGE PROCESSING.” 2008 Ieee International Conference on Image Processing, Proceedings, 2008, pp. 813–16.
Bartesaghi, Alberto, et al. “A framework for classification and averaging of 3D tomographic volumes.” Biophysical Journal, BIOPHYSICAL SOCIETY, 2007, pp. 509A-509A.
Patwardhan, Kedar A., et al. “A graph-based foreground representation and its application in example based people matching in video.” 2007 Ieee International Conference on Image Processing, Vols 1 7, 2007, pp. 2289-+.