- 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.
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
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
Qiu, Q., and G. Sapiro. “Learning transformations for classification forests.” 2nd International Conference on Learning Representations, Iclr 2014 Conference Track Proceedings, Jan. 2014.
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
Yuan, X., et al. “Adaptive temporal compressive sensing for video.” 2013 Ieee International Conference on Image Processing, Icip 2013 Proceedings, Dec. 2013, pp. 14–18. Scopus, doi:10.1109/ICIP.2013.6738004. Full Text Open Access Copy
Fiori, M., et al. “Polyps flagging in virtual colonoscopy.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8259 LNCS, no. PART 2, Dec. 2013, pp. 181–89. Scopus, doi:10.1007/978-3-642-41827-3_23. Full Text
Walczak, N., et al. “Locating occupants in preschool classrooms using a multiple RGB-D sensor system.” Ieee International Conference on Intelligent Robots and Systems, Dec. 2013, pp. 2166–72. Scopus, doi:10.1109/IROS.2013.6696659. Full Text
Tepper, M., and G. Sapiro. “Ants crawling to discover the community structure in networks.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8259 LNCS, no. PART 2, Dec. 2013, pp. 552–59. Scopus, doi:10.1007/978-3-642-41827-3_69. Full Text
Yang, J., et al. “Gaussian mixture model for video compressive sensing.” 2013 Ieee International Conference on Image Processing, Icip 2013 Proceedings, Dec. 2013, pp. 19–23. Scopus, doi:10.1109/ICIP.2013.6738005. 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-+.
Bai, Xue, et al. “Distancecut: Interactive segmentation and matting of images and videos.” 2007 Ieee International Conference on Image Processing, Vols 1 7, 2007, pp. 813–16.
Mairal, Julien, et al. “Multiscale sparse image representation with learned dictionaries.” 2007 Ieee International Conference on Image Processing, Vols 1 7, 2007, pp. 1233-+.
Sapiro, G., and G. IEEE. “Inpainting the colors.” 2005 International Conference on Image Processing (Icip), Vols 1 5, 2005, pp. 1265–68.