- 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.
Precursors to the development of anxiety disorders in young children with autism spectrum disorder awarded by Department of Defense (Collaborator). 2014 to 2017
AF: Small: Learning to Parsimoniously Model and Compute with Big Data awarded by National Science Foundation (Principal Investigator). 2013 to 2017
Visitors to the Information Initiative at Duke awarded by Office of Naval Research (Principal Investigator). 2015 to 2016
Structured and Collaborative Geometric Signal Models for Big Data Analysis: Theory and Applications in Image, Video and awarded by Army Research Office (Principal Investigator). 2012 to 2016
Informed Signal Models: Theory and Applications in Image Sciences awarded by Office of Naval Research (Principal Investigator). 2012 to 2016
MRI: Development of an Instrument that Monitors Behaviors with OCD and Schizophrenia awarded by University of Minnesota (Principal Investigator). 2013 to 2016
Information Acquisition, Analysis, and Integration awarded by University of Minnesota (Principal Investigator). 2012 to 2016
HARDI Mapping of Disease Effects on the Brain awarded by University of California, Los Angeles (Principal Investigator). 2012 to 2014
Learning sparse representations for restoration and classification: awarded by National Science Foundation (Principal Investigator). 2012 to 2013
Chiew, Kimberly S., et al. “Motivational valence alters memory formation without altering exploration of a real-life spatial environment.” Plos One, vol. 13, no. 3, 2018, p. e0193506. Pubmed, doi:10.1371/journal.pone.0193506. Full Text
Lezama, J., et al. “Not afraid of the dark: NIR-VIS face recognition via cross-spectral hallucination and low-rank embedding.” Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January, Nov. 2017, pp. 6807–16. Scopus, doi:10.1109/CVPR.2017.720. Full Text
Ye, Q., et al. “Self-learning scene-specific pedestrian detectors using a progressive latent model.” Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January, Nov. 2017, pp. 2057–66. Scopus, doi:10.1109/CVPR.2017.222. Full Text
Simhal, Anish K., et al. “Probabilistic fluorescence-based synapse detection.” Plos Computational Biology, vol. 13, no. 4, Apr. 2017, p. e1005493. Epmc, doi:10.1371/journal.pcbi.1005493. Full Text
Campbell, Kathleen, et al. “Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism.” J Pediatr, vol. 183, Apr. 2017, pp. 133-139.e1. Pubmed, doi:10.1016/j.jpeds.2017.01.021. Full Text
Gunalan, Kabilar, et al. “Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example.” Plos One, vol. 12, no. 4, Jan. 2017, p. e0176132. Epmc, doi:10.1371/journal.pone.0176132. Full Text
Lezama, José, et al. “Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes.” Biomed Opt Express, vol. 7, no. 12, Dec. 2016, pp. 4827–46. Pubmed, doi:10.1364/BOE.7.004827. Full Text
Kim, J., et al. “Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9350, 2015, pp. 587–94. Scopus, doi:10.1007/978-3-319-24571-3_70. Full Text
Huang, J., et al. “Discriminative robust transformation learning.” Advances in Neural Information Processing Systems, vol. 2015-January, 2015, pp. 1333–41.
Tepper, M., and G. Sapiro. “From local to global communities in large networks through consensus.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9423, 2015, pp. 659–66. Scopus, doi:10.1007/978-3-319-25751-8_79. Full Text
Qiu, Q., et al. “Data representation using the Weyl transform.” 3rd International Conference on Learning Representations, Iclr 2015 Workshop Track Proceedings, 2015.
Qiu, Q., et al. “Random forests can hash.” 3rd International Conference on Learning Representations, Iclr 2015 Workshop Track Proceedings, 2015.
Tepper, M., and G. Sapiro. “Intersecting 2D lines: A simple method for detecting vanishing points.” 2014 Ieee International Conference on Image Processing, Icip 2014, 2014, pp. 1056–60. Scopus, doi:10.1109/ICIP.2014.7025210. Full Text
Qiu, Q., and G. Sapiro. “Learning compressed image classification features.” 2014 Ieee International Conference on Image Processing, Icip 2014, 2014, pp. 5761–65. Scopus, doi:10.1109/ICIP.2014.7026165. Full Text
Yuan, X., et al. “Low-cost compressive sensing for color video and depth.” Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 3318–25. Scopus, doi:10.1109/CVPR.2014.424. Full Text