Guillermo Sapiro

Guillermo Sapiro
  • 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
Internal office address: Campus Box 90984, 140 Science Drive - 325 Gross Hall, Durham, NC 27708
Office Hours: 

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.

Education & Training
  • D.Sc., Israel Institute of Technology 1993

Selected Grants

Synaptomes of Mice and Men awarded by (Principal Investigator). 2014 to 2019

Synaptomes of Mice and Men awarded by (Principal Investigator). 2014 to 2019

Nonparametric Bayes Methods for Big Data in Neuroscience awarded by National Institutes of Health (Co-Mentor). 2014 to 2019

Path Toward MRI with Direct Sensitivity to Neuro-Electro-Magnetic Oscillations awarded by National Institutes of Health (Co Investigator). 2014 to 2018

Learning to Exploit Big Data awarded by (Principal Investigator). 2013 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

Pages

Bovery, M. D. M. J., et al. “A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder.” Ieee Transactions on Affective Computing, Jan. 2018. Scopus, doi:10.1109/TAFFC.2018.2890610. Full Text

Bertrán, Martín A., et al. “Active learning of cortical connectivity from two-photon imaging data..” Plos One, vol. 13, no. 5, Jan. 2018. Epmc, doi:10.1371/journal.pone.0196527. Full Text

Simhal, Anish K., et al. “A Computational Synaptic Antibody Characterization Tool for Array Tomography..” Frontiers in Neuroanatomy, vol. 12, Jan. 2018. Epmc, doi:10.3389/fnana.2018.00051. Full Text

Duchin, Yuval, et al. “Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI..” Plos One, vol. 13, no. 8, Jan. 2018. Epmc, doi:10.1371/journal.pone.0201469. Full Text

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. 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

Sokolić, J., et al. “Robust Large Margin Deep Neural Networks.” Ieee Transactions on Signal Processing, vol. 65, no. 16, Aug. 2017, pp. 4265–80. Scopus, doi:10.1109/TSP.2017.2708039. Full Text

Simhal, Anish K., et al. “Probabilistic fluorescence-based synapse detection..” Plos Computational Biology, vol. 13, no. 4, Apr. 2017. 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

Pages

Huang, J., et al. “Geometry-aware deep transform.” Proceedings of the Ieee International Conference on Computer Vision, vol. 2015 International Conference on Computer Vision, ICCV 2015, 2015, pp. 4139–47. Scopus, doi:10.1109/ICCV.2015.471. 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

Llull, P., et al. Temporal compressive sensing for video. no. 9783319160412, 2015, pp. 41–74. Scopus, doi:10.1007/978-3-319-16042-9_2. Full Text

Qiu, Q., et al. “Data representation using the Weyl transform.” 3rd International Conference on Learning Representations, Iclr 2015  Workshop Track Proceedings, 2015.

Kim, J., et al. “Clinical subthalamic nucleus prediction from high-field brain MRI.” Proceedings  International Symposium on Biomedical Imaging, vol. 2015-July, 2015, pp. 1264–67. Scopus, doi:10.1109/ISBI.2015.7164104. Full Text

Giryes, R., et al. “On the stability of deep networks.” 3rd International Conference on Learning Representations, Iclr 2015  Workshop Track Proceedings, 2015.

Huang, J., et al. “Alignment with intra-class structure can improve classification.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing  Proceedings, vol. 2015-August, 2015, pp. 1921–25. Scopus, doi:10.1109/ICASSP.2015.7178305. 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

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

Pages