Guillermo Sapiro

  • Edmund T. Pratt, Jr. School Professor of Electrical and Computer Engineering
  • Professor of Electrical and Computer Engineering
  • Professor of Computer Science (Secondary)
  • Professor of Mathematics (Secondary)
  • Faculty Network Member of the Duke Institute for Brain Sciences
Internal office address: 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.

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

CIF: AF: Small: Foundations of Multimodal Information Integration awarded by National Science Foundation (Principal Investigator). 2017 to 2020

The Foundations of Dynamic Drone-based Threat Detection awarded by National Science Foundation (Principal Investigator). 2017 to 2020

GitPaper: A Networked Model of Scientific Review and Dissemination awarded by Office of Naval Research (Principal Investigator). 2017 to 2020

REU Site for Meeting the Grand Challenges in Engineering awarded by National Science Foundation (Mentor). 2017 to 2020

Modeling, Computations, and Applications in Multimodal Information Integration awarded by Office of Naval Research (Principal Investigator). 2016 to 2019

Training in Medical Imaging awarded by National Institutes of Health (Mentor). 2003 to 2019

Network motifs in cortical computation awarded by University of California - Los Angeles (Co-Principal Investigator). 2016 to 2019

Synaptomes of Mice and Men awarded by Allen Institute for Brain Science (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


Aganj, I, Sapiro, G, and Harel, N. "Q-Space Modeling in Diffusion-Weighted MRI." Brain Mapping: An Encyclopedic Reference. February 14, 2015. 257-263. Full Text

Pisharady, PK, Duarte-Carvajalino, JM, Sotiropoulos, SN, Sapiro, G, and Lenglet, C. "Sparse bayesian inference of white matter fiber orientations from compressed multi-resolution diffusion MRI." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). January 1, 2015. 117-124. Full Text

Mémoli, F, and Sapiro, G. "Computing with point cloud data." Modeling and Simulation in Science, Engineering and Technology. January 1, 2006. 201-229. Full Text

Pisharady, PK, Sotiropoulos, SN, Duarte-Carvajalino, JM, Sapiro, G, and Lenglet, C. "Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning." NeuroImage (June 29, 2017). Full Text

Simhal, AK, Aguerrebere, C, Collman, F, Vogelstein, JT, Micheva, KD, Weinberg, RJ, Smith, SJ, and Sapiro, G. "Probabilistic fluorescence-based synapse detection." PLoS computational biology 13.4 (April 17, 2017): e1005493-. Full Text

Campbell, K, Carpenter, KLH, Espinosa, S, Hashemi, J, Qiu, Q, Tepper, M, Calderbank, R, Sapiro, G, Egger, HL, Baker, JP, and Dawson, G. "Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism." The Journal of pediatrics 183 (April 2017): 133-139.e1. Full Text

Gunalan, K, Chaturvedi, A, Howell, B, Duchin, Y, Lempka, SF, Patriat, R, Sapiro, G, Harel, N, and McIntyre, CC. "Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example." PloS one 12.4 (January 2017): e0176132-. Full Text

Lezama, J, Mukherjee, D, McNabb, RP, Sapiro, G, Kuo, AN, and Farsiu, S. "Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes." Biomedical optics express 7.12 (December 2016): 4827-4846. Full Text

Aguerrebere, C, Delbracio, M, Bartesaghi, A, and Sapiro, G. "Fundamental Limits in Multi-Image Alignment." IEEE Transactions on Signal Processing 64.21 (November 1, 2016): 5707-5722. Full Text

Elhamifar, E, Sapiro, G, and Sastry, SS. "Dissimilarity-Based Sparse Subset Selection." IEEE transactions on pattern analysis and machine intelligence 38.11 (November 2016): 2182-2197. Full Text

Giryes, R, Sapiro, G, and Bronstein, AM. "Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?." IEEE Transactions on Signal Processing 64.13 (July 1, 2016): 3444-3457. Full Text

Tepper, M, and Sapiro, G. "Compressed Nonnegative Matrix Factorization Is Fast and Accurate." IEEE Transactions on Signal Processing 64.9 (May 2016): 2269-2283. Full Text

Qiu, Q, Thompson, A, Calderbank, R, and Sapiro, G. "Data Representation Using the Weyl Transform." IEEE Transactions on Signal Processing 64.7 (April 2016): 1844-1853. Full Text


Chen, J, Chang, Z, Qiu, Q, Li, X, Sapiro, G, Bronstein, A, and Pietikäinen, M. "RealSense = real heart rate: Illumination invariant heart rate estimation from videos." January 17, 2017. Full Text

Chang, Z, Qiu, Q, and Sapiro, G. "Synthesis-based low-cost gaze analysis." January 1, 2016. Full Text

Kim, J, Duchin, Y, Sapiro, G, Vitek, J, and Harel, N. "Clinical deep brain stimulation region prediction using regression forests from high-field MRI." December 9, 2015. Full Text

Tepper, M, Newson, A, Sprechmann, P, and Sapiro, G. "Multi-temporal foreground detection in videos." December 9, 2015. Full Text

Hashemi, J, Qiu, Q, and Sapiro, G. "Cross-modality pose-invariant facial expression." December 9, 2015. Full Text

Draelos, M, Qiu, Q, Bronstein, A, and Sapiro, G. "Intel realsense = Real low cost gaze." December 9, 2015. Full Text

Huang, J, Qiu, Q, Calderbank, R, and Sapiro, G. "Geometry-aware deep transform." February 17, 2015. Full Text


The Lives of Things. Consultant. (2015)


The Nasher Museum has one of the most important collections of medieval art in an American university. These objects are mounted against the white walls of the Nasher Museum with short labels by way of identification. Yet how many of the visitors to the museum understand that these objects were once brightly painted, and once part of full-length figures that enriched the doorways and facades of medieval churches - that they were integrated into much larger decorative programs? The Lives of Things is a collaboration between Engineering and Art, Art History and Visual Studies. Computer scientists and engineers work with artists and art historians, using programming and graphical user interface design for artistic and historical contextualization with augmented reality and interactive capabilities. This eclectic blend of knowledges and capabilities brings new possibilities for interdisciplinary teamwork of broad impact and for horizontal knowledge transmission. Our goal is to use emerging technologies for developing a new model of the engaged museum that reaches out to involve the public of all ages in reconnecting works of art to their original context (e.g., chapels, church portals, or facades) through interactive and gaming displays. Our first installation is now part of the Nasher permanent collection. This is a collaboration with Dr. Tepper (engineering leader), Prof. Olson (art history leader) and Prof. Bruzelius.