- James B. Duke 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
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.
Learning and Privacy in a Closed Environment awarded by Office of Naval Research (Principal Investigator). 2018 to 2021
The Foundations of Dynamic Drone-based Threat Detection awarded by National Science Foundation (Principal Investigator). 2017 to 2020
CIF: AF: Small: Foundations of Multimodal Information Integration 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
Pokrass, J, Bronstein, AM, Bronstein, MM, Sprechmann, P, and Sapiro, G. "Sparse models for intrinsic shape correspondence." Mathematics and Visualization. January 1, 2016. 211-230. 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." October 2015. 117-124. Full Text
Sprechmann, P, Bronstein, AM, and Sapiro, G. "Supervised non-negative matrix factorization for audio source separation." Applied and Numerical Harmonic Analysis. January 1, 2015. 407-420. Full Text
Bartesaghi, A, Aguerrebere, C, Falconieri, V, Banerjee, S, Earl, LA, Zhu, X, Grigorieff, N, Milne, JLS, Sapiro, G, Wu, X, and Subramaniam, S. "Atomic Resolution Cryo-EM Structure of β-Galactosidase." Structure (London, England : 1993) 26.6 (June 2018): 848-856.e3. Full Text
Giryes, R, Eldar, YC, Bronstein, AM, and Sapiro, G. "Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems." Ieee Transactions on Signal Processing 66.7 (April 1, 2018): 1676-1690. Full Text
Campbell, K, Carpenter, KL, Hashemi, J, Espinosa, S, Marsan, S, Borg, JS, Chang, Z, Qiu, Q, Vermeer, S, Adler, E, Tepper, M, Egger, HL, Baker, JP, Sapiro, G, and Dawson, G. "Computer vision analysis captures atypical attention in toddlers with autism." Autism : the international journal of research and practice (March 2018): 1362361318766247-. Full Text
Vu, M-AT, Adalı, T, Ba, D, Buzsáki, G, Carlson, D, Heller, K, Liston, C, Rudin, C, Sohal, VS, Widge, AS, Mayberg, HS, Sapiro, G, and Dzirasa, K. "A Shared Vision for Machine Learning in Neuroscience." The Journal of Neuroscience : the Official Journal of the Society for Neuroscience 38.7 (February 2018): 1601-1607. 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 167 (February 2018): 488-503. Full Text
Chiew, KS, Hashemi, J, Gans, LK, Lerebours, L, Clement, NJ, Vu, M-AT, Sapiro, G, Heller, NE, and Adcock, RA. "Motivational valence alters memory formation without altering exploration of a real-life spatial environment." Plos One 13.3 (January 2018): e0193506-null. Full Text
Bertrán, MA, Martínez, NL, Wang, Y, Dunson, D, Sapiro, G, and Ringach, D. "Active learning of cortical connectivity from two-photon imaging data." Plos One 13.5 (January 2018): e0196527-null. Full Text
Duchin, Y, Shamir, RR, Patriat, R, Kim, J, Vitek, JL, Sapiro, G, and Harel, N. "Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI." Plos One 13.8 (January 2018): e0201469-null. Full Text
Lezama, J, Qiu, Q, and Sapiro, G. "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 2017-January (November 6, 2017): 6807-6816. Full Text
Ye, Q, Zhang, T, Ke, W, Qiu, Q, Chen, J, Sapiro, G, and Zhang, B. "Self-learning scene-specific pedestrian detectors using a progressive latent model." Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017 2017-January (November 6, 2017): 2057-2066. Full Text
Qiu, Q, Hashemi, J, and Sapiro, G. "Intelligent synthesis driven model calibration: framework and face recognition application." January 19, 2018. Full Text
Sokolić, J, Qiu, Q, Rodrigues, MRD, and Sapiro, G. "Learning to identify while failing to discriminate." January 19, 2018. Full Text
Asiedu, MN, Simhal, A, Lam, CT, Mueller, J, Chaudhary, U, Schmitt, JW, Sapiro, G, and Ramanujam, N. "Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a low-cost point-of-care digital colposcope." January 1, 2018. Full Text
Tepper, M, and Sapiro, G. "Nonnegative matrix underapproximation for robust multiple model fitting." November 6, 2017. Full Text
Pisharady, PK, Sotiropoulos, SN, Sapiro, G, and Lenglet, C. "A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI." September 4, 2017. Full Text
Sokolić, J, Giryes, R, Sapiro, G, and Rodrigues, MRD. "Generalization error of deep neural networks: Role of classification margin and data structure." September 1, 2017. 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
Fiori, M, Muse, P, Tepper, M, and Sapiro, G. "Tell me where you are and i tell you where you are going: Estimation of dynamic mobility graphs." September 15, 2016. Full Text
Tepper, M, and Sapiro, G. "A short-graph fourier transform via personalized pagerank vectors." May 18, 2016. Full Text
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.