- 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.
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
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
Lyzinski, V, Fishkind, DE, Fiori, M, Vogelstein, JT, Priebe, CE, and Sapiro, G. "Graph Matching: Relax at Your Own Risk." Ieee Transactions on Pattern Analysis and Machine Intelligence 38.1 (January 2016): 60-73. Full Text
Carpenter, KLH, Sprechmann, P, Calderbank, R, Sapiro, G, and Egger, HL. "Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach." PloS one 11.11 (January 2016): e0165524-. Full Text
Hashemi, J, Campbell, K, Carpenter, KLH, Harris, A, Qiu, Q, Tepper, M, Espinosa, S, Borg, JS, Marsan, S, Calderbank, R, Baker, JP, Egger, HL, Dawson, G, and Sapiro, G. "A scalable app for measuring autism risk behaviors in young children: A technical validity and feasibility study." MOBIHEALTH 2015 - 5th EAI International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies (December 22, 2015). Full Text
Delbracio, M, and Sapiro, G. "Removing Camera Shake via Weighted Fourier Burst Accumulation." Ieee Transactions on Image Processing 24.11 (November 2015): 3293-3307. Full Text
Qiu, Q, and Sapiro, G. "Learning transformations for clustering and classification." Journal of Machine Learning Research 16 (February 1, 2015): 187-225.
Yang, J, Liao, X, Yuan, X, Llull, P, Brady, DJ, Sapiro, G, and Carin, L. "Compressive sensing by learning a Gaussian mixture model from measurements." Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society 24.1 (January 2015): 106-119. Full Text
Huang, J, Qiu, Q, Calderbank, R, Rodrigues, M, and Sapiro, G. "Alignment with intra-class structure can improve classification." January 1, 2015. Full Text
Kim, J, Duchin, Y, Sapiro, G, Vitek, J, and Harel, N. "Clinical subthalamic nucleus prediction from high-field brain MRI." January 1, 2015. Full Text
Kim, J, Duchin, Y, Kim, H, Vitek, J, Harel, N, and Sapiro, G. "Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases." January 1, 2015. Full Text
Huang, J, Qiu, Q, Sapiro, G, and Calderbank, R. "Discriminative robust transformation learning." January 1, 2015.
Tepper, M, and Sapiro, G. "From local to global communities in large networks through consensus." January 1, 2015. Full Text
Yoo, TS, Lowekamp, BC, Kuybeda, O, Narayan, K, Frank, GA, Bartesaghi, A, Borgnia, M, Subramaniam, S, Sapiro, G, and Ackerman, MJ. "Accelerating Discovery in 3D Microanalysis: Leveraging Open Source Software and Deskside High Performance Computing." August 2014. Full Text
Qiu, Q, Sapiro, G, Qiu, Q, and Sapiro, G. "Learning compressed image classification featuresLearning compressed image classification features (PublishedPublished)." January 28, 2014. Full Text
Qiu, Q, Sapiro, G, Qiu, Q, and Sapiro, G. "Learning TransformationsLearning Transformations (PublishedPublished)." January 28, 2014. Full Text
Tepper, M, Sapiro, G, Tepper, M, and Sapiro, G. "Intersecting 2D lines: A simple method for detecting vanishing pointsIntersecting 2D lines: A simple method for detecting vanishing points (PublishedPublished)." January 28, 2014. Full Text