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Edmund T. Pratt, Jr. School Professor of Electrical and Computer Engineering
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.
Appointments and Affiliations
- Edmund T. Pratt, Jr. School Professor of Electrical and Computer Engineering
- Professor of Electrical and Computer Engineering
- Professor of Computer Science
- Professor of Mathematics
- Faculty Network Member of the Duke Institute for Brain Sciences
- D.Sc. Israel Institute of Technology, 1993
Image and video processing, computer vision, computer graphics, computational vision, biomedical imaging, brain imaging, cryo-tomography of viruses, computational tools in cryo-tomography, computational tools in early diagnosis of psychiatric disorders, differential geometry and differential equations, scientific computation, learning and high dimensional data analysis, sparse modeling and dictionary learning, applied mathematics.
Awards, Honors, and Distinctions
- Distinguished Israel Pollack Lecturer, Technion, Haifa.. Technion, Israel. 2016
- Plenary Speaker, European Signal Processing Conference (EUSIPCO). EUSIPCO. 2014
- Fellows. Institute for Electrical and Electronics Engineers. 2014
- Member, National Academies’ Board on Mathematical Sciences and their Applications (BMSA).. National Academies. 2014
- Science Advisory Board, Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University.. ICERM. 2014
- Fellow. Society for Industrial and Applied Mathematics. 2013
- Helmholtz Prize. IEEE Computer Society. 2011
- National Security Science and Engineering Faculty Fellows. Department of Defense. 2010
- BME 791: Graduate Independent Study
- BME 899: Special Readings in Biomedical Engineering
- COMPSCI 391: Independent Study
- ECE 391: Projects in Electrical and Computer Engineering
- ECE 392: Projects in Electrical and Computer Engineering
- ECE 494: Projects in Electrical and Computer Engineering
- ECE 588: Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital
- ECE 590: Advanced Topics in Electrical and Computer Engineering
- ECE 891: Internship
- ECE 899: Special Readings in Electrical Engineering
- EHD 395: Bass Connections: Interdisciplinary Team Projects
- EHD 795: Bass Connections: Interdisciplinary Team Projects
In the News
- Removing Blurred Lines Caused by Shaky Hands (Jun 8, 2017 | Pratt School of Engineering )
- A MindAnd an EarFor Big Data (Feb 27, 2017 | Pratt School of Engineering )
- New Collaborative Seed Grant Program Gives Eight Awards (Mar 16, 2016)
- New app may help diagnose autism in children (Jan 4, 2016 | The Times of India )
- Could this iPhone app transform how we diagnose autism? (Oct 15, 2015 | Vox.com )
- Duke Launches Autism Research App (Oct 14, 2015)
- 'Painting' Medieval Statues: Jordan Hashemi's Bass Connections Pathway (Aug 14, 2015)
- Guillermo Sapiro on innovative approaches to autism diagnoses (May 20, 2015)
- Duke Receives $9.75 Million for ‘Big Data’ Initiative (Apr 27, 2015)
- Burst Photography: A Method to Remove Image Blur (Apr 20, 2015)
- Big Insights, Little People: Duke Researchers Brief Officials on Childhood Mental Disorders (Dec 11, 2014)
- Putting Big Data to Work in Autism Diagnosis (Nov 25, 2014)
- 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; Dzirasa, K, A Shared Vision for Machine Learning in Neuroscience., The Journal of neuroscience : the official journal of the Society for Neuroscience, vol 38 no. 7 (2018), pp. 1601-1607 [10.1523/jneurosci.0508-17.2018] [abs].
- Giryes, R; Eldar, YC; Bronstein, A; Sapiro, G, Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems, IEEE Transactions on Signal Processing (2018) [10.1109/TSP.2018.2791945] [abs].
- Pisharady, PK; Sotiropoulos, SN; Sapiro, G; Lenglet, C, A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI., Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, vol 10433 (2017), pp. 602-610 [10.1007/978-3-319-66182-7_69] [abs].
- Sokolić, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Generalization error of deep neural networks: Role of classification margin and data structure, 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017 (2017), pp. 147-151 [10.1109/SAMPTA.2017.8024476] [abs].
- Sokolic, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Robust Large Margin Deep Neural Networks, IEEE Transactions on Signal Processing, vol 65 no. 16 (2017), pp. 4265-4280 [10.1109/TSP.2017.2708039] [abs].