- James B. Duke Professor of Electrical and Computer Engineering
- Professor of Electrical and Computer Engineering
- 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.
Zhan, Liang, et al. “Magnetic resonance field strength effects on diffusion measures and brain connectivity networks..” Brain Connectivity, vol. 3, no. 1, Jan. 2013, pp. 72–86. Epmc, doi:10.1089/brain.2012.0114. Full Text
Duarte-Carvajalino, J. M., et al. “Task-driven adaptive statistical compressive sensing of gaussian mixture models.” Ieee Transactions on Signal Processing, vol. 61, no. 3, Jan. 2013, pp. 585–600. Scopus, doi:10.1109/TSP.2012.2225054. Full Text Open Access Copy
Chen, B., et al. “Deep Learning with Hierarchical Convolutional Factor Analysis..” Ieee Transactions on Pattern Analysis and Machine Intelligence, Jan. 2013.
Caruyer, Emmanuel, et al. “Motion Detection in Diffusion MRI via Online ODF Estimation..” International Journal of Biomedical Imaging, vol. 2013, Jan. 2013. Epmc, doi:10.1155/2013/849363. Full Text
Duarte-Carvajalino, Julio M., et al. “A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation..” Frontiers in Neuroscience, vol. 7, Jan. 2013. Epmc, doi:10.3389/fnins.2013.00041. Full Text
Duarte-Carvajalino, Julio Martin, et al. “Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models..” Ieee Trans. Signal Processing, vol. 61, 2013, pp. 585–600.
Qiu, Qiang, and Guillermo Sapiro. “Learning Robust Subspace Clustering..” Corr, vol. abs/1308.0273, 2013.
Qiu, Qiang, et al. “Domain-invariant Face Recognition using Learned Low-rank Transformation..” Corr, vol. abs/1308.0275, 2013.
Fiori, M., et al. “Robust multimodal graph matching: Sparse coding meets graph matching.” Advances in Neural Information Processing Systems, Jan. 2013.
Teo, P. C., et al. “Anisotropic smoothing of posterior probabilities.” Dynamical Systems, Control, Coding, Computer Vision, vol. 25, 1999, pp. 419–32.
Black, M. J., et al. “Robust anisotropic diffusion: Connections between robust statistics, line processing, and anisotropic diffusion.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1252, 1997, pp. 323–26. Scopus, doi:10.1007/3-540-63167-4_27. Full Text
Black, Michael J., et al. “Robust Anisotropic Diffusion: Connections Between Robust Statistics, Line Processing, and Anisotropic Diffusion..” Scale Space, edited by Bart M ter Haar Romeny et al., vol. 1252, Springer, 1997, pp. 323–26.
Caselles, V., et al. “Three dimensional object modeling via minimal surfaces.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1064, 1996, pp. 97–106.
Sapiro, Guillermo, and Vicent Caselles. “Simultaneous contrast improvement and denoising via diffusion-related equations.” Vision Geometry Iv, SPIE, 1995. Crossref, doi:10.1117/12.216427. Full Text
Sapiro, Guillermo, et al. “Object detection and measurements in medical images via geodesic deformable contours.” Vision Geometry Iv, SPIE, 1995. Crossref, doi:10.1117/12.216429. Full Text
Sapiro, Guillermo. “Geometric invariant signatures and flows: classification and applications in image analysis.” Automatic Systems for the Identification and Inspection of Humans, SPIE, 1994. Crossref, doi:10.1117/12.191890. Full Text
Sapiro, G., et al. “Experiments on geometric image enhancement.” Proceedings International Conference on Image Processing, Icip, vol. 2, 1994, pp. 472–76. Scopus, doi:10.1109/ICIP.1994.413615. Full Text
Sapiro, G., and A. Tannenbaum. “Area and length preserving geometric invariant scale-spaces.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 801 LNCS, 1994, pp. 449–58.