- James B. Duke Distinguished Professor of Electrical and Computer Engineering
- Professor of Electrical and Computer Engineering
- Professor of Mathematics (Secondary)
- Professor of Computer Science (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.
Tepper, M., and G. Sapiro. “Ants crawling to discover the community structure in networks.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8259 LNCS, no. PART 2, Dec. 2013, pp. 552–59. Scopus, doi:10.1007/978-3-642-41827-3_69. Full Text
Yang, J., et al. “Gaussian mixture model for video compressive sensing.” 2013 Ieee International Conference on Image Processing, Icip 2013 Proceedings, Dec. 2013, pp. 19–23. Scopus, doi:10.1109/ICIP.2013.6738005. Full Text
Tepper, M., and G. Sapiro. “Fast L1 smoothing splines with an application to Kinect depth data.” 2013 Ieee International Conference on Image Processing, Icip 2013 Proceedings, Dec. 2013, pp. 504–08. Scopus, doi:10.1109/ICIP.2013.6738104. Full Text
Yuan, X., et al. “Adaptive temporal compressive sensing for video.” 2013 Ieee International Conference on Image Processing, Icip 2013 Proceedings, Dec. 2013, pp. 14–18. Scopus, doi:10.1109/ICIP.2013.6738004. Full Text Open Access Copy
Sprechmann, P., et al. “Audio restoration from multiple copies.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, Oct. 2013, pp. 878–82. Scopus, doi:10.1109/ICASSP.2013.6637774. Full Text
Sprechmann, P., et al. “Learnable low rank sparse models for speech denoising.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, Oct. 2013, pp. 136–40. Scopus, doi:10.1109/ICASSP.2013.6637624. Full Text
Cetingul, H. E., et al. “Importance sampling spherical harmonics to improve probabilistic tractography.” Proceedings 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, Prni 2013, Oct. 2013, pp. 46–49. Scopus, doi:10.1109/PRNI.2013.21. Full Text
Sotiropoulos, Stamatios N., et al. “Advances in diffusion MRI acquisition and processing in the Human Connectome Project.” Neuroimage, vol. 80, Oct. 2013, pp. 125–43. Epmc, doi:10.1016/j.neuroimage.2013.05.057. Full Text
Uğurbil, Kamil, et al. “Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project.” Neuroimage, vol. 80, Oct. 2013, pp. 80–104. Epmc, doi:10.1016/j.neuroimage.2013.05.012. Full Text
Chen, Bo, et al. “Deep learning with hierarchical convolutional factor analysis.” Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, Aug. 2013, pp. 1887–901. Epmc, doi:10.1109/tpami.2013.19. Full Text
Mairal, Julien, et al. “Multiscale sparse image representation with learned dictionaries.” 2007 Ieee International Conference on Image Processing, Vols 1 7, 2007, pp. 1233-+.
Bartesaghi, A., and G. Sapiro. “Tracking of moving objects under severe and total occlusions.” 2005 International Conference on Image Processing (Icip), Vols 1 5, IEEE, 2005, pp. 249–52.
Patwardhan, K. A., et al. “Video inpainting of occluding and occluded objects.” 2005 International Conference on Image Processing (Icip), Vols 1 5, 2005, pp. 1593–96.
Sapiro, G., and G. IEEE. “Inpainting the colors.” 2005 International Conference on Image Processing (Icip), Vols 1 5, 2005, pp. 1265–68.
Sole, A., et al. “Morse description and geometric encoding of digital elevation maps.” Free Boundary Problems: Theory and Applications, vol. 147, 2004, pp. 303–12.
Bertalmió, M., et al. “Variational problems and PDEs on implicit surfaces.” Proceedings Ieee Workshop on Variational and Level Set Methods in Computer Vision, Vlsm 2001, 2001, pp. 186–93. Scopus, doi:10.1109/VLSM.2001.938899. Full Text
Sapiro, G. “Harmonic map flows and image processing.” Foundations of Computational Mathematics, vol. 284, 2001, pp. 299–322.
Betelu, S., et al. “Noise-resistant affine skeletons of planar curves.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1842, 2000, pp. 742–54.
Giblin, P. J., and G. Sapiro. “Affine versions of the symmetry set.” Real and Complex Singularities, vol. 412, 2000, pp. 173–87.