- 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.
Qiu, Q., and G. Sapiro. “Learning transformations for clustering and classification.” Journal of Machine Learning Research, vol. 16, Feb. 2015, pp. 187–225.
Fiori, M., and G. Sapiro. “On spectral properties for graph matching and graph isomorphism problems.” Information and Inference, vol. 4, no. 1, Jan. 2015, pp. 63–76. Scopus, doi:10.1093/imaiai/iav002. Full Text
Yang, Jianbo, et al. “Compressive sensing by learning a Gaussian mixture model from measurements.” Ieee Transactions on Image Processing : A Publication of the Ieee Signal Processing Society, vol. 24, no. 1, Jan. 2015, pp. 106–19. Epmc, doi:10.1109/tip.2014.2365720. Full Text
Qiu, Q., et al. “Random forests can hash.” 3rd International Conference on Learning Representations, Iclr 2015 Workshop Track Proceedings, Jan. 2015.
Duarte-Carvajalino, Julio M., et al. “Estimation of the CSA-ODF using Bayesian compressed sensing of multi-shell HARDI.” Magnetic Resonance in Medicine, vol. 72, no. 5, Nov. 2014, pp. 1471–85. Epmc, doi:10.1002/mrm.25046. Full Text
Yang, Jianbo, et al. “Video compressive sensing using Gaussian mixture models.” Ieee Transactions on Image Processing : A Publication of the Ieee Signal Processing Society, vol. 23, no. 11, Nov. 2014, pp. 4863–78. Epmc, doi:10.1109/tip.2014.2344294. Full Text
Kim, Jinyoung, et al. “Semiautomatic segmentation of brain subcortical structures from high-field MRI.” Ieee Journal of Biomedical and Health Informatics, vol. 18, no. 5, Sept. 2014, pp. 1678–95. Epmc, doi:10.1109/jbhi.2013.2292858. Full Text
Prasad, Gautam, et al. “Automatic clustering and population analysis of white matter tracts using maximum density paths.” Neuroimage, vol. 97, Aug. 2014, pp. 284–95. Epmc, doi:10.1016/j.neuroimage.2014.04.033. Full Text
Zhou, T., et al. “Kernelized probabilistic matrix factorization: Exploiting graphs and side information.” Proceedings of the 12th Siam International Conference on Data Mining, Sdm 2012, 2012, pp. 403–14.
Castrodad, A., et al. “Sparse modeling for hyperspectral imagery with LiDAR data fusion for subpixel mapping.” International Geoscience and Remote Sensing Symposium (Igarss), 2012, pp. 7275–78. Scopus, doi:10.1109/IGARSS.2012.6351982. Full Text
Sprechmann, P., et al. “Real-time online singing voice separation from monaural recordings using robust low-rank modeling.” Proceedings of the 13th International Society for Music Information Retrieval Conference, Ismir 2012, 2012, pp. 67–72.
Hashemi, J., et al. “A computer vision approach for the assessment of autism-related behavioral markers.” 2012 Ieee International Conference on Development and Learning and Epigenetic Robotics, Icdl 2012, 2012. Scopus, doi:10.1109/DevLrn.2012.6400865. Full Text
Fasching, J., et al. “Detecting risk-markers in children in a preschool classroom.” Ieee International Conference on Intelligent Robots and Systems, 2012, pp. 1010–16. Scopus, doi:10.1109/IROS.2012.6385732. Full Text
Abosch, Aviva, et al. “178 Utility of 7T Imaging for Deep Brain Stimulation Surgery.” Neurosurgery, vol. 71, no. 2, Oxford University Press (OUP), 2012, pp. E569–70. Crossref, doi:10.1227/01.neu.0000417768.55934.bf. Full Text
Ramírez, Ignacio, and Guillermo Sapiro. “LOw-rank data modeling via the minimum description length principle.” Icassp, IEEE, 2012, pp. 2165–68.
Yu, Guoshen, and Guillermo Sapiro. “Statistical compressive sensing of Gaussian mixture models.” Icassp, IEEE, 2011, pp. 3728–31.
Léger, Flavien, et al. “Efficient matrix completion with Gaussian models.” Icassp, IEEE, 2011, pp. 1113–16.
Sprechmann, Pablo, et al. “Collaborative sources identification in mixed signals via hierarchical sparse modeling.” Icassp, IEEE, 2011, pp. 5816–19.