- 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.
Giryes, R., et al. “Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?” Ieee Transactions on Signal Processing, vol. 64, no. 13, July 2016, pp. 3444–57. Scopus, doi:10.1109/TSP.2016.2546221. Full Text
Tepper, M., and G. Sapiro. “Compressed Nonnegative Matrix Factorization Is Fast and Accurate.” Ieee Transactions on Signal Processing, vol. 64, no. 9, May 2016, pp. 2269–83. Scopus, doi:10.1109/TSP.2016.2516971. Full Text
Lyzinski, Vince, et al. “Graph Matching: Relax at Your Own Risk.” Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 1, Jan. 2016, pp. 60–73. Epmc, doi:10.1109/TPAMI.2015.2424894. Full Text
Carpenter, Kimberly L. H., et al. “Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach.” Plos One, vol. 11, no. 11, 2016, p. e0165524. Pubmed, doi:10.1371/journal.pone.0165524. Full Text
Hashemi, J., et al. “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, Dec. 2015. Scopus, doi:10.4108/eai.14-10-2015.2261939. Full Text
Delbracio, Mauricio, and Guillermo Sapiro. “Removing Camera Shake via Weighted Fourier Burst Accumulation.” Ieee Transactions on Image Processing : A Publication of the Ieee Signal Processing Society, vol. 24, no. 11, Nov. 2015, pp. 3293–307. Epmc, doi:10.1109/tip.2015.2442914. Full Text
Sprechmann, P., et al. “Learning Efficient Sparse and Low Rank Models.” Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, Sept. 2015, pp. 1821–33. Epmc, doi:10.1109/tpami.2015.2392779. Full Text
Qiu, Q., and G. Sapiro. “Learning transformations for clustering and classification.” Journal of Machine Learning Research, vol. 16, Feb. 2015, pp. 187–225.
Yuan, X., et al. “Low-cost compressive sensing for color video and depth.” Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 3318–25. Scopus, doi:10.1109/CVPR.2014.424. Full Text
Masci, J., et al. “Sparse similarity-preserving hashing.” 2nd International Conference on Learning Representations, Iclr 2014 Conference Track Proceedings, 2014.
Yoo, T. S., et al. “Accelerating discovery in 3D microanalysis: Leveraging open source software and deskside high performance computing.” Microscopy and Microanalysis, vol. 20, no. 3, 2014, pp. 774–75. Scopus, doi:10.1017/S1431927614005595. Full Text
Qiu, Q., and G. Sapiro. “Learning transformations for classification forests.” 2nd International Conference on Learning Representations, Iclr 2014 Conference Track Proceedings, 2014.
Dennis, E. L., et al. “Rich club analysis of structural brain connectivity at 7 tesla versus 3 tesla.” Mathematics and Visualization, vol. 0, 2014, pp. 209–18. Scopus, doi:10.1007/978-3-319-02475-2_19. Full Text
Frank, Gabriel A., et al. “Computational Separation of Conformational Heterogeneity using Cryo-Electron Tomography and 3D Sub-Volume Averaging.” Biophysical Journal, vol. 104, no. 2, Elsevier BV, 2013, pp. 350a-351a. Crossref, doi:10.1016/j.bpj.2012.11.1947. Full Text
Harris, Audray K., et al. “Molecular Structures of Native HA Trimers on 2009 H1N1 Pandemic Influenza Virus Complexed with Neutralizing Antibodies.” Biophysical Journal, vol. 104, no. 2, Elsevier BV, 2013, pp. 414a-414a. Crossref, doi:10.1016/j.bpj.2012.11.2310. Full Text
Tang, Zhongwei, et al. “Reflective Symmetry Detection by Rectifying Randomized Correspondences.” Procedings of the British Machine Vision Conference 2013, British Machine Vision Association, 2013. Crossref, doi:10.5244/c.27.115. Full Text
Yakar, T. B., et al. “Bilevel sparse models for polyphonic music transcription.” Proceedings of the 14th International Society for Music Information Retrieval Conference, Ismir 2013, 2013, pp. 65–70.
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