Guillermo Sapiro

Guillermo Sapiro
  • 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
Internal office address: Campus Box 90984, 140 Science Drive - 325 Gross Hall, Durham, NC 27708
Office Hours: 

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.

Education & Training
  • D.Sc., Israel Institute of Technology 1993

Selected Grants

Informed Signal Models: Theory and Applications in Image Sciences awarded by Office of Naval Research (Principal Investigator). 2012 to 2016

MRI: Development of an Instrument that Monitors Behaviors with OCD and Schizophrenia awarded by University of Minnesota (Principal Investigator). 2013 to 2016

Information Acquisition, Analysis, and Integration awarded by University of Minnesota (Principal Investigator). 2012 to 2016

HARDI Mapping of Disease Effects on the Brain awarded by University of California, Los Angeles (Principal Investigator). 2012 to 2014

Learning sparse representations for restoration and classification: awarded by National Science Foundation (Principal Investigator). 2012 to 2013

Pages

Gunalan, Kabilar, et al. “Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example..” Plos One, vol. 12, no. 4, Jan. 2017. Epmc, doi:10.1371/journal.pone.0176132. Full Text

Lezama, José, et al. “Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes..” Biomed Opt Express, vol. 7, no. 12, Dec. 2016, pp. 4827–46. Pubmed, doi:10.1364/BOE.7.004827. Full Text

Aguerrebere, C., et al. “Fundamental limits in multi-image alignment.” Ieee Transactions on Signal Processing, vol. 64, no. 21, Nov. 2016, pp. 5707–22. Scopus, doi:10.1109/TSP.2016.2600517. Full Text

Elhamifar, Ehsan, et al. “Dissimilarity-Based Sparse Subset Selection..” Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 11, Nov. 2016, pp. 2182–97. Epmc, doi:10.1109/tpami.2015.2511748. Full Text

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

Qiu, Q., et al. “Data Representation Using the Weyl Transform.” Ieee Transactions on Signal Processing, vol. 64, no. 7, Apr. 2016, pp. 1844–53. Scopus, doi:10.1109/TSP.2015.2505661. 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. 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

Pages

Tepper, M., and G. Sapiro. “Intersecting 2D lines: A simple method for detecting vanishing points.” 2014 Ieee International Conference on Image Processing, Icip 2014, 2014, pp. 1056–60. Scopus, doi:10.1109/ICIP.2014.7025210. Full Text

Qiu, Q., and G. Sapiro. “Learning compressed image classification features.” 2014 Ieee International Conference on Image Processing, Icip 2014, 2014, pp. 5761–65. Scopus, doi:10.1109/ICIP.2014.7026165. Full Text

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

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

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

Masci, J., et al. “Sparse similarity-preserving hashing.” 2nd International Conference on Learning Representations, Iclr 2014  Conference Track Proceedings, 2014.

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.

Pages