- 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.
Network Inference and Analysis of Big Dynamic Data awarded by (Principal Investigator). 2016 to 2020
Modeling, Computations, and Applications in Multimodal Information Integration awarded by Office of Naval Research (Principal Investigator). 2016 to 2019
Multimodal Subspace Learning and Modeling of Complex Systems awarded by (Principal Investigator). 2016 to 2019
Network motifs in cortical computation awarded by University of California, Los Angeles (Co-Principal Investigator). 2016 to 2019
Synaptomes of Mice and Men awarded by (Principal Investigator). 2014 to 2019
Nonparametric Bayes Methods for Big Data in Neuroscience awarded by National Institutes of Health (Co-Mentor). 2014 to 2019
Scalable Quantitative Video Analysis for Online Phenotyping, Early Screening, and Symptom Monitoring for Autism Spectrum Disorders awarded by (Principal Investigator). 2017 to 2018
Path Toward MRI with Direct Sensitivity to Neuro-Electro-Magnetic Oscillations awarded by National Institutes of Health (Co Investigator). 2014 to 2018
Learning to Exploit Big Data awarded by (Principal Investigator). 2013 to 2017
Precursors to the development of anxiety disorders in young children with autism spectrum disorder awarded by Department of Defense (Collaborator). 2014 to 2017
Bartesaghi, Alberto, et al. “Atomic Resolution Cryo-EM Structure of β-Galactosidase.” Structure (London, England : 1993), vol. 26, no. 6, June 2018, pp. 848-856.e3. Epmc, doi:10.1016/j.str.2018.04.004. Full Text
Giryes, R., et al. “Tradeoffs between convergence speed and reconstruction accuracy in inverse problems.” Ieee Transactions on Signal Processing, vol. 66, no. 7, Apr. 2018, pp. 1676–90. Scopus, doi:10.1109/TSP.2018.2791945. Full Text
Vu, Mai-Anh T., et al. “A Shared Vision for Machine Learning in Neuroscience.” J Neurosci, vol. 38, no. 7, Feb. 2018, pp. 1601–07. Pubmed, doi:10.1523/JNEUROSCI.0508-17.2018. Full Text
Pisharady, Pramod Kumar, et al. “Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning.” Neuroimage, vol. 167, Feb. 2018, pp. 488–503. Epmc, doi:10.1016/j.neuroimage.2017.06.052. Full Text
Qiu, Q., et al. “ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11206 LNCS, Jan. 2018, pp. 442–59. Scopus, doi:10.1007/978-3-030-01216-8_27. Full Text
Qiu, Q., et al. “DCFNet: Deep Neural Network with Decomposed Convolutional Filters.” 35th International Conference on Machine Learning, Icml 2018, vol. 9, Jan. 2018, pp. 6687–96. Open Access Copy
Bovery, M. D. M. J., et al. “A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder.” Ieee Transactions on Affective Computing, Jan. 2018. Scopus, doi:10.1109/TAFFC.2018.2890610. Full Text
Bertrán, Martín A., et al. “Active learning of cortical connectivity from two-photon imaging data.” Plos One, vol. 13, no. 5, Jan. 2018, p. e0196527. Epmc, doi:10.1371/journal.pone.0196527. Full Text
Simhal, Anish K., et al. “A Computational Synaptic Antibody Characterization Tool for Array Tomography.” Frontiers in Neuroanatomy, vol. 12, Jan. 2018, p. 51. Epmc, doi:10.3389/fnana.2018.00051. Full Text
Duchin, Yuval, et al. “Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI.” Plos One, vol. 13, no. 8, Jan. 2018, p. e0201469. Epmc, doi:10.1371/journal.pone.0201469. Full Text
Kim, J., et al. “Clinical deep brain stimulation region prediction using regression forests from high-field MRI.” Proceedings International Conference on Image Processing, Icip, vol. 2015-December, 2015, pp. 2480–84. Scopus, doi:10.1109/ICIP.2015.7351248. Full Text
Tepper, M., et al. “Multi-temporal foreground detection in videos.” Proceedings International Conference on Image Processing, Icip, vol. 2015-December, 2015, pp. 4599–603. Scopus, doi:10.1109/ICIP.2015.7351678. Full Text
Hashemi, J., et al. “Cross-modality pose-invariant facial expression.” Proceedings International Conference on Image Processing, Icip, vol. 2015-December, 2015, pp. 4007–11. Scopus, doi:10.1109/ICIP.2015.7351558. Full Text
Delbracio, M., and G. Sapiro. “Burst deblurring: Removing camera shake through fourier burst accumulation.” Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, vol. 07-12-June-2015, 2015, pp. 2385–93. Scopus, doi:10.1109/CVPR.2015.7298852. Full Text
Huang, J., et al. “Geometry-aware deep transform.” Proceedings of the Ieee International Conference on Computer Vision, vol. 2015 International Conference on Computer Vision, ICCV 2015, 2015, pp. 4139–47. Scopus, doi:10.1109/ICCV.2015.471. Full Text
Qiu, Q., et al. “Data representation using the Weyl transform.” 3rd International Conference on Learning Representations, Iclr 2015 Workshop Track Proceedings, 2015.
Kim, J., et al. “Clinical subthalamic nucleus prediction from high-field brain MRI.” Proceedings International Symposium on Biomedical Imaging, vol. 2015-July, 2015, pp. 1264–67. Scopus, doi:10.1109/ISBI.2015.7164104. Full Text
Giryes, R., et al. “On the stability of deep networks.” 3rd International Conference on Learning Representations, Iclr 2015 Workshop Track Proceedings, 2015.
Huang, J., et al. “Alignment with intra-class structure can improve classification.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings, vol. 2015-August, 2015, pp. 1921–25. Scopus, doi:10.1109/ICASSP.2015.7178305. Full Text
Kim, J., et al. “Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9350, 2015, pp. 587–94. Scopus, doi:10.1007/978-3-319-24571-3_70. Full Text