Guillermo Sapiro

Guillermo Sapiro
  • 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
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

Tailoring treatment targets for early autism intervention in Africa awarded by National Institutes of Health (Co Investigator). 2019 to 2021

Building a streamlined birth cohort to study autism risk factors and biomarkers awarded by Drexel University (Co Investigator). 2020 to 2021

REU Site for Meeting the Grand Challenges in Engineering awarded by National Science Foundation (Mentor). 2017 to 2021

CIF: AF: Small: Foundations of Multimodal Information Integration awarded by National Science Foundation (Principal Investigator). 2017 to 2020

GitPaper: A Networked Model of Scientific Review and Dissemination awarded by Office of Naval Research (Principal Investigator). 2017 to 2020

The Foundations of Dynamic Drone-based Threat Detection awarded by National Science Foundation (Principal Investigator). 2017 to 2020

Training in Medical Imaging awarded by National Institutes of Health (Mentor). 2003 to 2020

Network Inference and Analysis of Big Dynamic Data awarded by (Principal Investigator). 2016 to 2020

Pages

Sapiro, G., et al. “Computer vision and behavioral phenotyping: an autism case study.” Current Opinion in Biomedical Engineering, vol. 9, Mar. 2019, pp. 14–20. Scopus, doi:10.1016/j.cobme.2018.12.002. Full Text

Kim, Jinyoung, et al. “Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation.Human Brain Mapping, vol. 40, no. 2, Feb. 2019, pp. 679–98. Epmc, doi:10.1002/hbm.24404. Full Text

Cheng, X., et al. “RoTDCF: Decomposition of convolutional filters for rotation-equivariant deep networks.” 7th International Conference on Learning Representations, Iclr 2019, Jan. 2019. Open Access Copy

Azami, H., et al. “Multiscale fluctuation-based dispersion entropy and its applications to neurological diseases.” Ieee Access, vol. 7, Jan. 2019, pp. 68718–33. Scopus, doi:10.1109/ACCESS.2019.2918560. Full Text

Fellous, Jean-Marc, et al. “Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation.Frontiers in Neuroscience, vol. 13, Jan. 2019, p. 1346. Epmc, doi:10.3389/fnins.2019.01346. Full Text

Zhu, W., et al. “LDMNet: Low Dimensional Manifold Regularized Neural Networks.” Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, Dec. 2018, pp. 2743–51. Scopus, doi:10.1109/CVPR.2018.00290. Full Text

Lezama, J., et al. “OLE: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning.” Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, Dec. 2018, pp. 8109–18. Scopus, doi:10.1109/CVPR.2018.00846. Full Text

Dawson, Geraldine, et al. “Atypical postural control can be detected via computer vision analysis in toddlers with autism spectrum disorder.Sci Rep, vol. 8, no. 1, Nov. 2018, p. 17008. Pubmed, doi:10.1038/s41598-018-35215-8. Full Text

Aguerrebere, C., et al. “A Practical Guide to Multi-Image Alignment.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing  Proceedings, vol. 2018-April, Sept. 2018, pp. 1927–31. Scopus, doi:10.1109/ICASSP.2018.8461588. Full Text

Hashemi, J., et al. “Computer Vision Analysis for Quantification of Autism Risk Behaviors.” Ieee Transactions on Affective Computing, Aug. 2018. Scopus, doi:10.1109/TAFFC.2018.2868196. Full Text

Pages

Qiu, Q., et al. “Intelligent synthesis driven model calibration: framework and face recognition application.” Proceedings  2017 Ieee International Conference on Computer Vision Workshops, Iccvw 2017, vol. 2018-January, 2017, pp. 2564–72. Scopus, doi:10.1109/ICCVW.2017.301. Full Text

Sokolić, J., et al. “Learning to identify while failing to discriminate.” Proceedings  2017 Ieee International Conference on Computer Vision Workshops, Iccvw 2017, vol. 2018-January, 2017, pp. 2537–44. Scopus, doi:10.1109/ICCVW.2017.298. Full Text

Chen, J., et al. “RealSense = real heart rate: Illumination invariant heart rate estimation from videos.” 2016 6th International Conference on Image Processing Theory, Tools and Applications, Ipta 2016, 2017. Scopus, doi:10.1109/IPTA.2016.7820970. Full Text

Sokolić, J., et al. “Generalization error of invariant classifiers.” Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Aistats 2017, 2017.

Sokolić, J., et al. “Generalization error of invariant classifiers.” Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Aistats 2017, 2017.

Fiori, M., et al. “Tell me where you are and i tell you where you are going: Estimation of dynamic mobility graphs.” Proceedings of the Ieee Sensor Array and Multichannel Signal Processing Workshop, vol. 2016-September, 2016. Scopus, doi:10.1109/SAM.2016.7569685. Full Text

Tepper, M., and G. Sapiro. “A short-graph fourier transform via personalized pagerank vectors.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing  Proceedings, vol. 2016-May, 2016, pp. 4806–10. Scopus, doi:10.1109/ICASSP.2016.7472590. Full Text

Chang, Z., et al. “Synthesis-based low-cost gaze analysis.” Communications in Computer and Information Science, vol. 618, 2016, pp. 95–100. Scopus, doi:10.1007/978-3-319-40542-1_15. Full Text

Qiu, Q., et al. “Low-cost gaze and pulse analysis using realsense.” Mobihealth 2015  5th Eai International Conference on Wireless Mobile Communication and Healthcare  Transforming Healthcare Through Innovations in Mobile and Wireless Technologies, 2015. Scopus, doi:10.4108/eai.14-10-2015.2261657. Full Text

Draelos, M., et al. “Intel realsense = Real low cost gaze.” Proceedings  International Conference on Image Processing, Icip, vol. 2015-December, 2015, pp. 2520–24. Scopus, doi:10.1109/ICIP.2015.7351256. Full Text

Pages