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Guillermo Sapiro, Edmund T. Pratt, Jr. School Professor of Electrical and Computer Engineering and Professor of Mathematics and Computer Science and Faculty Network Member of Duke Institute for Brain Sciences

Guillermo Sapiro

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.

G. Sapiro is a Fellow of IEEE and SIAM.

G. Sapiro was the founding Editor-in-Chief of the SIAM Journal on Imaging Sciences.

Contact Info:
Office Location:  140 Science Drive
Office Phone:  (919) 660-5252
Email Address: send me a message
Web Pages:  http://www.math.duke.edu/~gs108
http://sapirolab.pratt.duke.edu/

Teaching (Fall 2017):

  • ECE 590.06, ADVANCED TOPICS IN ECE Synopsis
    Hudson 216, F 08:30 AM-11:30 AM
Teaching (Spring 2018):

  • ECE 588.01, IMAGE & VIDEO PROCESSING Synopsis
    Gross Hall 103, F 08:30 AM-11:20 AM
Office Hours:

By appointment. Contact via e-mail.
Education:

D.Sc.Israel Institute of Technology1993
MSTechnion, Haifa, Israel1991
BSTechnion, Haifa, Israel1989
Research Interests:

Image and video processing, computer vision, computer graphics, computational vision, biomedical imaging, brain imaging, cryo-tomography of viruses, computational tools in cryo-tomography, computational tools in early diagnosis of psychiatric disorders, differential geometry and differential equations, scientific computation, learning and high dimensional data analysis, sparse modeling and dictionary learning, applied mathematics.

Recent Publications   (More Publications)

  1. Pisharady, PK; Sotiropoulos, SN; Sapiro, G; Lenglet, C, A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI., Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 10433 (September, 2017), pp. 602-610, ISBN 9783319661810 [doi]  [abs]
  2. Sokolić, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Generalization error of deep neural networks: Role of classification margin and data structure, 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017 (September, 2017), pp. 147-151, ISBN 9781538615652 [doi]  [abs]
  3. Sokolic, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Robust Large Margin Deep Neural Networks, IEEE Transactions on Signal Processing, vol. 65 no. 16 (August, 2017), pp. 4265-4280 [doi]
  4. Pisharady, PK; Sotiropoulos, SN; Duarte-Carvajalino, JM; Sapiro, G; Lenglet, C, Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning., NeuroImage (June, 2017) [doi]  [abs]
  5. Simhal, AK; Aguerrebere, C; Collman, F; Vogelstein, JT; Micheva, KD; Weinberg, RJ; Smith, SJ; Sapiro, G, Probabilistic fluorescence-based synapse detection., PLoS computational biology, vol. 13 no. 4 (April, 2017), pp. e1005493 [doi]  [abs]
Recent Grant Support

  • CIF: AF: Small: Foundations of Multimodal Information Integration, National Science Foundation, 1712867, 2017/09-2020/08.      
  • The Foundations of Dynamic Drone-based Threat Detection, National Science Foundation, 1737744, 2017/09-2020/08.      
  • GitPaper: A Networked Model of Scientific Review and Dissemination, Office of Naval Research, N00014-17-1-2781, 2017/09-2020/08.      
  • Modeling, Computations, and Applications in Multimodal Information Integration, Office of Naval Research, N00014, 2016/04-2019/10.      
  • Training in Medical Imaging, National Institutes of Health, 2003/07-2019/08.      
  • Network motifs in cortical computation, University of California - Los Angeles, 1430 G UA755, 2016/09-2019/06.      
  • Synaptomes of Mice and Men, Allen Institute for Brain Science, 2014-0567, 2014/09-2019/06.      
  • Nonparametric Bayes Methods for Big Data in Neuroscience, National Institutes of Health, 2014/09-2019/06.      
  • Scalable Quantitative Video Analysis for Online Phenotyping, Early Screening, and Symptom Monitoring for Autism Spectrum Disorders, Simons Foundation Autism Research Initiative, 2017/10-2018/09.      
  • Multimodal Subspace Learning and Modeling of Complex Systems, Army Research Laboratory, W911NF-16-1-0088, 2016/04-2018/09.      
  • Path Toward MRI with Direct Sensitivity to Neuro-Electro-Magnetic Oscillations, National Institutes of Health, 2014/09-2018/06.      
  • Learning to Exploit Big Data, National Geospatial-Intelligence Agency, HM01771310007, 2013/02-2017/12.      
  • AF: Small: Learning to Parsimoniously Model and Compute with Big Data, National Science Foundation, CCF-1318168, 2013/09-2017/08.      
  • Visitors to the Information Initiative at Duke, Office of Naval Research, N00014-15-1-2334, 2015/05-2016/11.      
  • Structured and Collaborative Geometric Signal Models for Big Data Analysis: Theory and Applications in Image, Video and, Army Research Office, W911NF-13-1-0011, 2012/11-2016/11.      
  • Informed Signal Models: Theory and Applications in Image Sciences, Office of Naval Research, N00014-12-1-0839, 2012/07-2016/10.      
  • MRI: Development of an Instrument that Monitors Behaviors with OCD and Schizophrenia, University of Minnesota, A003891201, 2013/10-2016/07.      
  • Information Acquisition, Analysis, and Integration, University of Minnesota, A001413001, 2012/07-2016/04.      

 

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320