Guillermo Sapiro

Affiliations: 
University of Minnesota, Twin Cities, Minneapolis, MN 
Area:
Electronics and Electrical Engineering, Computer Science
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"Guillermo Sapiro"

Children

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Do-Hyun Chung grad student 2000 UMN
Marcelo Bertalmio grad student 2001 UMN
Alberto Bartesaghi grad student 2005 UMN
Roberto F. Memoli Techera grad student 2005 UMN
Kedar A. Patwardhan grad student 2007 UMN
Diego D. Rother grad student 2008 UMN
Iman Aganj grad student 2010 UMN
Mona Mahmoudi grad student 2010 UMN
Ignacio Ramirez grad student 2011 UMN
Alexey Castrodad grad student 2012 UMN
Liron Yatziv grad student 2012 UMN
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Publications

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Kim J, Duchin Y, Shamir RR, et al. (2018) 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
Duchin Y, Shamir RR, Patriat R, et al. (2018) Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI. Plos One. 13: e0201469
Shamir RR, Duchin Y, Kim J, et al. (2018) Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery. Neurosurgery
Bartesaghi A, Aguerrebere C, Falconieri V, et al. (2018) Atomic Resolution Cryo-EM Structure of β-Galactosidase. Structure (London, England : 1993)
Pisharady PK, Sotiropoulos SN, Sapiro G, et al. (2017) 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. 10433: 602-610
Pisharady PK, Sotiropoulos SN, Duarte-Carvajalino JM, et al. (2017) Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning. Neuroimage
Gunalan K, Chaturvedi A, Howell B, et al. (2017) Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example. Plos One. 12: e0176132
Carpenter KL, Sprechmann P, Calderbank R, et al. (2016) Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach. Plos One. 11: e0165524
Lyzinski V, Fishkind DE, Fiori M, et al. (2016) Graph Matching: Relax at Your Own Risk. Ieee Transactions On Pattern Analysis and Machine Intelligence. 38: 60-73
Tepper M, Sapiro G. (2016) Compressed Nonnegative Matrix Factorization Is Fast and Accurate Ieee Transactions On Signal Processing. 64: 2269-2283
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