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Christopher Kanan, Ph.D.

Affiliations: 
2004-2006 Computer Science University of Southern California, Los Angeles, CA, United States 
 2007-2013 Computer Science and Engineering University of California, San Diego, La Jolla, CA 
 2013-2015 Jet Propulsion Laboratory California Institute of Technology, Pasadena, CA 
 2015-2022 Chester F. Carlson Center for Imaging Science Rochester Institute of Technology, Rochester, NY, United States 
 2022- Computer Science University of Rochester, Rochester, NY 
Area:
Deep Learning, Artificial Intelligence, Computer Vision, Cognitive Science
Website:
http://chriskanan.com
Google:
"Christopher Kanan"
Cross-listing: Neurotree

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Parents

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Michael A. Arbib grad student 2005-2007 USC (Neurotree)
Garrison Cottrell grad student 2007-2013 UCSD (Neurotree)
 (In defense of brain-inspired cognitive models.)

Children

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Adam Casson research assistant 2016-2017 Rochester Institute of Technology (Neurotree)
Rodney Sanchez research assistant 2016-2019 Rochester Institute of Technology (Neurotree)
Robik Singh Shrestha grad student 2017- Rochester Institute of Technology (Neurotree)
Jhair Gallardo grad student 2019- Rochester Institute of Technology (Neurotree)
Yousuf Harun grad student 2020- Rochester Institute of Technology (Neurotree)
Shikhar Srivastava grad student 2023- Rochester (MathTree)
Ronald Kemker grad student 2015-2018 Rochester Institute of Technology (Neurotree)
Kushal Kafle grad student 2015-2020 Rochester Institute of Technology (Neurotree)
Ryne Roady grad student 2017-2020 Rochester Institute of Technology (Neurotree)
Zhongchao Qian grad student 2018-2020 Rochester Institute of Technology (Neurotree)
Tyler L Hayes grad student 2016-2022 Rochester Institute of Technology (Neurotree)
Manoj Acharya grad student 2017-2022 Rochester Institute of Technology (Neurotree)
Usman Mahmood grad student 2017-2022 Rochester Institute of Technology (Neurotree)

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Publications

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Raciti P, Sue J, Retamero JA, et al. (2022) Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection. Archives of Pathology & Laboratory Medicine
Mahmood U, Bates DDB, Erdi YE, et al. (2022) Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans. Diagnostics (Basel, Switzerland). 12
Mahmood U, Shrestha R, Bates DDB, et al. (2021) Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems. Frontiers in Digital Health. 3: 671015
Hayes TL, Krishnan GP, Bazhenov M, et al. (2021) Replay in Deep Learning: Current Approaches and Missing Biological Elements. Neural Computation. 1-44
Mahmood U, Apte A, Kanan C, et al. (2021) Quality control of radiomic features using 3D-printed CT phantoms. Journal of Medical Imaging (Bellingham, Wash.). 8: 033505
da Silva LM, Pereira EM, Salles PG, et al. (2021) Independent real-world application of a clinical-grade automated prostate cancer detection system. The Journal of Pathology
Roady R, Hayes TL, Kemker R, et al. (2020) Are open set classification methods effective on large-scale datasets? Plos One. 15: e0238302
Kafle K, Shrestha R, Kanan C. (2019) Challenges and Prospects in Vision and Language Research. Frontiers in Artificial Intelligence. 2: 28
Parisi GI, Kemker R, Part JL, et al. (2019) Continual lifelong learning with neural networks: A review. Neural Networks : the Official Journal of the International Neural Network Society. 113: 54-71
Kafle K, Shrestha R, Kanan C. (2019) Challenges and Prospects in Vision and Language Research Frontiers in Artificial Intelligence. 2
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