Jason R. Parham, Ph.D.

Senior R&D Engineer

Computer Vision

Kitware Remote

Ph.D. in Computer Science
Rensselaer Polytechnic Institute

M.S. in Computer Science
Rensselaer Polytechnic Institute

B.S. in Computer Science/Mathematics
Pepperdine University

Jason Parham

Jason R. Parham is a senior R&D engineer working remotely (from Oregon) on Kitware’s Computer Vision Team. He applies modern deep learning techniques to Kitware’s computer vision projects, deploying scalable systems and other DevOps enhancements. His recent work has focused on object detection in satellite imagery across multiple government programs. Jason works primarily with the Sensors Directorate at the Air Force Research Laboratory (AFRL) in Dayton on the AFRL Global Surveillance program, for which Kitware has been developing The Visual Global Intelligence and Analytics Toolkit (VIGILANT). Kitware’s relevant projects with AFRL and NGA have delivered object detection, patch-based search, and object-based change detection capabilities on aerial and satellite imagery. Lastly, Jason pitched and won an SBIR for the Department of Energy on audio-baed bat detection at wind farms.

Jason participated in Kitware’s Open Source Software Technology Program, which allowed him to intern at Kitware and receive tuition reimbursement benefits while earning his Ph.D.

Prior to joining Kitware, Jason was a senior computer vision research engineer at Wild Me, a non-profit that builds open source software and AI for conservation research. He was responsible for writing papers, productizing the latest machine learning algorithms, and deploying open source software on hand-built computer hardware. Jason was also part of the Wildbook IA project for wildlife conservation, which has a machine learning API backend and a comprehensive suite of detection, classification, and segmentation algorithms to prepare images of animals for a re-identification pipeline. This system has been used for wildlife conservation projects worldwide (based on the Wildbook project) and is accessible as a pre-configured containerized instance with GPU acceleration.

Jason received his Ph.D. and master’s degree in computer science from Rensselaer Polytechnic Institute in 2021 and 2015, respectively. In 2012 received his bachelor’s degree in computer science/mathematics from Pepperdine University.

Invited Talks & Media

  • Presenter, “Automating Wildlife Conservation for Cetaceans,” NeurIPS Expo, 2020

  • Invited Speaker, “Wildbook – Photographic Censusing of Wildlife,” Oregon State University Department of EECS Colloquium, 2019

  • Invited Speaker, “Fighting Extinction with AI – A Digital Transformation for Wildlife Conservation,” Portland CIO Forum, 2018

  • Invited Speaker, “Computer Vision for Social Good – Animal Censusing in the Wild,” Pepperdine University’s Natural Science Department Lecture Series, 2017

  • Presenter, “Unsupervised Deep Learning with KWCNN,” Kitware Summer 2016 Tech Talk Lunch Series, 2016

  • Presenter, “Introducing the KitWare Convolutional Neural Network *KWCNN) Python Module with Live Demos,” Kitware Summer 2015 Tech Talk Lunch Series, 2015

Professional Associations & Service

  • Member, The Computer Vision Foundation, 2017-present

  • Member, The Association for the Advancement of Artificial Intelligence, 2017-2019

  • Workshop co-organizer, on animal re-ID, WACV, 2020


  1. J. Parham, D. Joy, P. Gurram, S. Brockman, R. Blue, A. Hoogs, B. Minnehan, S. Thomas, C. Liberatore, R. Profeta, and T. Rovito, "From Commercial Satellites to National Defense: A Review of VIGILANT for Object Detection, Classification, Mensuration, and Patch-based Search in Satellite Imagery," in Proceedings of the National Security Sensor and Data Fusion Committee (NSSDF), 2023.
  2. R. Tyson Moore, K. Urian, J. Allen, C. Cush, J. Parham, D. Blount, J. Holmberg, J. Thompson, and R. Wells, "Corrigendum: Rise of the machines: Best practices and experimental evaluation of computer-assisted dorsal fin image matching systems for bottlenose dolphins," Frontiers in Marine Science, vol. 9, pp. 998145, Sep. 2022. [URL]
  3. C. Khan, D. Blount, J. Parham, J. Holmberg, P. Hamilton, C. Charlton, F. Christiansen, D. Johnston, W. Rayment, S. Dawson, E. Vermeulen, V. Rowntree, K. Groch, J. Levenson, and R. Bogucki, "Artificial intelligence for right whale photo identification: from data science competition to worldwide collaboration," Mammalian Biology, Jun. 2022. [URL]
  4. D. Blount, E. Bohnett, J. Holmberg, J. Parham, S. Faryabi, Ö. Johansson, L. An, B. Ahmad, W. Khan, and S. Ostrowski, "Comparison of Two Individual Identification Algorithms for Snow Leopards after Automated Detection," Bioinformatics, Jan. 2022. [URL]
  5. D. Blount, S. Gero, J. Van Oast, J. Parham, C. Kingen, B. Scheiner, B. Stere, M. Fisher, G. Minton, C. Khan, V. Dulau, J. Thompson, O. Moskvyak, T. Berger-Wolf, C. Stewart, J. Holmberg, and J. Levenson, "Flukebook: an open-source AI platform for cetacean photo identification," Mammalian Biology, pp. 1-19, Apr. 2022. [URL]
  6. S. Dunbar, E. Anger, J. Parham, C. Kingen, M. Wright, C. Hayes, S. Safi, J. Holmberg, L. Salinas, and D. Baumbach, "HotSpotter: Using a computer-driven photo-id application to identify sea turtles," Journal of Experimental Marine Biology and Ecology, vol. 535, pp. 151490, Feb. 2021. [URL]
  7. D. Blount, J. Holmberg, J. Parham, S. Gero, J. Gordon, and J. Levenson, "Comparison of Three Individual Identification Algorithms for Sperm Whales (Physeter macrocephalus) after Automated Detection," Ecology, Dec. 2021. [URL]
  8. H. Weideman, C. Stewart, J. Parham, J. Holmberg, K. Flynn, J. Calambokidis, D. Paul, A. Bedetti, M. Henley, F. Pope, and J. Lepirei, "Extracting identifying contours for African elephants and humpback whales using a learned appearance model," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020. [URL]
  9. J. Parham, C. Stewart, J. Crall, D. Rubenstein, J. Holmberg, and T. Berger-Wolf, "An animal detection pipeline for identification," in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2018. [URL]
  10. S. Han, A. Fafard, J. Kerekes, M. Gartley, E. Ientilucci, A. Savakis, C. Law, J. Parhan, M. Turek, K. Fieldhouse, and T. Rovito, "Efficient generation of image chips for training deep learning algorithms," in SPIE Defense + Security, 2017. [URL]
  11. J. Parham, J. Crall, C. Stewart, T. Berger-Wolf, and D. Rubenstein, "Animal population censusing at scale with citizen science and photographic identification," in AAAI Spring Symposium Series, 2017. [URL]
  12. C. Law, J. Parham, M. Dawkins, P. Tunison, D. Stoup, R. Blue, K. Fieldhouse, M. Turek, A. Hoogs, S. Han, A. Farafard, J. Kerekes, E. Lentilucci, M. Gartley, T. Savakis, T. Rovito, S. Thomas, and C. Stansifer, "Deep learning for object detection and object-based change detection in satellite imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2017.
  13. J. Parham and C. Stewart, "Detecting plains and Grevy's Zebras in the realworld," in 2016 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2016. [URL]
  14. T. Berger-Wolf, J. Crall, J. Holberg, J. Parham, C. Stewart, B. Mackey, P. Kahumbu, and D. Rubenstein, "The Great Grevy’s Rally: The Need, Methods, Findings, Implications and Next Steps," Grevy's Zebra Trust, Aug. 2016. [URL]
  15. S. Menon, T. Berger-Wolf, E. Kiciman, L. Joppa, C. Stewart, J. Parham, J. Crall, J. Holmberg, and J. Van Oast, "Animal Population Estimation Using Flickr Images," in Proceedings of ACM Conference, 2016. [URL]
  16. D. Rubenstein, C. Stewart, T. Berger-Wolf, J. Parham, J. Crall, C. Machogu, P. Kahumbu, and N. Maingi, "The Great Zebra and Giraffe Count: The Power and Rewards of Citizen Science," Kenya Wildlife Service, Jul. 2015. [URL]

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