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Meet the Team

Jason R. Parham, Ph.D.

Staff 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, Ph.D. is a staff R&D engineer on Kitware’s Computer Vision Team. For over a decade, Jason has supervised and performed research in various areas of computer vision and machine learning, including: object detection, classification, and re-identification; remote sensing; acoustic detection and classification; environmental monitoring; deep learning; unsupervised foundation models; and multi-modal LLMs. He has contributed to many projects sponsored by government entities, including: Defense Advanced Research Projects Agency (DARPA), Intelligence Advanced Research Projects Agency (IARPA), Air Force Research Laboratory (AFRL), Office of Naval Research (ONR), National Geospatial-Intelligence Agency (NGA), National Oceanic and Atmospheric Administration (NOAA), Department of Energy (DOE), Oregon Department of Fish and Wildlife (ODFW), and other Department of Defense and intelligence agencies. Since re-joining Kitware in 2022, Jason has co-led or contributed substantially to grants resulting in more than $6M in new funding.  Jason is the principal investigator (PI) on a multi-year DARPA Phase 2 SBIR and has authored over two dozen peer-reviewed papers and technical reports covering topics from object detection and remote sensing to wildlife conservation.

Jason has worked extensively with the Sensors Directorate at the Air Force Research Laboratory (AFRL) on the AFRL Global Surveillance program, serving as a core contributor to the Visual Global Intelligence and Analytics Toolkit (VIGILANT). His work on VIGILANT has led to the creation of the V1 algorithm, an optimized, explainable, and modularized object detection pipeline for large-area imagery and rare objects.

Jason is actively involved in the computer vision and machine learning communities, co-leading Kitware’s internal Weekly Research Roundup reading group to discuss the latest machine learning developments, review recent papers, and provide internal updates from the computer vision team. He has served as a reviewer for premier conferences and journals in computer vision and ecology, including: International Conference on Computer Vision (ICCV), Conference on Computer Vision and Pattern Recognition (CVPR), European Conference on Computer Vision (ECCV), IEEE/CVF Winter Conference (WACV), AAAI Conference on Artificial Intelligence (AAAI), Conference on Neural Information Processing Systems (NeurIPS), International Conference on Learning Representations (ICLR), International Geoscience and Remote Sensing Symposium (IGARSS), Mammalian Biology, Ecosphere, and the CV4Ecology program. Jason also co-organized a 2020 WACV computer vision workshop on deep learning for animal re-identification.

Prior to joining Kitware, Jason was a senior computer vision research engineer at Wild Me, a non-profit organization that built open source software and AI for conservation research. He led the Wildbook IA project, which offered a comprehensive suite of detection, classification, segmentation, and animal re-identification algorithms and has been deployed worldwide for endangered wildlife conservation projects.  Jason was also a visiting instructor at Caltech during the inaugural CV4Ecology summer workshop in the summer of 2022.

Jason received his Ph.D. and master’s degree in computer science from Rensselaer Polytechnic Institute in 2021 and 2015, respectively. In 2012, he received his bachelor’s degree in computer science / mathematics from Pepperdine University. Furthermore, he participated in Kitware’s Open Source Software Technology Program, which provided him with internship opportunities and tuition reimbursement benefits while earning his Ph.D.

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

Publications

  1. K. Tombak, L. Nonnamaker, J. Parham, C. Stewart, R. Warungu, and D. Rubenstein, "Darwin's hostile forces shape social scaling in equids: a comparison of group size dynamics in Grevy's and plains zebras," Animal Behaviour, vol. 224, pp. 123158, Jun. 2025. [URL]
  2. A. Sundaresan, J. Parham, J. Crall, R. Warungu, T. Muthami, J. Miliko, M. Mwangi, J. Holmberg, T. Berger‐Wolf, D. Rubenstein, C. Stewart, and S. Beery, "Adapting the Re‐ID Challenge for Static Sensors," IET Computer Vision, vol. 19, no. 1, pp. e70027, Jan. 2025. [URL]
  3. 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, 2023.
  4. E. Cole, S. Stathatos, B. Lütjens, T. Sharma, J. Kay, J. Parham, B. Kellenberger, and S. Beery, "Teaching Computer Vision for Ecology," 2023. [URL]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. C. Stewart, J. Parham, J. Holmberg, and T. Berger-Wolf, "The Animal ID Problem: Continual Curation," 2021. [URL]
  10. 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]
  11. 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]
  12. J. Parham, "Animal detection for photographic censusing," Ph.D. dissertation, Rensselaer Polytechnic Institute, 2021.
  13. J. Holmberg, J. Parham, and A. Blount, "Feasibility Analysis: Using Artificial Intelligence to Match Photographed Lateral Ridges of Gray Whales," US Department of the Interior, Bureau of Ocean Energy Management, Pacific OCS Region, vol. 59, no. OCS Study BOEM, pp. 29, 2021.
  14. D. Blount et al., "Flukebook: Continuing growth and technical advancement for cetacean photo identification and data archiving, including automated fin, fluke, and body matching," International Whaling Commission, Technical Report, 2020.
  15. 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, 2020. [URL]
  16. 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]
  17. D. Rubenstein et al., "The State of Kenya’s Grevy’s Zebras and Reticulated Giraffes: Results of the Great Grevy’s Rally 2018," 2018.
  18. T. Berger-Wolf, D. Rubenstein, C. Stewart, J. Holmberg, J. Parham, S. Menon, J. Crall, J. Oast, E. Kiciman, and L. Joppa, "Wildbook: Crowdsourcing, computer vision, and data science for conservation," 2017. [URL]
  19. 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]
  20. 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]
  21. 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.
  22. J. Parham and C. Stewart, "Detecting plains and Grevy's Zebras in the realworld," in IEEE Winter Applications of Computer Vision Workshops, 2016. [URL]
  23. 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]
  24. 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]
  25. 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]
  26. T. Berger-Wolf, D. Rubenstein, C. Stewart, J. Holmberg, J. Parham, and J. Crall, "IBEIS: Image-based ecological information system: From pixels to science and conservation," Bloomberg Data for Good Exchange Conference, 2015.
  27. J. Parham, "Photographic censusing of zebra and giraffe in the Nairobi National Park," M.S. thesis, Department of Computer Science, Rensselaer Polytechnic Institute, 2015.
  28. J. Crall, J. Parham, and C. Stewart, "HotSpotter User Guide," Department of Computer Science, Renssealer Polytechnic Institute, 2013.

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