Meet the Team
Jason R. Parham, Ph.D.
Senior R&D Engineer
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 R. Parham is a senior R&D engineer on Kitware’s Computer Vision Team. Jason’s research focuses on machine learning for image-based object detection, object classification, object-based change detection capabilities on aerial and satellite imagery, patch-based search, acoustic detection and classification, and unsupervised foundation models and multi-modal LLMs.
Jason has worked extensively with the Sensors Directorate at the Air Force Research Laboratory (AFRL) on the AFRL Global Surveillance program, for which he has been 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 has also led and contributed to projects for the Defense Advanced Research Projects Agency (DARPA), Intelligence Advanced Research Projects Activity (IARPA), Air Force Research Laboratory (AFRL), National Geospatial-Intelligence Agency (NGA), National Oceanic and Atmospheric Administration (NOAA), Department of Energy (DOE), and other Department of Defense and intelligence agencies.
Since re-joining Kitware in 2022, Jason has contributed substantially to winning proposals resulting in more than $4M in new funding. He also co-leads the internal Weekly Research Roundup reading group to discuss the latest machine learning news, review recent papers, and update the vision team on internal programs.
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 novel research and productizing machine learning algorithms for conservation tasks. Jason led 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).
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 and 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
Publications
- 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.
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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.
- 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]
- 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]
- 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]
- 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]