Meet the Team
Jon Crall, Ph.D.
Staff R&D Engineer
Kitware New York
Clifton Park, NY
Ph.D. in Computer Vision
Rensselaer Polytechnic Institute
B.S. in Computer Science
State University of New York Polytechnic Institute
Jon Crall, Ph.D., is a staff R&D engineer on Kitware’s Computer Vision Team. He is a machine learning (ML) and Python expert with significant experience in computer vision, geospatial AI, and scientific software. He applies ML to real-world problems in areas such as classification, detection, and segmentation.
He received his Ph.D. in computer science from Rensselaer Polytechnic Institute in 2017. There, he focused on the problem of instance recognition of individual animals and served as the lead image-analysis developer for the Image Based Ecological Information System (IBEIS). That work contributed to wildlife-identification efforts including Wildbook and was adopted by multiple research teams around the world. He received his bachelor’s degree in computer science with a minor in math from the State University of New York Polytechnic Institute in 2010.
Jon has a long history with Kitware. He first joined as an intern on the Computer Vision Team in 2007, left in 2010 to focus on grad school, and then returned full-time in 2017. Since returning he has helped win proposals that launched major research efforts, worked on government and environmental problems supported by agencies including the Air Force Research Laboratory (AFRL), Defense Advanced Research Projects Agency (DARPA), Intelligence Advanced Research Projects Activity (IARPA), National Geospatial-Intelligence Agency (NGA), and the National Oceanic and Atmospheric Administration (NOAA). Notably, he played a key technical role in multimillion-dollar programs including IARPA’s SMART program and DARPA’s AI Quantified (AIQ) initiative.
Through this work Jon has developed and contributed to software packages reusable across projects including VIAME, GeoWATCH, and KWCOCO. He also maintains dozens of smaller Python libraries used in computer-vision and research workflows. He is part of Kitware’s internal consulting group for AI, which helps other teams navigate AI-related challenges.
In addition to his research, Jon is an active open-source developer. His projects include Ubelt, Xdoctest, and KWCOCO, and he has maintained Line Profiler since late 2019. Beyond his own projects, he has contributed to many open-source efforts, including NetworkX, PyTorch, and CPython. He also leads the ScatSpotter project, which studies the surprisingly hard problem of detecting dog poop in cluttered outdoor settings with mobile phones. Some of Jon’s notable achievements include winning 10th place in TopCoder Urban 3D Challenge (sponsored by USSOCOM and NASA) and 3rd place in the 2021 PyTorch Annual Hackathon for TorchLiberator.
Invited Talks & Media
“Developing With Doctests,” presented talk, Python Conference (PyCon), 2020
Professional Associations & Service
Reviewer, Winter Conference on Applications of Computer Vision (WACV), 2026
Reviewer, Neural Information Processing Systems (NeurIPS), 2025
Reviewer, Association for the Advancement of Artificial Intelligence (AAAI), 2025
Reviewer, Winter Conference on Applications of Computer Vision (WACV), 2025
Reviewer, International Conference on Machine Learning (ICML), 2024
Reviewer, Neural Information Processing Systems (NeurIPS), 2024
Reviewer, International Conference on Learning Representations (ICLR), 2023
Reviewer, Neural Information Processing Systems (NeurIPS), 2023
Reviewer, Neural Information Processing Systems (NeurIPS), 2022
Reviewer, Association for the Advancement of Artificial Intelligence (AAAI), 2021
Publications
- 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]
- C. Greenwell, J. Crall, M. Purri, N. Jacobs, A. Hadzic, S. Workman, and M. Leotta, "WATCH: Wide-Area Terrestrial Change Hypercube," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024. [URL]
- A. Mankowski, P. Gurram, J. Crall, S. McCloskey, and A. Hoogs, "Transformers for Small Object Detection and Tracking in OPIR Imagery," in Proceedings of the National Security Sensor and Data Fusion Committee, 2023.
- A. Hoogs, M. Dawkins, B. Richards, G. Cutter, D. Hart, M. Clarke, W. Michaels, J. Crall, L. Sherrill, N. Siekierski, M. Woehlke, and K. Edwards, "An Open-Source System for Do-It-Yourself AI in the Marine Environment," in AGU Ocean Sciences Meeting, 2020. [URL]
- C. Funk, J. Crall, W. Hicks, C. Law, P. Tunison, R. Blue, A. Hoogs, T. Rovito, and A. Maltenfort, "WEFT Feature Detection and Mensuration for Airplane Classification in Satellite Imagery," in MSS National Symposium on Sensor and Data Fusion, 2019.
- J. Crall, J. Becker, P. Tunison, M. Dawkins, A. Basharat, R. Blue, M. Turek, and A. Hoogs, "Deep Learning for Small Object Detection in Satellite Infrared Imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018.
- 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]
- J. Parham, C. Stewart, J. Crall, D. Rubenstein, J. Holmberg, and T. Berger-Wolf, "Focusing animal identification with annotations of interest," in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2018.
- 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]
- J. Crall, "Identifying Individual Animals using Ranking, Verification, and Connectivity," Ph.D. dissertation, Rensselaer Polytechnic Institute, 2017.
- S. Dunbar, D. Baumbach, M. Wright, C. Hayes, J. Holmberg, J. Crall, and C. Stewart, "HotSpotter: less manipulating, more learning, and better vision for turtle photo identification," in Annual Symposium on Sea Turtle Biology and Conservation, 2017.
- T. Morrison, D. Keinath, W. Estes-Zumpf, J. Crall, and C. Stewart, "Individual Identification of the Endangered Wyoming Toad Anaxyrus baxteri and Implications for Monitoring Species Recovery," Journal of Herpetology, vol. 50, no. 1, pp. 44-49, Mar. 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]
- 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.
- J. Crall, C. Stewart, T. Berger-Wolf, D. Rubenstein, and S. Sundaresan, "HotSpotter–Patterned species instance recognition," in Proceedings of the IEEE Workshop on Applications of Computer Vision, 2013. [URL]
