EU

Meet the Team

Connor Greenwell, Ph.D.

Senior R&D Engineer

Computer Vision

Kitware Remote

Ph.D. in Computer Science
University of Kentucky

B.S. in Computer Science and Mathematics
University of Kentucky

Connor Greenwell

Connor Greenwell, Ph.D. is a senior research scientist working remotely on Kitware’s Computer Vision Team. He helps develop solutions in the areas of object-based detection, feature detection, motion pattern learning, and anomaly detection, with an emphasis on remote sensing imagery. He also manages a team of engineers and scientists who are working on a system for understanding human population dynamics through massive high-fidelity simulations.

Prior to joining Kitware, Connor was a research assistant at the University of Kentucky from 2014-2022. He also had multiple internships during that time at the University of North Carolina, Charlotte, Oak Ridge National Laboratory, and Kitware. During his internship at Kitware, he was a member of the IARPA SMART team. After assuming his current role, he rejoined the SMART team and continued developing our geowatch system, led the effort for detecting short-duration transient events, and contributed to the Phase 3 extension of the program. Additionally, Connor stepped into the role of interim Chief Scientist on the IARPA HAYSTAC effort, successfully overseeing the critical first round of software and data deliveries and coordinating an in-person site visit.

Connor received his Ph.D. in computer science at the University of Kentucky in 2022. He also received his bachelor’s degree in computer science and mathematics from Kentucky in 2016.

Publications

  1. 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 (WACV), 2024. [URL]
  2. G. Liang, C. Greenwell, Y. Zhang, X. Xing, X. Wang, R. Kavuluru, and N. Jacobs, "Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 4, pp. 1640-1649, Apr. 2022. [URL]
  3. B. Brodie, S. Khanal, M. Rafique, C. Greenwell, and N. Jacobs, "Hierarchical Probabilistic Embeddings for Multi-View Image Classification," in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021. [URL]
  4. S. Workman, M. Rafique, H. Blanton, C. Greenwell, and N. Jacobs, "Single Image Cloud Detection via Multi-Image Fusion," in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020. [URL]
  5. H. Blanton, C. Greenwell, S. Workman, and N. Jacobs, "Extending Absolute Pose Regression to Multiple Scenes," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020. [URL]
  6. C. Greenwell, S. Workman, and N. Jacobs, "Implicit Land Use Mapping Using Social Media Imagery," in 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2019. [URL]
  7. T. Salem, C. Greenwell, H. Blanton, and N. Jacobs, "Learning to Map Nearly Anything," in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019. [URL]
  8. C. Greenwell, S. Workman, and N. Jacobs, "What Goes Where: Predicting Object Distributions from Above," in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018. [URL]
  9. R. Baltenberger, M. Zhai, C. Greenwell, S. Workman, and N. Jacobs, "A fast method for estimating transient scene attributes," in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. [URL]
  10. S. Workman, C. Greenwell, M. Zhai, R. Baltenberger, and N. Jacobs, "DEEPFOCAL: A method for direct focal length estimation," in 2015 IEEE International Conference on Image Processing (ICIP), 2015. [URL]
  11. M. Islam, C. Greenwell, R. Souvenir, and N. Jacobs, "Large-scale geo-facial image analysis," EURASIP Journal on Image and Video Processing, vol. 2015, no. 1, pp. 17, Dec. 2015. [URL]
  12. C. Greenwell, S. Spurlock, R. Souvenir, and N. Jacobs, "GeoFaceExplorer: exploring the geo-dependence of facial attributes," in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, 2014. [URL]

Bibliography generated 2024-01-02-16:00:04 (6942)