Meet the Team
Connor Hashemi, Ph.D.
Senior R&D Engineer
Kitware Minneapolis
Minneapolis, MN
Ph.D. in Electrical Engineering
University of Minnesota
M.S. in Electrical Engineering
University of Minnesota
B.S. in Electrical Engineering
University of Minnesota
Connor Hashemi is a Senior R&D Engineer working at KMW on Kitware’s Computer Vision Team. He specializes in computational imaging, characterizing fundamental performance limits and developing physics-based models and algorithms for computer vision and imaging applications.
Throughout his Ph.D, his primary focus was on the DARPA-funded REVEAL project, where he developed methods and algorithms for passive non-line-of-sight imaging using scattered light. His work combined multispectral and thermal imaging, linear unmixing, convex optimization, physics-based modeling, and light fields to address inverse imaging problems under low-signal conditions.
At Kitware, Connor has contributed to multiple projects spanning event-based imaging, sensor modeling, synthetic data generation, and underwater acoustics. He has published several papers on event-based imaging, including his work on the NEST (Neuromorphic Event-based Star Tracker) project, and developed sensor-based modeling and synthetic data generation pipelines through the CDAO NRTK program. Recently, his work includes dataset creation and model training for underwater acoustic target recognition (UATR) of ships through the DARPA MARINA program, as well as creating implicit neural representations for low-signal C-ray imaging through the DARPA XENA program.
Connor earned his B.S., master’s degree, and Ph.D. in Electrical Engineering from the University of Minnesota. He serves as a reviewer for conferences in computer vision and computational imaging, including Optica, CVPR, AAAI, ICCV, ECCV, IEEE TCI, ACM TOG, NeurIPS, and BMVC.
Awards
Best Paper Award for “Centroiding Point-Objects with Event Cameras” at the 2025 International Conference on Computational Photography.
Publications
- C. Hashemi, B. Ravichandran, C. Anderson, S. Govil, B. RichardWebster, B. Hu, and K. Fieldhouse, "Improving semantic coverage in AI test and evaluation through generative augmentation," in Assurance and Security for AI-enabled Systems 2026, 2026. [URL]
- C. Anderson, C. Hashemi, B. Ravichandran, B. RichardWebster, B. Hu, and K. Fieldhouse, "Understanding Sensor-based Robustness of Object Detection Models for Overhead Imagery," in MSS Parallel Conference of the Military Sensing Symposia (MSS) Special Committees on Battlefield Survivability and Discrimination, Detectors, Materials, and Passive Sensors, 2026.
- A. Reed, C. Hashemi, D. Melamed, N. Menon, K. Hirakawa, and S. McCloskey, "EBS-EKF: Accurate and High Frequency Event-Based Star Tracking," in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. [URL]
- C. Hashemi, D. Melamed, A. Reed, N. Menon, K. Hirakawa, and S. McCloskey, "Centroiding Point-Objects With Event Cameras," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-10, 2025. [URL]
- C. Hashemi, R. Avelar, and J. Leger, "Clutter rejection in passive non-line-of-sight imaging via blind multispectral unmixing," Optics Express, vol. 32, no. 2, pp. 2132, Jan. 2024. [URL]
- C. Hashemi, R. Avelar, and J. Leger, "Isolating Signals in Passive Non-Line-of-Sight Imaging Using Spectral Content," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 9, pp. 7328-7339, Sep. 2023. [URL]
- C. Hashemi, T. Sasaki, and J. Leger, "Parallax-Driven Denoising of Passive Non-Line-of-Sight Thermal Imagery," in IEEE International Conference on Computational Photography, 2023. [URL]
- T. Sasaki, C. Hashemi, and J. Leger, "Passive 3D location estimation of non-line-of-sight objects from a scattered thermal infrared light field," Optics Express, vol. 29, no. 26, pp. 43642, Dec. 2021. [URL]
- D. Lin, C. Hashemi, and J. Leger, "Passive non-line-of-sight imaging using plenoptic information," Journal of the Optical Society of America A, vol. 37, no. 4, pp. 540, Apr. 2020. [URL]