Meet the Team
Adam Romlein
Senior R&D Engineer
Kitware New York
Clifton Park, NY
B.S. in Computer Engineering
Clarkson University
Adam Romlein is a Senior Research and Development Engineer on Kitware’s Computer Vision Team based in Clifton Park, New York. Since joining Kitware in March 2020, he has played a pivotal role in leading cutting-edge cyber-physical systems projects, specializing in systems engineering, model deployment, and software management. With extensive expertise in ROS and embedded systems, Adam has consistently bridged the gap between theory and practical field deployment, advancing the field of real-time vision systems.
Adam was the lead systems engineer on the DARPA URSA (Urban Reconnaissance through Supervised Autonomy) project from 2020 to 2023, where he successfully integrated deep learning algorithms onto Jetson Xavier platforms for real-time, edge-based tracking and activity recognition. His work involved managing a sophisticated network of over 24 cameras, combining mobile (UGV, UAV) and static (PTZ) setups, to enhance autonomous surveillance capabilities.
Currently, Adam leads the NOAA KAMERA project, a multi-camera, multi-modal system that leverages EO, IR, and UV imagery for automated aerial surveys of ice-associated mammals. The system supports real-time, deep-learning-based detection and geolocation of species like polar bears and seals in Arctic habitats.
In addition to his leadership on KAMERA, Adam is actively contributing to the Army’s Countermine initiatives, developing advanced detection and tracking solutions using embedded platforms such as Jetson Xavier and Orin for rugged, fieldable applications.
Adam graduated summa cum laude with a B.S. in Computer Engineering from Clarkson University in 2019. He has presented his work at the 2024 STRATUS UAV Conference and co-authored multiple publications, including papers in WACV, MSS and a workshop at AAAI.
Publications
- D. Davila, J. VanPelt, A. Lynch, A. Romlein, P. Webley, and M. Brown, "ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI," in Workshop on Practical Deep Learning in the Wild, 2022. [URL]
- M. Brown, K. Fieldhouse, A. Romlein, D. Davila, E. Borovikov, A. Lynch, and A. Hoogs, "Person Tracking, Re-identification, and Threat Detection by Autonomous Unmanned Systems within Complex Urban Environments," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2021.
- M. Brown, K. Fieldhouse, E. Swears, P. Tunison, A. Romlein, and A. Hoogs, "Multi-Modal Detection Fusion on a Mobile UGV for Wide-Area, Long-Range Surveillance," in 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019. [URL]
- M. Brown, K. Fieldhouse, E. Swears, P. Tunison, A. Romlein, and A. Hoogs, "Multi-Modal Detection Fusion on Mobile UGV for Squad-Level Threat Alerting," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018.
