Adam Romlein

R&D Engineer

Computer Vision

Kitware New York
Clifton Park, NY

B.S. in Computer Engineering
Clarkson University

Adam Romlein is an R&D engineer on Kitware’s Computer Vision Team located in Clifton Park, New York. He is heavily involved in Kitware’s cyber-physical systems projects, including systems engineering, algorithm applications, and software management. He is also responsible for managing and mentoring interns on these projects. Adam has extensive ROS experience and works closely on full-stack robotics.

As a co-op at Kitware in 2018, and returning as an intern in 2019, Adam was involved in both the Defense Advanced Research Projects Agency (DARPA) Squad-X and URSA (Urban Reconnaissance through Supervised Autonomy) programs, both programs involved in-person detection and tracking in urban environments. Since joining Kitware full-time, he has continued his work on DARPA URSA as the main systems engineer.

Adam was also involved in DARPA Angler, an underwater autonomy program. He was the remotely operated underwater vehicle (ROV) operator and was responsible for the hardware and control portions of the project.

Outside of DARPA projects, Adam has taken on the role of systems engineer and operator for the National Oceanic and Atmospheric Administration (NOAA) Kitware Image Acquisition ManagER and Archiver (KAMERA) project. KAMERA is a system for curated image collection and real-time, deep-learning-based animal detection to support automated aerial surveys of ice-mammal populations.

Adam received his bachelor’s degree in computer engineering from Clarkson University in 2019.


  1. 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]
  2. 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]
  3. 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.

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