Matt Brown, Ph.D.

Principal Engineer

Matt Brown, Ph.D., is a principal engineer on Kitware’s Computer Vision Team located in Carrboro, North Carolina. He has over 12 years of experience developing advanced imaging systems and image-exploitation algorithms. His expertise spans from the fundamental physics of imaging to the applied aspects of designing and integrating hardware and software to solve challenging problems. At Kitware, he has made key contributions to various cyber-physical projects. 

Matt was the lead algorithm developer on the Defense Advanced Research Projects Agency (DARPA) Squad X program, where he wrote software for camera modeling and calibration, multi-modal detection fusion, and person tracking. As the chief scientist on the DARPA URSA program, he established approaches and developed algorithms deployed on a heterogenous video surveillance network. Using these algorithms, the network was able to detect, track, and re-identify entities within a complex urban environment while assessing their activities and relationships.

In addition to his DARPA-funded projects, Matt has led several projects funded by the National Oceanic and Atmospheric Administration (NOAA). These projects included developing autonomous imaging systems for monitoring animal populations and environmental conditions from both manned and unmanned aircraft. Supporting the Marine Mammal Lab of NOAA’s Alaska Fisheries Science Center, Matt developed a system for curated image collection and real-time, deep-learning-based animal detection to support automated aerial surveys of ice-mammal populations. He has also led related efforts miniaturizing this system to run on small unmanned aircraft to produce real-time, deep-learning-based land-cover maps.

Prior to joining Kitware, Matt worked at Logos Technologies, where he developed Wide Area Motion Imagery (WAMI) sensor systems (Kestrel, Simera, Serenity, Redkite) for civilian and military surveillance applications. His contributions included optimizing the design and calibration of complex, multi-camera, optomechanical sensor systems, as well as prototyping the associated control and image processing software. At Logos, Matt was the principal investigator in the development of real-time camera-pose estimation and georegistered EO–IR video rendering algorithms deployed with the sensor systems. He also developed algorithms to demonstrate automated, near-real-time target detection from hyperspectral imagery.

Matt received his Ph.D. in mechanical and aerospace engineering from Princeton University in 2011. His doctoral dissertation, supervised by Professor Craig Arnold, explored a novel, laser-actuated printing process. This work involved time-resolved imaging experiments coupled with image processing and computational modeling of complex fluid-structure interactions. In 2007, He also received his master’s degree in mechanical and aerospace engineering from Princeton. Matt received his bachelor’s degree in mechanical and aerospace engineering from Rutgers University in 2006. He graduated summa cum laude.


Ph.D. in mechanical and aerospace engineering from Princeton University, 2011

M.S. in mechanical and aerospace engineering from Princeton University, 2007 

B.S. in mechanical and aerospace engineering, summa cum laude, from Rutgers University, 2006

Get to Know Matt

What is your favorite thing about working at Kitware? The collaborative and innovative environment supported by colleagues with exceptional breadth and depth of expertise.


Matt’s publication list is below. To see all of Kitware’s computer vision publications, please visit the Computer Vision Publications page.

  1. 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]
  2. 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.
  3. M. Brown, C. Brasz, Y. Ventikos, and C. Arnold, "Impulsively actuated jets from thin liquid films for high-resolution printing applications," Journal of Fluid Mechanics, vol. 709, pp. 341-370, Oct. 2012. [URL]
  4. M. Brown, E. Glaser, S. Grassinger, A. Slone, and M. Salvador, "Development of an efficient automated hyperspectral processing system using embedded computing," in SPIE Defense, Security, and Sensing, 2012. [URL]
  5. N. Kattamis, M. Brown, and C. Arnold, "Finite element analysis of blister formation in laser-induced forward transfer," Journal of Materials Research, vol. 26, no. 18, pp. 2438-2449, Sep. 2011. [URL]
  6. M. Brown, N. Kattamis, and C. Arnold, "Time-resolved dynamics of laser-induced micro-jets from thin liquid films," Microfluidics and Nanofluidics, vol. 11, no. 2, pp. 199-207, Aug. 2011. [URL]
  7. M. Brown, N. Kattamis, and C. Arnold, "Time-resolved study of polyimide absorption layers for blister-actuated laser-induced forward transfer," Journal of Applied Physics, vol. 107, no. 8, pp. 083103, Apr. 2010. [URL]
  8. M. Brown and C. Arnold, "Fundamentals of Laser-Material Interaction and Application to Multiscale Surface Modification," in Laser Precision Microfabrication. Springer Berlin Heidelberg, 2010, pp. 91-120. [URL]
  9. M. Brown, J. Shan, C. Lin, and F. Zimmermann, "Electrical polarizability of carbon nanotubes in liquid suspension," Applied Physics Letters, vol. 90, no. 20, pp. 203108, May 2007. [URL]

Bibliography generated 2019-09-23-18:10:06 (3149)