Sarah Brockman is an R&D engineer on Kitware’s Computer Vision Team located in Clifton Park, New York. She assists with developing robust solutions for real-world problems within a variety of CV focus areas, including 3D reconstruction, object-based change detection, feature detection, and motion pattern learning, and anomaly detection, among others.
While earning her degree, Sarah held machine learning research and software engineering internship positions at the Naval Nuclear Laboratory and MIT Lincoln Laboratory.
Sarah received her master’s degree in computer science from the University of Massachusetts at Amherst (UMASS Amherst). She also received her bachelor’s degrees in computer science and mathematics from UMASS Amherst.
M.S. in computer science from the University of Massachusetts Amherst
B.S. in computer science from the University of Massachusetts Amherst
B.S. in mathematics from the University of Massachusetts Amherst
Get to Know Sarah
What made you want to become a Kitwarean? I really enjoyed my summer internship at Kitware. Everyone I met was super nice and helpful, and I was excited to work in that environment again.
What do you love most about what you do? There are practically endless opportunities to learn something new.
Share something interesting about yourself that is not on your resume. I speak some French and really enjoy learning the language in my free time. I also like reading, playing video games, hiking, and watching my favorite show, Frasier.
Sarah’s publication list is below. To see all of Kitware’s computer vision publications, please visit the Computer Vision Publications page.
- A. Chambers, A. Stringfellow, B. Luo, S. Underwood, T. Allard, I. Johnston, S. Brockman, L. Shing, A. Wollaber, and C. VanDam, "Automated Business Process Discovery from Unstructured Natural-Language Documents," in International Conference on Business Process Management, 2020. [URL]
- M. Blossom, S. Gigure, S. Brockman, A. Kobren, Y. Brun, E. Brunskill, and P. Thomas, "Offline contextual bandits with high probability fairness guarantees," Advances in neural information processing systems, vol. 32, Dec. 2019. [URL]