Kellie Corona, Ph.D.

Data Informatics Analyst

Computer Vision

Kitware New York
Clifton Park, NY

M.S. in Physics
University of North Carolina

Ph.D. in Physics
University of North Carolina

Dr. Kellie Beicker Corona is a Data Informatics Analyst in Kitware’s Computer Vision Team, where she is responsible for annotation quality and efficiency across Vision projects. She works with internal, third-party and crowdsourced annotation processes to design annotation workflows that optimize efficiency and quality for project specific data labeling.  On the IARPA Deep Intermodal Video Analytics (DIVA) program, she was responsible for the experimental design and implementation of scripted scenarios to ensure the realism and program requirements for the one hundred actor, multi-camera data collect. Also, she is a lead in the effort for annotating the resulting data using internal annotators, external annotation companies and crowdsourcing platforms. At Kitware, Dr. Corona works on developing full-pipeline annotation data management and tooling pipeline for crowdsourced annotations.  She implements metrics for performance, reliability and quality control in annotations. As a part of this work, she develops and implements new techniques and tools into the annotation workflow to improve efficiency, performance and management. She also builds curriculum for training the Kitware annotation team, providing this training and professional guidance during the annotation process.

Dr. Corona received her M.S. and Ph.D. in physics from the University of North Carolina at Chapel Hill.  Prior to joining Kitware in 2016, she worked at the Computer Integrated Systems for Microscopy and Manipulation Center which focused on the development of instrumentation, analysis techniques and visualization tools for a wide range of biological and materials projects.  Her research was aimed at the development of a novel technique for high-resolution, high-speed fluorescence imaging of mechanically deformed samples. This involved the development of analysis packages and visualization tools for synchronous examination of two-dimensional strain mapping from fluorescence images and direct mechanical measurements from atomic force spectroscopy.


  1. K. Corona, K. Osterdahl, R. Collins, and A. Hoogs, "MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021. [URL]
  2. R. Collins, K. Osterdahl, A. Shringi, K. Corona, R. Meth, and A. Hoogs, "Data, Algorithms, and Framework for Automated Analytics of Surveillance Camera Networks," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018. [URL]

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