EU

Meet the Team

Joseph VanPelt

R&D Engineer

Computer Vision

Kitware North Carolina
Carrboro, NC

B.S. in Computer Science
Herzing University

Joseph VanPelt is a senior R&D engineer on Kitware’s Computer Vision Team located in Carrboro, North Carolina. He works closely with the cyber-physical systems team on artificial intelligence and machine learning development and systems integrations.

Prior to joining Kitware, Joseph worked at Hexagon Manufacturing Intelligence. He was involved in custom software development and product management. He worked on a variety of applications performing robotic integration, general automation, and systems integrations. He also commonized bespoke software solutions under a single platform. One of his major accomplishments at Hexagon was integrating Cobots into a standard solution for automating complex metrology operations. He also assisted with the development of a new cloud-based IIoT platform.

Joseph earned his bachelor’s degree in computer science, with a concentration in software engineering, from Herzing University.

Invited Talks & Media

Publications

  1. J. Webb, J. VanPelt, E. Veenhuis, and S. Snarski, "Development and Analysis of the PitViper Infrared Projectile Tracking System for Direct and Indirect Fire Applications," in TARGETS, BACKGROUNDS, AND DISCRIMINATION, 2023. [URL]
  2. D. Davila, D. Du, B. Lewis, C. Funk, J. Van Pelt, R. Collins, K. Corona, M. Brown, S. McCloskey, A. Hoogs, and B. Clipp, "MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification," in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023. [URL]
  3. J. Webb, A. Bray, H. Scheirich, J. VanPelt, R. Clipp, J. Gerard, and S. Frembgen, "IMPLEMENTATION OF A DYNAMIC AND EXTENSIBLE MECHANICAL VENTILATOR MODEL FOR REAL-TIME PHYSIOLOGICAL SIMULATION," in ANNSIM’22, 2022. [URL]
  4. 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]

Bibliography generated 2024-02-14-11:00:05 (7076)