Savio Nguyen
About Savio Nguyen
Savio Nguyen is a Junior Imagery Analyst at ECS, where he has worked since 2020. He has a background in various roles at Wegmans Food Markets and holds a Bachelor of Science in Business Administration from Old Dominion University.
Work at ECS
Savio Nguyen has been employed at ECS as a Junior Imagery Analyst since 2020. In this role, he has focused on the precise labeling of imagery, which is essential for the development of computer vision algorithms. His contributions include creating training, testing, and evaluation data sets that enhance the accuracy of these algorithms. Additionally, he has supported significant initiatives, such as the Apollo program, by providing labeled imagery that aids in situational awareness for Full Motion Video and Overhead Aerial Imagery.
Previous Employment at Wegmans Food Markets
Prior to his role at ECS, Savio Nguyen worked at Wegmans Food Markets in various capacities. He served as a Cashier from 2007 to 2009, followed by a position as Maintenance from 2009 to 2014, and later as a Store Facility Technician from 2014 to 2017. His experience at Wegmans spanned a total of ten years, during which he developed skills in customer service and facility management.
Education and Expertise
Savio Nguyen earned a Bachelor of Science in Business Administration (BSBA) from Old Dominion University, where he studied Information Systems & Technology. His education spanned from 2016 to 2020, equipping him with a foundational understanding of technology and business principles. This academic background supports his current work in imagery analysis and computer vision.
Contributions to Computer Vision
In his current position, Savio Nguyen has made significant contributions to the field of computer vision. He has worked with a defined ontology to ensure high-level accuracy and precision in labeling objects, which is critical for the development of effective computer vision algorithms. His work not only supports algorithm development but also enhances the overall functionality of computer vision applications.