Ryan Shilliday
About Ryan Shilliday
Ryan Shilliday is a Computer Vision Engineer at SharpestMinds in Toronto, Canada, where he has worked since 2019. He specializes in algorithms for 3D object detection and has made significant contributions to the field, including the application of the PointPainting algorithm and the development of a custom dataset for testing Incremental Structure from Motion.
Work at SharpestMinds
Ryan Shilliday has been employed as a Computer Vision Engineer at SharpestMinds since 2019. In this role, he focuses on developing and applying advanced algorithms for computer vision tasks. His work involves leveraging deep learning techniques and innovative methodologies to enhance the capabilities of computer vision systems.
Education and Expertise
Ryan Shilliday holds a Master's degree in Applied Mathematics from Ryerson University, where he studied from 2018 to 2019. He also earned a Bachelor of Science degree in Biology from the same institution, completing his studies from 2012 to 2016. His educational background provides a strong foundation in both mathematical principles and scientific methodologies.
Background
Ryan Shilliday has a diverse academic background that combines mathematics and biology. His studies in Applied Mathematics have equipped him with analytical skills essential for solving complex problems in computer vision. His experience in biology complements his technical expertise, allowing him to approach engineering challenges from a multidisciplinary perspective.
Achievements in Computer Vision
Ryan has made significant contributions to the field of computer vision, particularly through the implementation of the PointPainting algorithm. He applied this algorithm to the KITTI dataset to enhance car location detection. Additionally, he developed a custom dataset for testing the Incremental Structure from Motion algorithm and achieved an outlier rate of less than 1% in point cloud generation.