Ryan Sanford
About Ryan Sanford
Ryan Sanford is an AI researcher with a strong background in electrical and biomedical engineering. He specializes in developing machine learning and computer vision algorithms for sports analysis and has contributed to research on neurological diseases.
Work at Sportlogiq
Ryan Sanford has been employed at Sportlogiq as an AI Researcher since 2019. In this role, he focuses on enhancing machine learning and computer vision algorithms to develop practical and scalable solutions for sports analysis. His work involves the application of advanced machine learning and deep learning techniques to address real-world challenges in the sports domain.
Education and Expertise
Ryan Sanford holds a Bachelor's Degree in Electrical and Biomedical Engineering from McMaster University, where he studied from 2010 to 2014. He further pursued a Master's Degree in Biomedical Engineering at McGill University, completing it in 2015. He continued his education at McGill, earning a Doctor of Philosophy (Ph.D.) in Biological and Biomedical Engineering from 2016 to 2019. His expertise encompasses video processing, medical imaging, computer vision, and machine learning.
Background
Ryan Sanford has a diverse academic and research background. He served as a Teaching Assistant at McMaster University from 2013 to 2014 and worked as an Undergraduate Research Assistant there from 2011 to 2014. At McGill University, he held positions as a Graduate Researcher from 2014 to 2019 and as a Research Assistant in the School of Communication Sciences and Disorders from 2015 to 2019. His research has contributed to the understanding of neurological diseases and advanced image processing techniques.
Achievements
Ryan Sanford has co-authored several papers presented at the Conference on Computer Vision and Pattern Recognition (CVPR) 2020, including works on Group Activity Detection from Trajectory and Video Data in Soccer and Actor-Transformers for Group Activity Recognition. He is also involved in a patent application for a System and Method for Group Activity Recognition in Images and Videos utilizing Self-Attention Mechanisms.