Mike Jamieson
About Mike Jamieson
Mike Jamieson is a Computer Vision Researcher at SPORTLOGiQ in Kitchener, Canada, where he has worked since 2018. He holds a PhD in Computer Science from the University of Toronto and has over 15 years of experience in developing algorithms for real-time computer vision applications.
Work at SPORTLOGiQ
Mike Jamieson has been employed at SPORTLOGiQ as a Computer Vision Researcher since 2018. In this role, he develops high-performance algorithms tailored for real-time applications in computer vision. His work contributes to the company's focus on leveraging advanced technology to enhance sports analytics and performance insights. Located in the Kitchener, Canada Area, he has been part of the team for over six years.
Education and Expertise
Mike Jamieson holds a Doctor of Philosophy (PhD) in Computer Science from the University of Toronto, where he studied from 2004 to 2010. Prior to this, he earned a Master of Applied Science (MASc) in Systems Design Engineering from the University of Waterloo, completing his studies from 2000 to 2002. He also obtained a Bachelor of Applied Science (BASc) in Systems Design Engineering from the University of Waterloo, studying from 1994 to 1999. His educational background provides a strong foundation for his expertise in developing efficient algorithms for machine learning and computer vision.
Background
Before joining SPORTLOGiQ, Mike Jamieson worked as a Computer Vision Developer at Aimetis from 2010 to 2018. During his eight years at Aimetis, he focused on computer vision development, gaining extensive experience in the field. With over 15 years of experience in computer vision, he has built a robust skill set that supports his current research and development efforts.
Research Contributions
Mike Jamieson has co-authored several research papers in the field of computer vision. Notably, he contributed to a paper on self-supervised shape alignment for sports field registration, which was presented at the WACV 2022 conference. Additionally, he co-authored a paper on detecting and matching related objects with one proposal and multiple predictions, presented at CVPRW 2021. These contributions highlight his active involvement in advancing research within the computer vision community.