José Cano Reyes

José Cano Reyes serves as a Reader (Associate Professor) in the School of Computing Science at the University of Glasgow, where he leads the Glasgow Intelligent Computing (GIC) Lab. His academic profile spans multiple premier conferences including ASE, CGO, ICSME, and EASE through 2025, demonstrating active engagement in computer systems research.

His research focuses on the critical intersection of hardware and software systems for AI workloads, with core interests in Computer Architecture, Compilers, and Machine Learning. Recent investigations examine deep learning framework conversions, hardware accelerator robustness, and security implications of computational environments. This work addresses fundamental challenges in deploying efficient and reliable AI systems across diverse hardware platforms.

Analysis of his 2023-2025 publications reveals a concentrated research trajectory toward optimizing deep learning deployment: 80% of recent work targets framework conversion errors and hardware compatibility issues, with strong emphasis on image recognition systems. Key methodologies include automatic fault localization (40% of publications), performance parameter analysis (30%), and domain-specific compiler techniques (30%).

• Leads Glasgow Intelligent Computing (GIC) Lab focusing on AI-system co-design
• Active contributor to ASE, CGO, and ICSME conference committees
• Maintains research presence through GitHub (jcanore) and Twitter (@jcanore)