ProfessorBernhard Kainz

I lead the Department of Computing’s teaching in Deep Learning and Computer Graphics, two of the largest and most technically advanced modules in the curriculum. My teaching combines hands-on experimentation, accessibility, and innovation, enabling students to engage directly with real-world artificial intelligence challenges.

I play a leading role in redesigning the Machine Learning curriculum at Imperial, moving Deep Learning to an earlier year to give students stronger foundations and earlier exposure to AI principles. This restructuring now supports a double cohort of several hundred students and provides a clearer pathway from fundamentals to advanced machine learning and research.

I am also closely involved in developing Imperial’s HX-AI infrastructure, a new high-performance computing environment that underpins large-scale AI and ML education. HX-AI provides every student with dedicated GPU access, creating one of the most advanced educational platforms for AI and data science worldwide.

In Computer Graphics, I created a framework for parallel vector compute shader programming (https://shaderlabweb.doc.ic.ac.uk/), which allows students to experiment with real-time GPU programming interactively in their browsers. The framework has been adopted internationally and is recognised as a transformative teaching tool for visual computing.

In Deep Learning, our approach has been featured by Imperial College as a model of excellence in AI education (link). The course combines scalable GPU-backed infrastructure with research-inspired coursework, preparing students to design, evaluate, and deploy AI systems responsibly and effectively.

Across all my teaching, I aim to make complex ideas intuitive, to democratise access to cutting-edge computing resources, and to inspire students to think like researchers and innovators in shaping the future of AI.