• Scalable/interpretable Bayesian ML models: helping scientists better understand the world around us.
• Artificial intelligence for scientists: discovering equations, searching for molecules, and designing genes.
• Active learning: accelerating the design of emission-reducing technologies.

Fundamental AI Research: Building upon foundations in probabilistic modeling and statistical learning, I develop algorithms and theoretical guarantees for emulating, calibrating, and designing complex, costly systems.

AI Tools: I have deployed algorithms in the real-world, e.g. designing electric motors with Mazda, heat exchangers for Reaction Engines, and currently build AI models of point clouds with Boeing. My optimisation algorithms are embedded in the core operations of Amazon Alexa, Meta, and Mazda. 

Software Development: High-quality, domain-specific codebases are essential to furthering AI research. I have developed popular open-source Python ML libraries, including Trieste, GPFlow, and GPJax, and, in collaboration with chemists, created an award-winning Gaussian process library now widely adopted across US biotech startups.  

Recent advances in high-throughput experimentation and computation now empower scientists in traditionally high-cost fields—such as drug discovery, materials science, and engineering—to tackle ambitious challenges that surpass established experimental design methodology. Fortunately, Generative AI, capable of producing novel images, molecules, and engineered structures, holds the ability to fundamentally redefine how experiments are conceived, conducted, and iterated. My team is developing the algorithmic breakthroughs essential for harnessing the full potential of generative AI within experimental design, providing tools to accelerate scientific and industrial innovation.