About
Dr. Henry Moss is a Lecturer in Mathematics and AI at Lancaster University's School of Mathematical Sciences. His research bridges fundamental AI theory with real-world applications, developing scalable Bayesian ML models and AI tools for scientific discovery. He leads projects in generative AI for experimental design and maintains collaborations with industry leaders including Boeing, Mazda, and Meta.
Research Focus: His work spans three interconnected pillars: 1) Foundational AI research in probabilistic modeling and statistical learning, 2) Deployment of optimization algorithms in industrial settings (electric motors, heat exchangers, point clouds), and 3) Development of high-impact open-source software including Trieste, GPFlow, and GPJax. His team pioneers AI methods to accelerate scientific innovation in chemistry, materials science, and engineering.
Publications: Recent works (2024) demonstrate strong focus on Bayesian methods applied to chemical informatics and optimization, featuring Gaussian process libraries and novel encoding techniques for high-throughput screening.
Awards: Recognized for developing an award-winning Gaussian process library widely adopted by US biotech startups.
Research Leadership: Advises PhD student Ruiyang Zhang and leads research groups including MARS (Mathematics for AI in Real-world Systems), Statistical Artificial Intelligence, and the STOR-i Centre for Doctoral Training.
