Prof Biagio Lucini

Biagio Lucini
PhD SFHEA FLSW

Professor of Mathematics
Head of The School of Mathematical Sciences

School of Mathematical Sciences
Queen Mary University of London
www.qmul.ac.uk/maths/profiles/lucinib.html
Centre for Geometry, Analysis and Gravitation
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Research

Theoretical Particle Physics, Strongly-interacting Gauge Theories, Monte Carlo Methods, Mathematical Foundations of Machine Learning, High-Performance Compunting, Mathematical modelling

Interests

Biagio's main research interest is Theoretical Particle Physics. Using the Lattice Gauge Theory approach, whereby strong interactions are discretised on a spacetime lattice and corresponding physical quantities are calculated using Monte Carlo methods on state-of-the-art supercomputers, he has been studying various open problems in strongly interacting Quantum Field Theories, including colour confinement, the existence of the large-N limit of SU(N) gauge theories, models of Higgs Compositeness and Top Partial Compositeness, and gravitational waves emitted by beyond the standard model strong interactions. Achievements include the first precise determination of the spectrum of glueballs in the large-N limit and the development of a highly efficient algorithm for studying systems at first-order phase transition points.

Biagio has been an early adopter of Machine Learning methods for studying phase transitions in Statistical Mechanics. His research in the field has resulted in the first computations of critical exponents using data-driven approaches. Currently, he is interested in physics-informed machine learning generative models, including deep belief networks and diffusion models.

Leveraging his expertise in large-scale data analysis and high-performance computing, he has been instrumental in deploying models to study the real-time evolution of the COVID-19 pandemic in Wales. This activity has resulted in a 4* REF Impact Case Study. Biagio remains active in infectious disease modeling and is currently working on novel agent-based models for a multiscale approach to infections caused by multiple pathogens.

Biagio is coauthor of the textbook Stochastic Methods for Scientific Programming - From Foundations to Advanced Techniques", published by CRC Press.