Dr Lawrence Bull
Lawrence Bull is a Research Fellow in statistical machine learning at the University of Glasgow. He has expertise in building interpretable models of engineered and natural systems, with expert and physical knowledge encoded in model structure. Applications include health and condition monitoring: from rail bridges and wind turbines to peat bogs and woodland.
Bio
May.2024-May.2029 University of Glasgow | Research Fellow | Centre for Data Science (2026-2029) Statistics & Data Analytics (2024-2026)
Sep.2022-May.2024 University of Cambridge | Research Associate | Computational Statistics and Machine Learning Group | Department of Engineering
Jul.2021-Sep.2022 The Alan Turing Institute | Research Associate | Data-Centric Engineering
Sep.2016-Jun.2021 University of Sheffield | Research Assistant (2019-2021) PhD Student (2016-2019) | Dynamics Research Group | Department of Engineering
PhD supervision
- Caitlin Castelvecchi: Probabilistic smart sensing for environmental monitoring [w/ Claire Miller, Craig Wilkie, Abdollah Jalilian]
- Alasdair Pool: Multiscale modelling of peat bogs [w/ Raimondo Penta, Vinny Davies]
- Daniel Clarkson: Active learning for engineering fleets [w/ Nikos Dervilis, Keith Worden]
- Sarah Bee: Multi-task learning for structural health monitoring [w/ Nikos Dervilis, Keith Worden]
Research projects
2026 (PI) Uncertainty Quantification for Adaptive Emulators | w/ Tim Rogers, Vinny Davies | DTnet+ pilot project
2025 (PI) Scaling Cardiac Models by Super-Resolution | w/ William Ryan, David Dalton | SoftTMech feasibility project
2023 (CI) Staffordshire Rail Bridge Digital Twin: Phase III | w/ Miguel Bravo Haro | Trimble Fund of Cambridge University
PhD + Post-doc Semi-supervised and population-based structural health monitoring: planes, trains, automobiles, and wind turbines. Supervisors: Keith Worden, Nikos Dervilis, Mark Girolami
External standing
Engineer at the data-centric engineering consultancy AQ, Editor for the Data-Centric Engineering journal, and member of Statistical Engineering Group of the Royal Statistical Society.
Previously on the Early-Career Academic and Professionals Panel (2021-2023) for the Cambridge Centre for Smart Infrastructure and Construction (CSIC).