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Last updated 5th June 2025

Dr. Ann Smith’s research interests lie at the interface of statistical inference and real-world applications in industrial contexts. Research is at the core of her professional pursuits and links naturally to her participation in the Analysis for Innovators (A4I) scheme with the Newton Gateway to Mathematics partnership, and her facilitation of industry problems at European Study Groups for Industry (ESGI). Her educational foundation includes a PhD in Engineering, an MSc in Applied Statistics, a BSc in Mathematics and a PGCE(FE). In addition, Dr. Smith is a Knowledge Exchange Champion with the Isaac Newton Institute for Mathematical Sciences, University of Cambridge, a Fellow of both the Institute of Mathematics and its Applications, and the Higher Education Academy and has been awarded Advanced Data Science Professional status.
Within the CEPE research group, she contributes statistical expertise in condition monitoring for predictive maintenance. Her research pursuits encompass an array of subjects, including input parameter volume reduction, innovative non-linear systems approach to detecting deviant events, autonomous abnormality assessment techniques, evidence-based healthcare diagnostics, and the promotion of effective mathematics education through e-learning. 
Globally acknowledged, Dr. Smith extends her influence as a visiting lecturer at Fuzhou Normal University in China and Universität Greiswald in Neubrandenburg, Germany. These international connections allow her to disseminate her knowledge and expertise to diverse audiences, enriching academic and collaborative networks.

• Condition Monitoring-statistical analysis of mechanical systems using vibration signal outputs. Fault detection in reciprocating compressors.
• Modelling System Behaviours.
• Input Volume Reduction Methodologies for Predictive Classifiers.
• Predictive Analytics for Applied Process Monitoring.
• Non-linear Systems Approaches to Detection of Deviant Events.
• Autonomous Abnormality Assessment.
• Mathematical modelling.
• Evidence based health care.
• e-learning and Education of Mathematics.