Ann Smith

Dr. Ann Smith is a Senior Lecturer in Data Science at the Department of Computer Science, University of Huddersfield. She is affiliated with the Centre for Efficiency and Performance Engineering and the Centre for Autonomous and Intelligent Systems. Her research focuses on statistical inference applied to industrial contexts, condition monitoring for predictive maintenance, and the reduction of input parameters in predictive classifiers. She is also engaged in promoting e-learning in mathematics and contributes to evidence-based healthcare diagnostics. Dr. Smith holds a PhD in Engineering, MSc in Applied Statistics, BSc in Mathematics, and PGCE(FE). She is a Knowledge Exchange Champion at the Isaac Newton Institute (University of Cambridge) and a Fellow of both the Institute of Mathematics and its Applications and the Higher Education Academy.

Her educational background includes advanced degrees in mathematics and engineering, complemented by a PGCE in further education. Her international collaborations include visiting lecturer roles at Fuzhou Normal University (China) and Universität Greiswald (Germany). Key research areas include fault detection in mechanical systems, non-linear systems analysis, and autonomous abnormality assessment techniques. She actively participates in industry-focused initiatives like the Analysis for Innovators scheme and European Study Groups for Industry (ESGI).

Dr. Smith’s scientific awards include Advanced Data Science Professional status and prestigious fellowships. Her work contributes to the UN Sustainable Development Goals, particularly in advancing quality education and industry innovation. She supervises PhD students and has published widely on topics ranging from coffee brewing optimization to genetic disorders linked to nasal polyps and bronchiectasis.

Her recent articles highlight advancements in medical imaging techniques (e.g., PET/CT), fault detection in renewable energy systems, and mathematical modeling of industrial processes. She has delivered invited talks on predictive maintenance and participated in numerous international conferences, showcasing her interdisciplinary impact.