Dr Martin Wonders

Assistant Professor

Department: Computer and Information Sciences

Martin is a senior lecturer in the department of computer and information sciences and teaches computer vision, applied programming, machine learning, Artificial Intelligent and Robotics. Martin's doctoral thesis on Activity Recognition in monitored environments using utility meter disaggregation, focused on the use of machine learning to both disaggregate signals from utility meters, and use these signals to infer the activity of the occupants.

Martins current research interests focus on Machine and Deep learning using data from a variety of sources including images and video as well as internal and external environmental sensor data from buildings. The latter of which is being applied to energy load demand forecasting for a local architectural company - European Regional Development Fund (ERDF).

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Key Publications

Please visit the Pure Research Information Portal for further information
A reinforcing transfer learning approach to predict buildings energy performance, Mahamedi, E., Wonders, M., Gerami seresht, N., Woo, W., Kassem, M. 9 Jan 2024, In: Construction Innovation
Digital Twin and Building Performance: A Review and Proposed Framework, Mahamedi, E., Wonders, M., Kassem, M., Woo, W., Greenwood, D. 5 Sep 2022, Proceedings of the 38th Annual ARCOM Conference, 5-7 September 2022, Glasgow, UK, London, Association of Researchers in Construction Management (ARCOM)
Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation, Tan, Y., Li, J., Wonders, M., Chao, F., Shum, H., Yang, L. 25 Jul 2016, WCCI 2016 - IEEE World Congress on Computational Intelligence
Training with synthesised data for disaggregated event classification at the water meter, Wonders, M., Ghassemlooy, Z., Hossain, A. 1 Jan 2016, In: Expert Systems with Applications

PGR Supervision

Elham Mahamedi The Application of AI and Machine Learning for the Better Performance and Predictive Maintenance of Buildings Using Data from Digital Twins Start Date: 08/11/2021 End Date: 25/06/2024
Neville Bulmer Intelligent Predictive Maintenance and Energy Load Forecasting for Smart Buildings Start Date: 01/09/2021 End Date: 29/09/2021
Dean Rimmer Real-Time Sentiment-Enhanced Financial Models: From Data Integration to Profitability Metrics" Start Date: 01/10/2024

Qualifications

Computer Science PhD March 02 2017
Engineering MSc June 06 2003
Computing PGDip June 01 2001
Communications Engineering June 30 1997
Associate Member Institution of Engineering and Technology (IET) 2014

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