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).
- Please visit the Pure Research Information Portal for further information
- Scalable cloud-based transfer learning for building energy prediction from limited digital twin data, Suliman, A., Mahamedi, E., Wonders, M. 25 Feb 2026, In: Journal of Information Technology in Construction (ITcon)
- A Cloud-Based Framework for Creating Scalable Machine Learning Models Predicting Building Energy Consumption from Digital Twin Data, Mahamedi, E., Suliman, A., Wonders, M. 23 Apr 2025, In: Architecture
- Measuring Flow: Refining Research Protocols that integrate Physiological and Psychological Approaches, Wonders, M., Hodgson, D., Whitton, N. 14 Feb 2025, In: Human Behavior and Emerging Technologies
- 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
Dean Rimmer Real-Time Sentiment-Enhanced Financial Models: From Data Integration to Profitability Metrics' Start Date: 01/10/2024
- 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
