Dr Alex Shenfield is a senior lecturer in Embedded Systems Engineering at Sheffield Hallam University. He joined Sheffield Hallam University in November 2013 from Manchester Metropolitan University and is a Fellow of the Higher Education Academy (FHEA). Alex's main research interests lay in the field of machine learning and its application to real-world problems in image processing, pattern recognition, and engineering design. He teaches predominantly in the areas of embedded systems, technologies for enabling the Internet-of-Things, and software engineering.
Dr Shenfield is also the organiser of an invited special session at the 2017 IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology in Manchester on the use of Machine Learning in Medical Diagnosis and Prognosis.
Dr Shenfield is an active researcher, with research interests mainly focused in the field of machine learning and its application to real-world problems in image processing, pattern recognition, and engineering design. He has published over a dozen internationally peer-reviewed journal and conference papers in the fields of intelligent systems, control, and pattern recognition. Alex sits on the international technical programme committee for the IEEE International Symposium on Communication Systems, Networks and Digital Signal Processing and is a reviewer for several high impact international journals.
Alex is currently the course leader for BEng (Hons) Computer Systems Engineering course and supervises several undergraduate and postgraduate projects in embedded systems, the Internet-of-Things, and data mining and analysis. Dr Shenfield regularly acts as an external panel member for the validation of courses at other institutions.
2008 – PhD in “Grid Enabled Optimisation using Evolutionary Algorithms”, University of Sheffield
2002 – MEng Computer Systems Engineering, University of Sheffield
Business, Technology and Enterprise
Dr Alex Shenfield is a course leader for the BEng (Hons) Computer Systems Engineering course in the Department of Engineering and Mathematics. He is also currently module leader for a wide range of modules in computer systems engineering and computer technology, including:
- Object Oriented Methods (level 7)
- Embedded Computer Networks (level 6)
- Architectures for the Internet-of-Things (level 6)
- Microprocessor System Applications (level 6)
- Object Oriented Programming for Engineers (level 5)
- Project 1 (level 4)
- Communication and Computing Research Centre
- Culture and Creativity Research Institute , Materials and Engineering Research Institute
Dr Shenfield is a member of the Geometric Modelling and Pattern Recognition (GMPR) research group in the Communication and Computing Research Centre (CCRC) and is involved in several current research projects in the fields of image processing and interpretation and pattern recognition including:
• The Vision-based Early Warning System (VIEWS) project in collaboration with North Middlesex University Hospital looking at automatic identification of facial expressions and correlation of those to physiological states in patients at risk of deterioration.
• The Intensive Care Unit Artificial Neural Network (IcuANN) project in collaboration with North Middlesex University Hospital and the North East and North Central London Hospital Network looking at the prediction of patient response to intensive care unit admission.
Alex has successfully supervised 2 PhD students to completion in the fields of machine learning and intelligent control systems and is currently Director of Studies for a PhD student looking at developing smart grid systems for recreational vehicles.
Dr Shenfield is also actively involved in applied knowledge transfer with the Materials and Engineering Research Institute (MERI), and has recently completed successful supervision of a 2 year knowledge transfer partnership (KTP) project looking at embedding modern electronic systems design knowledge within the company partner, Thetford LTD.
Liao, H., Milanovic, J.V., Rodrigues, M., & Shenfield, A. (2018). Voltage sag estimation in sparsely monitored power systems based on deep learning and system area mapping. IEEE Transactions on Power Delivery. http://doi.org/10.1109/TPWRD.2018.2865906
Faust, O., Shenfield, A., Kareem, M., San, T.R., Fujita, H., & Acharya, U.R. (2018). Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Computers in Biology and Medicine. http://doi.org/10.1016/j.compbiomed.2018.07.001
Madrigal-Garcia, M., Rodrigues, M., Shenfield, A., Singer, M., & Moreno-Cuesta, J. (2018). What faces reveal : a novel method to identify patients at risk of deterioration using facial expressions. Critical Care Medicine. http://doi.org/10.1097/CCM.0000000000003128
Rostami, S., & Shenfield, A. (2016). A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 21 (17), 4963-4979. http://doi.org/10.1007/s00500-016-2227-6
Shenfield, A., & Rostami, S. (2015). A multi-objective approach to evolving artificial neural networks for coronary heart disease classification. 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 1-8. http://doi.org/10.1109/CIBCB.2015.7300294
Rostami, S., O'Reilly, D., Shenfield, A., & Bowring, N. (2015). A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection. Information Sciences, 295, 494-520. http://doi.org/10.1016/j.ins.2014.10.031
Shenfield, A., & Howarth, M. (2020). A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors, 20, 5112.
Shenfield, A., & Howarth, M. (2020). A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors, 20 (18), e5112. http://doi.org/10.3390/s20185112
Madrigal-Garcia, M.I., Archer, D., Singer, M., Rodrigues, M., Shenfield, A., & Moreno-Cuesta, J. (2020). Do temporal changes in facial expressions help identify patients at risk of deterioration in hospital wards? A post hoc analysis of the Visual Early Warning Score study. Critical Care Explorations, 2 (5), e0115. http://doi.org/10.1097/CCE.0000000000000115
Faust, O., Kareem, M., Shenfield, A., Ali, A., & Rajendra Acharya, U. (2020). Validating the robustness of an internet of things based atrial fibrillation detection system. Pattern Recognition Letters, 133, 55-61. http://doi.org/10.1016/j.patrec.2020.02.005
Shenfield, A., & Wainwright, R. (2018). Human Activity Recognition Making Use of Long Short-Term Memory Techniques. .
Shenfield, A., Day, D., & Ayesh, A. (2018). Intelligent intrusion detection systems using artificial neural networks. ICT Express. http://doi.org/10.1016/j.icte.2018.04.003
Shenfield, A., Rodrigues, M., Valentine, D., Liu, D., & Moreno-Cuesta, J. (2015). An improved classifier for mortality prediction in adult critical care admissions. Journal of the Intensive Care Society, 16 (4), 118. http://doi.org/10.1177/1751143715615287
Twigg, P., Sigurnjak, S., Southall, D., & Shenfield, A. (2014). Exploration of the effect of EEG Levels in experiencedarchers. Measurement and Control, 47 (6), 185-190. http://doi.org/10.1177/0020294014539281
Shenfield, A., & Fleming, P. (2014). Multi-objective evolutionary design of robust controllers on the grid. Engineering Applications of Artificial Intelligence, 27, 17-27. http://doi.org/10.1016/j.engappai.2013.09.015
Shenfield, A., & Fleming, P.J. (2013). A Novel Workload Allocation Strategy for Batch Jobs. International Journal of Computing and Network Technology, 1 (1), 1-17. http://doi.org/10.12785/IJCNT/010102
Shenfield, A., & Rodenburg, J. (2011). Evolutionary determination of experimental parameters for ptychographical imaging. Journal of Applied Physics, 109 (12), 124510. http://doi.org/10.1063/1.3600235
Shenfield, A., Fleming, P., Kadirkamanathan, V., & Allan, J. (2010). Optimisation of maintenance scheduling strategies on the grid. Annals of Operations Research, 180 (1), 213-231. http://doi.org/10.1007/s10479-008-0496-x
Shenfield, A., Fleming, P., & Alkarouri, M. (2007). Computational steering of a multi-objective evolutionary algorithm for engineering design. Engineering Applications of Artificial Intelligence, 20, 1047-1057. http://doi.org/10.1016/j.engappai.2007.01.005
Shenfield, A., Khan, Z., & Ahmadi, H. (2020). Deep Learning Meets Cognitive Radio: Predicting Future Steps. In IEEE 91st Vehicular Technology Conference: VTC2020-Spring, 25 May 2020 - 28 May 2020. IEEE: http://doi.org/10.1109/VTC2020-Spring48590.2020.9129042
Shenfield, A., & Rostami, S. (2017). Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance. IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology. http://cibcb2017.org/
Shenfield, A., Rodrigues, M., Moreno-Cuesta, J., & Nooreldeen, H. (2017). A novel hybrid differential evolution strategy applied to classifier design for mortality prediction in adult critical care admissions. IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology. http://cibcb2017.org/
Rostami, S., Shenfield, A., Sigurnjak, S., & Fakorede, O. (2015). Evaluation of mental workload and familiarity in human computer interaction with integrated development environments using single-channel EEG. Proceeding of PPIG 2015 - 26th Annual Workshop. http://www.ppig.org/library/paper/evaluation-mental-workload-and-familiarity-human-computer-interaction-integrated
Rostami, S., & Shenfield, A. (2012). CMA-PAES : Pareto archived evolution strategy using covariance matrix adaptation for Multi-Objective Optimisation. 12th UK Workshop on Computational Intelligence (UKCI), 2012, 1-8. http://doi.org/10.1109/UKCI.2012.6335782
Delves, P., Manning, W., & Shenfield, A. (2012). A torque vectoring approach to post incident control. Proceedings of AVEC 2012.
Shenfield, A., & Fleming, P. (2011). Multi-objective evolutionary design of robust controllers on the grid. Proceedings of the 18th IFAC World Congress, 2011, 18/1 (18/1), 14711-14716. http://doi.org/10.3182/20110828-6-IT-1002.01384
Shenfield, A., Fleming, P., Kadirkamanathan, V., & Allan, J. (2007). Optimisation of maintenance scheduling strategies on the grid. In IEEE Symposium Series on Computational Intelligence (SSCI) 2007, Honolulu, Hawaii, 1 April 2007 - 5 April 2007.
Shenfield, A., & Fleming, P. (2005). A service oriented architecture for decision making in engineering design. Advances in Grid Computing - EGC 2005 Lecture Notes in Computer Science, 3470 (3470). http://link.springer.com/chapter/10.1007%2F11508380_35
Theses / Dissertations
Musameh, M.F.K.H. (2020). Power management strategy for the electric recreational vehicle. (Doctoral thesis). Supervised by Shenfield, A. http://doi.org/10.7190/shu-thesis-00288
Moreno-Cuesta, J., Madrigal, M., Shenfield, A., & Rodrigues, M. (2016). A novel method for identification of patients at risk of deterioration using FACS. Presented at: ICSSOA-2016 Intensive Care Society State of Art Meeting, London, 2016
- Mohammad Musameh. Smart grid for the leisure vehicle industry (working title).
- Fayad Abdulah. Comparative Modelling and Shade Analysis of Renewable Photovoltaic Systems. (1st supervisor)
- Peter Delves. Simulation Study Investigating the Novel use of Drive Torque Vectoring for Dynamic Post-Impact Vehicle Dynamic Control (2015).
- Shahin Rostami. Preference Focussed Many-Objective Evolutionary Computation (2014).