Dr. Alex Shenfield MEng, PhD, SMIEEE
Associate Professor in Machine Learning
- Department of Engineering and Mathematics
- Communication and Computing Research Centre
- National Centre of Excellence for Food Engineering
- Industry and Innovation Research Institute
Summary
I am currently an Associate Professor of Machine Learning at Sheffield Hallam University. I joined Sheffield Hallam University in November 2013 from Manchester Metropolitan University and am a Senior Member of the IEEE (SMIEEE) and a Fellow of the Higher Education Academy (FHEA). My main research interests are in the field of machine learning and particularly in its application to real-world problems in image processing and pattern recognition, healthcare, and Industry 4.0.
About
I am an active researcher with research interests focused primarily in the field of machine learning and its application to real-world problems in image processing and pattern recognition, healthcare, and Industry 4.0. I have published over thirty peer-reviewed journal and conference papers on the application of AI and ML to a variety of problems in engineering, security, and healthcare in a range of high-impact venues (with more than a dozen oral presentations at international conferences). According to Google Scholar, my publications have received over 400 citations and I have a current H-Index of 10. I am currently principal investigator / co-investigator on five successfully funded external research and development projects investigating the application of ML techniques and software engineering principles to a variety of problem domains, with a total value to Sheffield Hallam University of over £750,000. As well as this, I have been principal investigator / co-investigator on several recently completed externally funded research and innovation projects (including grants from Innovate UK and MRC) and several smaller grants (including support from Google and nVidia). I was 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.
Teaching
Department of Engineering and Mathematics
Business, Technology and Enterprise
Subject Area
Electronic Engineering
Courses
BEng (Hons) Computer Systems Engineering; BEng / MEng (Hons Electrical and Electronic Engineering.
Modules
Level 6 – Embedded Computer Networks; Architectures for the Internet-of-Things; Microprocessor Systems Applications // Level 5 – Object Oriented Programming for Engineers
Research
- Communication and Computing Research Centre , National Centre of Excellence for Food Engineering
- Industry and Innovation Research Institute
I am currently principal investigator or co-investigator on five successful externally funded research and development projects investigating the application of AI techniques and software engineering principles to a variety of problem domains, with a total value to Sheffield Hallam University of over £750,000. These include: The 24-month BBSRC funded "Reinventing Rice Milling for the Digital Era" project as Co-I with a total value of c. £1.07M (of which £426,000 is allocated to SHU). I am responsible for the development of novel AI techniques for computer vision based QA and machine optimisation in Industry 4.0. An Innovate UK funded KTP with ACS Stainless Steel as PI investigating the application of modern software engineering techniques to Design and Manufacturing Management (DMM) systems with a total project value of £220,000. A GrowMedTech Proof-of-Feasibility project (as Co-I) investigating the application of AI in a stroke risk monitoring service with a total project value of c. £29,000. I developed the models and algorithms underpinning the AI part of this project. A GrowMedTech Proof-of-Market project as PI investigating the application of AI assisted computer vision techniques in the development of a digital pathology platform with a total value of c. £5,000. I have also been principal investigator / co-investigator on several recently completed research and knowledge exchange projects including: As Co-I on the 12-month European Regional Development Fund (ERDF) funded project with British Online Archives investigating "Machine Learning in Linguistic Analysis for Historical Archives" (total value c. £90,000). Completed March 2020. As Co-I on the Medical Research Council (MRC) funded "AFRICAA (Automatic Face Recognition in Critical CAre)" project with North Middlesex University Hospital (total value c. £60,000). Completed January 2020. As PI for a European Regional Development Fund (ERDF) funded Sheffield Innovation Programme project with CIT Digital to investigate the application of deep learning algorithms in automated image tagging (project value £15,000). This led to two (unsuccessful) Innovate UK grant applications which both scored 70%. SIP project completed November 2017. As PI for a consultancy project with Proctor and Gamble investigating the use of embedded systems and miniaturisation for shaving augmentation (value c. £8,000). Completed March 2017. As PI for an Innovate UK funded KTP with Thetford Ltd. investigating the development of novel cooking appliances (and their embedded control systems) for use in the recreational vehicle and leisure industry (project value c. £132,000). Completed November 2016.
Featured Projects
Relevant Projects
Undergraduate
– Machine learning for automated quality control in the Industrial Internet-of-Things;
Brain-computer interfaces for robotic arm control;
An Internet-of-Things enabled wildlife camera;
Postgraduate
– AI based nuclei segmentation in breast cancer;
Automated food quality assessment using machine learning-based computer vision;
Brain-computer interfaces for wheelchair control;
Collaborators and Sponsors
Alex works with a wide variety of industrial collaborators, funders, and other research organisations both in the UK and internationally. These include: // Proctor and Gamble, Koolmill, Microform Imaging, AI nexus, JJA pack, Slanted Theory, Quality Service Solutions, De Montford University (UK), Bournemouth University (UK), York University (UK), University of Toronto (Canada), Missouri University of Science and Technology (US), Innopolis University (Russia), University of Oulu (Finland).
Publications
Key Publications
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., 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
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
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., Kareem, M., Shenfield, A., Ali, A., & Acharya, U.R. (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., & Howarth, M. (2020). A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors, 20, 5112.
Faust, O., Barika, R., Shenfield, A., Ciaccio, E.J., & Acharya, U.R. (2020). Accurate detection of sleep apnea with long short-term memory network based on RR interval signals. Knowledge-Based Systems, 106591. http://doi.org/10.1016/j.knosys.2020.106591
Lagree, A., Mohebpour, M., Meti, N., Saednia, K., Lu, F.-.I., Slodkowska, E., ... Tran, W. (2021). A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Scientific Reports, 11 (1), 8025. http://doi.org/10.1038/s41598-021-87496-1
Lagree, A., Shiner, A., Alera, M.A., Fleshner, L., Law, E., Law, B., ... Tran, W.T. (2021). Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Current Oncology, 28 (6), 4298-4316. http://doi.org/10.3390/curroncol28060366
Journal articles
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
Shenfield, A., & Wainwright, R. (2018). Human Activity Recognition Making Use of Long Short-Term Memory Techniques. .
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., 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
Conference papers
Barika, R., Shenfield, A., Razaghi, H., & Faust, O. (2021). A smart sleep apnea detection service. 17th International Conference on Condition Monitoring and Asset Management, CM 2021. https://www.bindt.org/shopbindt/cd-roms/proceedings-of-cm-2021.html#.YWQX39rMIuV
Tran, W., Lu, F., Tabbarah, S., Lagree, A., Dodington, D., Jerzak, K., ... Shenfield, A. (2020). Quantitative Digital Pathology Biomarkers of Neoadjuvant Therapy Response in Breast Cancer. RADIOTHERAPY AND ONCOLOGY, 152, S277-S278.
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
Posters
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
Other activities
Guest editor for a special issue on “Green ICT, Artificial Intelligence and Smart Cities” in Sustainability.
Postgraduate supervision
Current students:
- Richard Wainwright (as Director of Studies).
- Olusogo Popoola (as Co-supervisor).
- Ryan Lewis (as Co-supervisor).
- Zeena Al-Tekreeti (as Co-supervisor).
- Surapong Kokkrathoke (as Co-supervisor).
- Ragab Barika (as Co-supervisor).
Past students:
- Mohammad Musameh. Smart grid for the leisure vehicle industry (2020).
- Fayad Abdulah. Comparative Modelling and Shade Analysis of Renewable Photovoltaic Systems (2016).
- 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).