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Dr Alex Shenfield

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Dr. Alex Shenfield MEng, PhD, SMIEEE

Associate Professor (Reader) in Machine Learning


Summary

I am currently 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). I am also the Digital Connectivity research theme lead for the National Centre of Excellence for Food Engineering (NCEFE). 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 (particularly in it's application to food and drink manufacturing). 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 500 citations and I have a current H-Index of 11.

I am currently / have been principal investigator or co-investigator on several successfully funded external research and development projects investigating the application of ML techniques and software engineering principles to a variety of problem domains (including funding from BBSRC, MRC, ERDF, and Research England), with a total value to Sheffield Hallam University of over £1,000,000. As well as this, I have been principal investigator on 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

College of Business, Technology and Engineering

Subject Area

Electronic Engineering

Courses

BEng/MEng (Hons) Electrical and Electronic Engineering

Modules

Level 6 – Embedded Computer Networks

Research

1) 2022-2024. An intelligent Industry 4.0 control systems platform for the food manufacturing industry (with QSS Ltd.). Funded by Innovate UK. £182,500.

2) 2022-2024. Machine learning tools for archive analysis (with Microform Imaging Ltd.). Funded by Innovate UK. £182,500.

3) 2021. Mapping the surgical wound in infrared: Feasibility to develop automated AI-based segmentation of anatomical bounding boxes for incision and reference sites. Funded by GrowMedTech. £10,000.

4) 2019-2022. A stroke risk monitoring service. Funded by GrowMedTech. £34,000.

5) 2019-2021. Next generation rice milling. Funded by BBSRC. £1,100,000.

6) 2019-2020. Machine learning in linguistic analysis for historical archives. Funded by ERDF. £90,000.

7) 2018-2019. Automatic face recognition in critical care (AFRICAA). Funded by MRC. £60,000.

Featured Projects

The University was awarded a grant for research into rice milling in China in June 2019.

Collaborators and Sponsors

Innovate UK; STFC; UKRI

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

Shenfield, A., Kasturi, S., & Tran, W. (2022). Accurate nuclei segmentation in breast cancer tumour biopsies. In 19th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, Ottawa, ON, Canada, 15 August 2022 - 17 August 2022. IEEE: http://doi.org/10.1109/CIBCB55180.2022.9863023

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 (Abstract only). Radiotherapy & Oncology, 152 (S1), S277-S278. https://www.thegreenjournal.com/issue/S0167-8140(20)X0011-9?pageStart=9

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

  • Associate Editor - Network: Computation in Neural Systems
  • Guest Editor - MDPI Processes: Special Issue on "The Role of Artificial Intelligence and Machine Learning in the Context of Sustainability"
  • Guest Editor - MDPI Sustainability: Special Issue on "Green ICT, Artificial Intelligence and Smart Cities"

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)
  • Ragab Barika (as Co-supervisor)

Past students:

  • Surapong Kokkrathoke. Nonlinear optimal control and it's application to a two-wheeled robot (2022)
  • 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)
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