Martin is currently a (part-time) Professor of Computational Neuroscience and Cognitive Robotics in the Department of Computer Science at Nottingham Trent University. He leads the Computational Neuroscience and Cognitive Robotics research group at NTU. Previously he was Pro-Vice Chancellor for Student Affairs and Head of the College of Science and Technology.
Martin joined NTU as Dean of the School of Science and Technology in March 2014. In August 2016, he became Pro Vice-Chancellor for Student Affairs and Head of the College of Science and Technology. Formerly, he was Director of the Intelligent Systems Research Centre, Acting Associate Dean of the Faculty of Computing and Engineering and Head of the School of Computing and Intelligent Systems at Ulster University.
He was also a Director of the Ulster University’s technology transfer company, Innovation Ulster, and a spin out company Flex Language Services. He was awarded both a Senior Distinguished Research Fellowship and a Distinguished Learning Support Fellowship from Ulster University in recognition of his contribution to research and teaching. Martin is a Fellow of the IET, SMIEEE and a Chartered Engineer.
He retired from his post as PvC and Head of College in January 2018 and now focusses solely on research in a part-time capacity.
Martin's Research Track Record
Martin remains an active researcher. His current research interests are focused on computational intelligence, and in particular on computational systems which explore and model biological signal processing, specifically in relation to cognitive robotics and computational neuroscience. His work addresses the creation of computational models of neural processing of visual, auditory or tactile stimuli, and the creation of artificial intelligent systems to emulate such sensory processes, with implementations in both software and reprogrammable hardware. His work on cognitive robotics addresses the issues of autonomous learning and skill building in mobile robotics.
He is the author or co-author of over 330 research papers and has attracted approximately £25 million to support his research, from a wide variety of funding sources. He also has a strong interest in innovations in teaching and learning and has attracted substantial funding to support implementation of technology assisted learning in higher education.
In terms of computational neuroscience he is particularly interested in learning and memory studies of spiking neutrons grown on multielectrode arrays, working across the disciplines of neuroscience, biosciences and materials and computer science. His specific focus in robotics at present relates to the exploitation of modern tactile sensors in robot grasping, for applications in industry and healthcare.
Currently he supervises one Postdoctoral Fellow at NTU and mentors one independent Research Fellow.
Previous PhD Student Supervision
Martin has (jointly) supervised to successful completion 34 students (R. McLellan, S.I. Salih, B. Roche, J. Harkin, E. Coyle, D. Coyle, A. Johnston, P. Kelly, Q. Wu, G. Leng, C. Carr, A. Belatreche, A. Ghani, B. Meehan, B. Glackin, P. Herman, P. Goyal, T. Strain, M. McBride, J. Wall, V. Gandhi, E. Obilze, I. Sirajuddin, G. Das, E. Gerlein, Scott McDonald, Augusto Gomez, Emmett Kerr, Jinling Wang, Yauheniya Shynkevich, Richard Gault, Benjamin Wingfield, Nick Weir, Nikesh Lama)
Current PhD Student Supervision
Currently, Martin is a PhD supervisor (joint) for 3 full time PhD students and 1 Part-time PhD student at NTU and three full time PhD students at Ulster University.
Martin's leads the Computational Neuroscience and Cognitive Robortics research group at NTU. The group is one of four in the Centre for Computer Science and Informatics at NTU.
Martin is a current or former grant holder on a large number of national, international and EU projects with a total well in excess of £25million. He is a regular reviewer for national and international research funding organisations, and journals / prestigious conferences and he contributes strongly to the research community. His research collaborations include projects with universities and companies through Europe, India, USA, and China among others. Currently he is a member of a consortium working on a US Ireland research proposal, with academic partners in the Republic of Ireland and the USA. He has been an expert reviewer for the EU Framework programmes for over a decade.
Martin is a member of the EPSRC Peer Review College.
Sponsors and collaborators
Martin’s research funding has included support from the European Union (13 grants), UK Engineering and Physical Sciences Research Council, N.I. Industrial Research and Development Unit, UK Dept. Trade and Industry, Leverhulme Trust, NI Dept. Education and Learning, industry (Citi, Fidessa, First Derivatives, Singularity, NYSE Technologies), Udaras na Gaeltachta Ireland, N.I. Foresight Programme, Science Research Infrastructure Fund 2 and 3, HEA North South Collaborative Research Programme Nireland-Ireland, N.I. Dept. Enterprise,Trade and Investment, EU Interreg, InvestNI, Derry City Council, Enterprise Ireland, Intertrade Ireland and the ILEX Urban Regeneration (Derry).
While Dean of Science and Technology Martin played a major role in getting £10million HEFCE funding for the £13 million Interdisciplinary Science and Technology (ISTeC) facility at NTU. He was also instrumental in gaining external funding of £9.7 million for the £23 million Medical Technologies Innovation Facility (MTIF) at NTU.
Currently he is the lead proposer on a Derry City and Strabane District Council City Deal project entitled the Centre for Industrial Digitalisation, Robotics and Automation (CIDRA), a major £25million Industry 4.0 innovation proposal which has been approved to proceed to outline business case.
Some Recent Research Publications include:
- TAHERKHANI, A., BELATRECHE, A., LI, Y., COSMA, G., MAGUIRE, L.P. and MCGINNITY, T.M., 2020. A review of learning in biologically plausible spiking neural networks. Neural Networks, 122, pp. 253-272. ISSN 0893-6080. https://doi.org/10.1016/j.neunet.2019.09.036
- COSMA, G. and MCGINNITY, T.M., 2019. Feature extraction and classification using leading eigenvectors: applications to biomedical and multi-modal mHealth data. IEEE Access, 7, pp. 107400-107412. ISSN 2169-3536https://doi.org/10.1109/access.2019.2932868
- TAHERKHANI, A., COSMA, G. and MCGINNITY, T.M., 2019. Optimization of output spike train encoding for a spiking neuron based on its spatiotemporal input pattern. IEEE Transactions on Cognitive and Developmental Systems. ISSN 2379-8920 http://doi.org/10.1109/TCDS.2019.2909355
- DAS, G.P., VANCE, P.J., KERR, D., COLEMAN, S.A., MCGINNITY, T.M. and LIU, J.K., 2019. Computational modelling of salamander retinal ganglion cells using machine learning approaches. Neurocomputing, 325, pp. 101-112. ISSN 0925-2312https://doi.org/10.1016/j.neucom.2018.10.004
- GÓMEZ EGUÍLUZ, A., RAÑÓ, I., COLEMAN, S.A. and MCGINNITY, T.M., 2019. Reliable robotic handovers through tactile sensing. Autonomous Robots. ISSN 0929-5593 http://doi.org/10.1007/s10514-018-09823-2
- TAHERKHANI, A., COSMA, G. and MCGINNITY, T.M., 2018. Deep-FS: a feature selection algorithm for deep Boltzmann machines. Neurocomputing, 322, pp. 22-37. ISSN 0925-2312 https://doi.org/10.1016/j.neucom.2018.09.040
- GAULT, R., MCGINNITY, T.M. and COLEMAN, S., 2018. A computational model of thalamocortical dysrhythmia in people with tinnitus. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26 (9), pp. 1845-1857. ISSN 1558-0210https://doi.org/10.1109/TNSRE.2018.2863740
- GÓMEZ EGUÍLUZ, A., RAÑÓ, I., COLEMAN, S.A. and MCGINNITY, T.M., 2018. Multimodal material identification through recursive tactile sensing. Robotics and Autonomous Systems. ISSN 0921-8890 http://doi.org/10.1016/j.robot.2018.05.003
- WINGFIELD, B., COLEMAN, S., MCGINNITY, T.M. and BJOURSON, A., 2018. Robust microbial markers for non-invasive inflammatory bowel disease identification. IEEE/ACM Transactions on Computational Biology and Bioinformatics. ISSN 1545-5963http://doi.org/10.1109/TCBB.2018.2831212
- Kerr D., Coleman S.A, McGinnity T.M., (2018) Biologically Inspired Intensity and Depth Image Edge Extraction, IEEE Transactions on Neural Networks and Learning Systems 2018
- Kerr, Emmett, McGinnity, T.M. and Coleman, SA (2017) Material Recognition using Tactile Sensing. Expert Systems with Applications
- Gerlein, Eduardo, McGinnity, TM, Belatreche, Ammar and Coleman, Sonya (2017) Network on Chip Architecture for Multi-agent Systems in FPGA. Accepted for ACM Transactions on Reconfigurable Technology and Systems
- Herman, P.H., Prasad, Girijesh and McGinnity, TM (2017) Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain-Computer Interface Classification of Motor Imagery Induced EEG Patterns. IEEE Transactions on Fuzzy Systems 25 (1). pp. 29-42
- Jing, Min, Bryan, Scotney, Coleman, SA and McGinnity, T. Martin (2017) Novel "Squiral" (Square Spiral) Architecture for Fast Image Processing. Journal of Visual Communication and Image Representation, 49. pp. 371-381
- Joshi, Alok, Youssofzadeh, Vahab, Vemana, Vinith, McGinnity, TM, Prasad, Girijesh and Wong-Lin, KongFatt (2017) An Integrated Modelling Framework for Neural Circuits with Multiple Neuromodulators. Journal of The Royal Society, Interface, 14 (126)
- Shynkevich, Yauheniya, McGinnity, T.Martin, Coleman, Sonya, Belatreche, Ammar and Li, Yuhua (2017) Forecasting Price Movements using Technical Indicators: Investigating the Impact of Varying Input Window Length. Neurocomputing, 264 . pp. 71-88
- Vance, Philip, Das, Gautham, Kerr, Dermot, Coleman, Sonya, McGinnity, T.Martin, Gollisch, Tim and Liu, Jian (2017) Bio-Inspired Approach to Modelling Retinal Ganglion Cells using System Identification Techniques. IEEE Transactions on Neural Networks and Learning Systems
- Yang, Shufan, Wong-Lin, KongFatt, Andrew, James, Mak, Terrence and McGinnity, TM (2017) A neuro-inspired visual tracking method based on programmable system-on-chip platform. Neural Computing and Applications
- Wang, J., Belatreche, A., Maguire, L.P., McGinnity, T.M., (2015) SpikeTemp: An enhanced rank-order based learning approach for Spiking Neural Networks with Adaptive Structure, IEEE Transactions on Neural Networks and Learning Systems
For a list of publications in the last six years, see NTU iREP.