Julie Wall

Professor Julie Wall

Professor of AI and Advanced Computing
School of Computing and Engineering
julie.wall@uwl.ac.uk

Julie Wall is a Professor of AI and Advanced Computing at the University of West London. She is a member of the British Standards Institution (BSI) and serves as an expert in the field of AI. Her research focuses on designing intelligent systems to process and model temporal data, with a particular emphasis on speech and language applications.

With extensive expertise in neural network architectures, Julie has explored a range of models, from biologically inspired designs to computationally efficient machine and deep learning architectures. She has applied these techniques to various data structures, including tabular, audio, images, video and 3D feature data, achieving significant results. Julie has also developed production-grade deep learning and natural language understanding systems for a variety of platforms, including virtual and augmented reality environments. Her extensive research contributions have been widely recognised through the publication of a US and UK patent and 50+ high-quality research papers.

  • Qualifications

    • BSc (Limerick Institute of Technology)
    • MSc (Ulster University)
    • PhD (Ulster University)
    • PGCert (University of East London)

  • Memberships

    British Standards Institution (BSI)
    Higher Education Academy (HEA)
    British Computer Society (BCS)
    Institute of Electrical and Electronics Engineers (IEEE)
    Cambridge Wireless

Research

  • Research and publications

    For the updated list, please see Google Scholar

    Journal articles

    Iftikhar, A., Ghazanfar, M.A., Ayub, M., Alahmari, S.A., Qazi, N. and Wall, J., 2024. A reinforcement learning recommender system using bi-clustering and Markov Decision Process. Expert Systems with Applications, 237, p.121541.

    Bajaj, N., Constance, T.G., Rajwadi, M., Wall, J., Moniri, M., Glackin, C., Cannings, N., Woodruff, C., Laird, T., Laird, J., Deception Detection in Conversations using the Proximity of Linguistic Markers, Elsevier Knowledge Based Systems, 2023.

    Zorto, A.D., Sharif, M.S., Wall, J., Brahma, A., Alzahrani, A.I., Alalwan, N., An Innovative approach based on machine learning to evaluate the risk factors importance in diagnosing keratoconus, Informatics in Medicine Unlocked, 2023.

    Amirhosseini, M. and Wall, J. A Machine Learning Approach to Identify the Preferred Representational System of a Person. Multimodal Technologies and Interaction. 6 (12), p. 112, 2022.

    Nossier, S A., Wall, J., Moniri, M., Glackin, C., Cannings, N. An Experimental Analysis of Deep Learning Architectures for Supervised Speech Enhancement, Electronics, vol. 10, 1(17), pp. 1-32, 2021.

    Schatz, D., Bashroush, R. and Wall, J., Towards a More Representative Definition of Cyber Security, Journal of Digital Forensics, Security and Law, 2017.

    Wall, J.A., McDaid, L.J., Maguire, L.P. and McGinnity, T.M., Spiking neural network model of sound localization using the interaural intensity difference, IEEE Transactions on Neural Networks and Learning Systems, 23(4), pp.574-586, 2012.

    Glackin, B., Wall, J.A., McGinnity, T.M., Maguire, L.P. and McDaid, L.J., A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization, Frontiers in Computational Neuroscience, vol. 4, p.18, 2010.

  • Conferences

    Nossier, S.A., Wall, J., Moniri, M., Glackin, C. and Cannings, N., 2023, November. A Deep Learning Speech Enhancement Architecture Optimised for Speech Recognition and Hearing Aids. In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 553-558). IEEE.

    Nossier, S.A., Wall, J., Moniri, M., Glackin, C. and Cannings, N., 2023, November. Enhancing Automatic Speech Recognition Quality with a Second-Stage Speech Enhancement Generative Adversarial Network. In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 546-552). IEEE.

    Jacob, S., Wall, J. and Sharif, S., 2023, September. Analysis of Deep Neural Networks for Military Target Classification using Synthetic Aperture Radar Images. In 3ICT 2023: International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies. IEEE.

    Maltby, H., Wall, J., Goodluck Constance, T., Moniri, M., Glackin, C., Rajwadi, M. and Cannings, N., 2023. Short Utterance Dialogue Act Classification Using a Transformer Ensemble. UA-DIGITAL 2023: UA Digital Theme Research Twinning.

    Shrestha, R., Glackin, C., Wall, J., Cannings, N., Intelligent Voice Speaker Recognition and Diarization System for IberSpeech 2022 Albayzin Evaluations Speaker Diarization and Identity Assignment Challenge, IberSPEECH 2022.

    Shrestha, R., Glackin, C., Wall, J., Cannings, N., Rajwadi, M., Kada, S., Laird, J., Laird T., Woodruff, C., Speaker Recognition using Multiple X-Vector Speaker Representations with Two-Stage Clustering and Outlier Detection Refinement, 7th IEEE Cyber Science and Technology Congress (CyberSciTech), 2022.

    Nossier, S A., Wall, J., Moniri, M., Glackin, C., Cannings, Convolutional Recurrent Smart Speech Enhancement Architecture for Hearing Aids, INTERSPEECH 2022.

    Nossier, S A., Wall, J., Moniri, M., Glackin, C., Cannings, A two-stage DNN for speech enhancement and reconstruction in the frequency and time domains, IEEE International Joint Conference on Neural Networks (IJCNN), 2022.

    Poobalasingam, V., Cannings, N., Glackin, C., Wall, J., Sharif S., Moniri, M., A Mixed Reality Approach for dealing with the Video Fatigue of Online Meetings, 7th International XR Conference, 2022.

    Shrestha, R., Glackin, C., Wall, J. Cannings, N. Bird Audio Diarization with Faster R-CNN, International Conference on Artificial Neural Networks (ICANN), 2021.

    Goodluck Constance, T., Bajaj, N., Rajwadi, M., Wall, J., Moniri, M., Woodruff, C., Laird, T., Glackin, C., Laird J., Nigel Cannings, Resolving Ambiguity in Hedge Detection by Automatic Generation of Linguistic Rules, International Conference on Artificial Neural Networks (ICANN), 2021.

    Nossier, S A., Wall, J., Moniri, M., Glackin, C., Cannings, N. A Comparative Study of Time and Frequency Domain Approaches to Deep Learning based Speech Enhancement, IEEE International Joint Conference on Neural Networks (IJCNN), 2020.

    Nossier, S A., Wall, J., Moniri, M., Glackin, C., Cannings, N. Mapping and Masking Targets Comparison using Different Deep Learning based Speech Enhancement Architectures, IEEE International Joint Conference on Neural Networks (IJCNN), 2020.

    Bajaj, N., Constance, T.G., Rajwadi, M., Wall, J., Moniri, M., Glackin, C., Cannings, N., Woodruff, C., Laird, J., Fraud detection in telephone conversations for financial services using linguistic features, 33rd Conf. Neural Information Processing Systems (NeurIPS), AI for Social Good Workshop, 2019.

    Rajwadi, M., Glackin, C., Wall, J., Chollet, G., Cannings, N., Explaining Sentiment Classification, INTERSPEECH, 2019.

    Glackin, C., Dugan, N., Cannings, N., Wall, J., Smart Transcription, 31st European Conference on Cognitive Ergonomics (ECCE), 2019.

    Ali, A., Glackin, C., Cannings, N., Wall, J., Sharif, S., Moniri, M., A Framework for Augmented Reality Based Shared Experiences, Immersive Learning Research Network Conf. (iLRN), 2019.

    Rajwadi, M., Glackin, C., Cannings, N., Wall, J., Moniri, M., Study of Deconvolution Approaches for Text/Image AI Explainability, Research & Knowledge Exchange Conference, University of East London, 2019.

    Ali, A., Glackin, C., Cannings, N., Wall, J., Sharif, S., Moniri, M., Next Generation Video Conferencing using Personalised Augmented Reality, Research and Knowledge Exchange Conference, University of East London, 2019.

    Glackin, C., Wall, J., Chollet, G., Dugan, N. and Cannings, N., Convolutional Neural Networks for Phoneme Recognition, 7th International Conference on Pattern Recognition, Applications and Methods (ICPRAM), 2018.

    Glackin, C., Chollet, G., Dugan, N., Cannings, N., Wall, J., Tahir, S., Ghosh Ray, I. and Rajarajan, M., Privacy preserving encrypted phonetic search of speech data, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017.

    Badii, A., Glackin, C., Chollet, G., Dugan, N., Cannings, N., Wall, J., Tahir, S., Ghosh Ray, I., Rajarajan, M. and Falkner, R., A Roadmap for Privacy Preserving Speech Processing, EAB Workshop on Preserving Privacy in an Age of Increased Surveillance – A Biometrics Perspective, 2017.

    Reljan-Delaney, M. and Wall, J., Solving the linearly inseparable XOR problem with spiking neural networks, SAI Computing Conf., 2017.

    Wall, J., Glackin, C., Cannings, N., Chollet, G., Dugan, N., Recurrent lateral inhibitory spiking networks for speech enhancement, IEEE International. Joint Conference on Neural Networks (IJCNN), 2016.

    Wall, J., Glackin, C., Speech Enhancement with Recurrent Lateral Inhibitory Spiking Networks, Research and Knowledge Exchange Conference, University of East London, 2016.

    Wall, J., Deep Laterally Recurrent Spiking Neural Networks for Speech, Computing and Engineering Showcase, University of East London, 2016.

    Doumanis, I., Wall, J., Monaghan, D., Playing immersive games on the REVERIE platform, Workshop on Virtual Environments and Advanced Interfaces (VEAI), 2015.

    Pasin, M., Frisiello, A., Wall, J., Poulakos, S. and Smolic, A., A Methodological Approach to User Evaluation and Assessment of a Virtual Environment Hangout, 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN), 2015.

    O’Connor, N.E., Alexiadis, D., Apostolakis, K., Daras, P., Izquierdo, E., Li, Y., Monaghan D.S., Rivera, F., Stevens, C., Van Broeck, S., Wall, J. and Wei, H., Tools for user interaction in immersive environments, International Conference on Multimedia Modeling, pp. 382-385. Springer, Cham, 2014.

    Wall, J., Izquierdo, E., Argyriou, L., Monaghan, D.S., O'Connor, N.E., Poulakos, S., Smolic, A. and Mekuria, R., REVERIE: Natural human interaction in virtual immersive environments, IEEE International Conference on Image Processing (ICIP), pp. 2165-2167, 2014.

    Mauro, D.A., O’Connor, N.E., Monaghan, D., Gowing, M., Fechteler, P., Eisert, P., Wall, J., Izquierdo, E., Alexiadis, D.S., Daras, P. and Mekuria, R., Advancements and Challenges Towards a Collaborative Framework for 3D Tele-Immersive Social Networking, 4th IEEE International Workshop on Hot Topics in 3D (Hot3D). San Jose, CA, USA, 2013.

    Kuijk, F., Van Broeck, S., Dareau, C., Ravenet, B., Ochs, M., Apostolakis, K., Daras, P., Monaghan, D., O'Connor, N.E., Wall, J. and Izquierdo, E, A Framework for Human-like Behavior in an immersive virtual world, 18th IEEE International Conference on Digital Signal Processing (DSP), pp. 1-7, 2013.

    Fechteler, P., Hilsmann, A., Eisert, P., Broeck, S.V., Stevens, C., Wall, J., Sanna, M., Mauro, D.A., Kuijk, F., Mekuria, R. and Cesar, P, A framework for realistic 3D tele-immersion, 6th International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications, pp. 1-8, 2013.

    Wall, J.A., McGinnity, T.M., Maguire, L.P. A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics, IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1981-1987, 2011.

    Wall, J., McGinnity, T.M., Maguire, L. Using the interaural time difference and cross-correlation to localise short-term complex noises, Artificial Intelligence and Cognitive Science (AICS), 2011.

    Wall, J.A., McDaid, L.J., Maguire, L.P., McGinnity, T.M. Spiking neuron models of the medial and lateral superior olive for sound localisation, IEEE International Joint Conference on Neural Networks, pp. 2641-2647, 2008.

    Wall, J., McDaid, L.J., Maguire, L.P., McGinnity, T.M., A spiking neural network implementation of sound localisation, IET Irish Signals and Systems, pp.1-5, 2007.

    Wall, J. Perception-based Modelling of System Behaviour, IEEE Systems, Man and Cybernetics Society, 2006.

  • Research degree supervision

    Speech and language applications:

    • Research focus: Developing cutting-edge technologies for advanced speech and language applications using neural network architectures.
    • Proposed projects: Investigate new methods for speech recognition, natural language processing and language generation. Explore the application of neural networks in enhancing language-related technologies.

    Neural network architectures:

    • Research focus: Exploring diverse neural network models, including biologically inspired designs and computationally efficient machine and deep learning architectures.
    • Proposed projects: Research novel neural network architectures, compare their performance across various tasks and optimise designs for specific applications. Consider the application of neural networks in interdisciplinary contexts.

    Deep learning and natural language understanding:

    • Research focus: Developing advanced deep learning systems and technologies for natural language understanding.
    • Proposed projects: Investigate deep learning approaches for semantic understanding, sentiment analysis and context-aware language processing. Explore how deep learning can enhance natural language understanding in complex domains.

    Prospective PhD candidates are invited to engage in discussions for further details on these research topics, and there is room for tailoring these general themes to the specific interests and expertise of the candidates.

Courses taught on