Search for courses or information

Dr Julie Wall

Reader in Computer Science

Intelligent Systems Research Group , Architecture, Computing and Engineering (ACE)


  • EB.1.98
    University of East London
    Docklands Campus
    University Way
    London, E16 2RD
    United Kingdom
    E16 2RD
  • 7791

    Since starting her PhD in 2006, Julie has been exploring the overarching research area of designing intelligent systems for processing and modelling temporal data. This primarily involves investigating the architectures and learning algorithms of neural networks for a variety of data sources, including numerical, audio, images, video, 3D, etc.

    The work began during her PhD in Computing and Intelligent Systems at Ulster University with the development of architectures and networks of spiking neuron models to simulate the sound localisation capability of the mammalian auditory pathways. During her first post-doctoral research position at Ulster University, the research progressed to adapting these spiking neural architectures to operate in an efficient and low-cost way on a mobile robot. The aim was to provide an ideal platform for the development of a human-like auditory system, which can operate in a dynamic and noisy environment.

    In 2011, Julie moved to Queen Mary, University of London and applied these computational intelligence techniques to the domain of virtual 3D data for the development of immersive experiences. She worked as the technical lead for the FP7 collaborative project “Real and Virtual Engagement in Realistic Immersive Environments (REVERIE)”, Grant No 287723. This project focused on the implementation and integration of cutting-edge technologies related to 3D data acquisition and processing, sound processing, autonomous avatars, networking, real-time rendering, and physical interaction and engagement in virtual worlds. The aim was for users to meet, socialise and share experiences using equipment they already have at home, along with a range of content creation tools we built for the platform. The possibilities of this type of technology are endless and we developed real-world scenarios, which were trialled by real-world users, such as primary school students attending a virtual field trip to the European Parliament and families communicating virtually using a type of 3D Skype.

    In 2015, Julie moved to UEL as a Senior Lecturer and later became Programme Leader for BSc (Hons) Computer Science. Her research now focuses primarily on deep neural networks for speech enhancement and recognition and she maintains collaborative R&D links with the industry. This has led to the successful acceptance of two Innovate UK grants with a combined total value of £2,273,177; UEL's portion amounts to £745,479.


    • Glackin, C., Wall, J., Chollet, G., Dugan N. and Cannings, N., “TIMIT and NTIMIT Phone Recognition using Convolutional Neural Networks”, Pattern Recognition Applications and Methods (ICPRAM 2018), Lecture Notes in Computer Science (LNCS), vol 11351, Springer, 2018.
    • C. Glackin, J. Wall, G. Chollet, N. Dugan, N. Cannings, “Convolutional Neural Networks for Phoneme Recognition”, 7th International Conference on Pattern Recognition Applications and Methods, 2018
    • C. Glackin, G. Chollet, N. Dugan, N. Cannings, J. Wall, S. Tahir, I. Ghosh Ray, M. Rajarajan, “Privacy preserving encrypted phonetic search of speech data”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
    • D. Schatz, R. Bashroush, J. Wall, “Towards a More Representative Definition of Cyber Security”, Journal of Digital Forensics, Security and Law, 2017
    • Badii, C. Glackin, G. Chollet, N. Dugan, N. Cannings, J. Wall, S. Tahir, I. Ghosh Ray, M. Rajarajan, R. Falkner, “A Roadmap for Privacy Preserving Speech Processing”, EAB Workshop on Preserving Privacy in an Age of Increased Surveillance – A Biometrics Perspective, 2017
    • M. Reljan-Delaney, J. Wall, “Solving the linearly inseparable XOR problem with spiking neural networks”, SAI Computing Conference, 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.
    • Doumanis, I., Wall, J. and Monaghan, D., “Playing immersive games on the REVERIE platform”, Workshop on Virtual Environments and Advanced Interfaces (VEAI), 2015.
    • M Pasin, A Frisiello, J Wall, S Poulakos, A Smolic, “A Methodological Approach to User Evaluation and Assessment of a Virtual Environment Hangout”, 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN), 2015.
    • Wall, J., Izquierdo, E., Argyriou, L., Monaghan, D., O’Connor, N., Poulakos, S., Smolic, A. & Mekuria, R., “REVERIE: Natural Human Interaction in Virtual Immersive Environments”, 21st IEEE International Conference on Image Processing (ICIP), pp. 2022-2024, 2014.
    • 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. & Wei, H., “Tools for User Interaction in Immersive Environments”, MultiMedia Modeling, pp. 382-385, Springer International Publishing, 2014.
    • Wall, J. and Glackin, C., “Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding”, Frontiers in Computational Neuroscience, vol. 7, no. 182, 2013.

    Full publication list: 


    Total funding: £2,277,177

    Apr 2019 - Mar 2021 - Innovate UK, "Automation and Transparency across Financial and Legal services: Mitigating Risk, Enhancing Efficiency and Promoting Customer Retention through the Application of Voice and Emotional AI", £2,005,177 (total), £473,479 (UEL)

    Sept 2018 - Aug 2021 - Knowledge Transfer Partnership (KTP), Innovate UK, "Improving Video Conferencing with Augmented Reality", £268,000

    Mar 2018 - May 2018 - UEL Funded Internship Scheme, "Development of computing taster sessions to support outreach & recruitment", £2,000

    June 2018 - Oct 2018 - UEL Funded Research Internship, "Deep Learning for Speech Enhancement in Noisy Environments", £2000


    Teaching 2018/19

    CN6121 Artificial Intelligence
    CN7023 Artificial Intelligence & Machine Vision
    CN5104 Computing in Practice
    Final year project supervision
    MSc dissertation supervision

    PhD Supervision

    2019 - 2022        Soha Abdallah                   Industrial PhD studentship

    Completed PhDs

    2014 - 2019         Dr Daniel Schatz