Dr Gaetano Di Caterina

Reader

Electronic and Electrical Engineering

Personal statement

I am an academic in the Department of Electronic and Electrical Engineering, and Fellow of the HEA. In particular, I am the Leonardo Lecturer on a joint programme (2023-2027) between Strathclyde and the global security company Leonardo UK Ltd.

Having completed a PhD in video analytics for smart surveillance, in the Centre for Signal & Image Processing (CeSIP), within the EEE Department at Strathclyde, my background and areas of expertise reside in signal, image and video processing. My current research interests are Neuromorphic Engineering, Machine Learning and Deep Learning, Digital Signal Processing and Embedded Systems.

I am Director of the Neuromorphic Sensor Signal Processing Lab, within the CeSIP group, supervising PhD students and PostDoc researchers working on various research projects and applications within these broad research areas.

I am Course Director of the MSc in Machine Learning and Deep Learning, which is a joint MSc programme between the EEE Department and the Computer and Information Sciences Department at Strathclyde.

And I am also Adviser of Studies for Year 1 of the undergraduate degree programme Computer and Electronic Systems, joint between EEE and CIS Departments.

Expertise

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Prize And Awards

Best Paper Award: Recipient; 27/8/2022
IEEE Brain Data Bank Challenges and Competitions: Recipient; 31/10/2017

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Publications

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Publications

A biopsy/non-biopsy approach to voice disorder classification using deep learning: Conway Frank, Di Caterina Gaetano, Perry Ross, Cohen Wendy, Wynne David M; IEEE International Conference on ICT Solutions for eHealth (2025)
An approach to time series forecasting with derivative spike encoding and spiking neural networks: Manna Davide Liberato, Di Caterina Gaetano, Vicente Sola Alex, Kirkland Paul; Proceedings of the 58th Annual Hawaii International Conference on System Sciences, HICSS 2025 58th Hawaii International Conference on System Sciences Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 7258-7267 (2025)
Spiking Neural Networks for event-based action recognition : a new task to understand their advantage: Vicente-Sola Alex, Manna Davide L, Kirkland Paul, Di Caterina Gaetano, Bihl Trevor J; Neurocomputing Vol 611 (2025); https://doi.org/10.1016/j.neucom.2024.128657
Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations: Moguilner Sebastian, Baez Sandra, Hernandez Hernan, Migeot Joaquín, Legaz Agustina, Gonzalez-Gomez Raul, Farina Francesca R, Prado Pavel, Cuadros Jhosmary, Tagliazucchi Enzo, Altschuler Florencia, Maito Marcelo Adrián, Godoy María E, Cruzat Josephine, Valdes-Sosa Pedro A, Lopera Francisco, Ochoa-Gómez John Fredy, Hernandez Alfredis Gonzalez, Bonilla-Santos Jasmin, Gonzalez-Montealegre Rodrigo A, Anghinah Renato, d’Almeida Manfrinati Luís E, Fittipaldi Sol, Medel Vicente, Olivares Daniela, Yener Görsev G, Escudero Javier, Babiloni Claudio, Whelan Robert, Güntekin Bahar, Yırıkoğulları Harun, Santamaria-Garcia Hernando, Lucas Alberto Fernández, Huepe David, Di Caterina Gaetano, Soto-Añari Marcio, Birba Agustina, Sainz-Ballesteros Agustin, Coronel-Oliveros Carlos, Yigezu Amanuel, Herrera Eduar, Abasolo Daniel, Kilborn Kerry, Rubido Nicolás, Clark Ruaridh A, Herzog Ruben, Yerlikaya Deniz, Hu Kun, Parra Mario A, Reyes Pablo, García Adolfo M, Matallana Diana L, Avila-Funes José Alberto, Slachevsky Andrea, Behrens María I, Custodio Nilton, Cardona Juan F, Barttfeld Pablo, Brusco Ignacio L, Bruno Martín A, Sosa Ortiz Ana L, Pina-Escudero Stefanie D, Takada Leonel T, Resende Elisa, Possin Katherine L, de Oliveira Maira Okada, Lopez-Valdes Alejandro, Lawlor Brian, Robertson Ian H, Kosik Kenneth S, Duran-Aniotz Claudia, Valcour Victor, Yokoyama Jennifer S, Miller Bruce, Ibanez Agustin; Nature Medicine Vol 30, pp. 3646-3657 (2024); https://doi.org/10.1038/s41591-024-03209-x
Bi-modal accuracy distribution in quantisation aware training of SNNs : an investigation: Pannir Selvam Durai Arun, Wilmshurst Alan, Thomas Kevin, Di Caterina Gaetano; Proceedings of SPIE: The International Society for Optical Engineering Vol 13206 (2024)
A demonstration of vector symbolic architecture as an effective integrated technology for AI at the network edge: Bent Graham, Davies Cai, Roig Vilamala Marc, Li Yuhua, Preece Alun, Di Caterina Gaetano, Vicente Sola Alex, Kirkland Paul, Pearson Gavin, Tutcher Benomy; Proceedings of SPIE: The International Society for Optical Engineering Vol 13206 (2024)

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Research

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Research Interests

Neuromorphic Engineering and Technologies
Machine Learning and Deep Learning
Image and Video Processing
Video Analytics for Surveillance
EMG Signal Processing
Speech Processing
Medical Image Processing
DSP Embedded Systems

Professional Activities

Strategic Themes: Applied AI Workshop: Participant; 17/1/2025
Ian O'Neil: Host; 9/12/2024
Strathclyde researchers seek diagnostic tool for respiratory condition: Contributor; 26/11/2018
Faculty Robotics and Automation Users Group Discussion: Participant; 10/10/2017

More professional activities

Projects

Leonardo Lab Phase 0 Activity: Di Caterina, Gaetano (Principal Investigator) Clemente, Carmine (Co-investigator); 01-Jan-2025 - 30-Jan-2025
Neuromorphic Sensor Fusion for Under Water Object Detection: Patil, Chaitanya (Principal Investigator) Di Caterina, Gaetano (Co-investigator); The project aims to develop a protocol of multimodal data collection through neuromorphic sensors for underwater object detection.
The underwater object detection is very tricky phenomenon due to variability of environmental
changes like wave motion, salinity as well as visibility under water. Various sensors with different
ranges and properties can be deployed to overcome the adverse environmental conditions for object detection. However, increasing number of sensors increases the data processing power, noise and uncertainty in the process rendering the data fusion from these sensors difficult. The proven neuromorphic methods introduce a paradigm shift in handling these sensor uncertainties through sensor fusion and increasing the autonomy of the data management and decision making within the operating envelope of all the sensors used for the object detection task.; 01-Jan-2024 - 01-Jan-2025
Neuromorphic sensor signal processing for spatio-temporal tracking in low SNR: Di Caterina, Gaetano (Principal Investigator); 01-Jan-2023 - 31-Jan-2026
NEU4SST – Neuromorphic Processing for Space Surveillance and Tracking: Di Caterina, Gaetano (Principal Investigator) Clemente, Carmine (Co-investigator) Macdonald, Malcolm (Co-investigator) Kirkland, Paul (Research Co-investigator); 15-Jan-2022 - 30-Jan-2023
Photonic Spiking Neural Networks for Ultrafast Detection and Tracking: Hurtado, Antonio (Principal Investigator) Di Caterina, Gaetano (Co-investigator); 01-Jan-2021 - 30-Jan-2023
Neuromorphic and spiking neural network applications: Di Caterina, Gaetano (Principal Investigator) Soraghan, John (Co-investigator); 01-Jan-2020 - 31-Jan-2024