Role
Dr Pedro Machado is Associate Professor of Neuromorphic Computing at Nottingham Trent University. He holds an integrated MSc in Electrical and Computer Engineering from the University of Coimbra, Portugal, awarded in 2012, and a PhD in Computer Science from Nottingham Trent University, completed in 2022. His doctoral research developed computational models of retinal ganglion cells for motion detection and translated biologically inspired spiking architectures into FPGA-accelerated systems for real-time visual processing.
His research focuses on neuromorphic vision, spiking neural networks, event-driven perception and biologically inspired computing. Particular emphasis is placed on understanding how specialised retinal mechanisms, including direction selectivity, object-motion sensitivity, looming detection and anticipatory motion processing, can be reproduced in artificial systems. Current work investigates how such mechanisms can support low-latency, energy-efficient and adaptive perception on reconfigurable and edge-computing platforms. His wider research examines how neuromorphic systems can be integrated with modern artificial intelligence pipelines, multimodal sensing and distributed edge-to-cloud infrastructures. Visual, acoustic and environmental data are combined to support adaptive monitoring, behavioural analysis and autonomous decision-making in complex environments. Methodological contributions include spiking neural modelling, multimodal data fusion, hardware acceleration, reproducible MLOps workflows and distributed architectures capable of coordinating multiple sensors and intelligent agents.
Applications include autonomous robotics, environmental monitoring, aquatic animal welfare, biomedical sensing and resilient monitoring systems. Across these domains, his work seeks to reduce computational and energy requirements while improving responsiveness, adaptability and deployment reliability. Dr Machado leads interdisciplinary research involving computer science, engineering, environmental science and healthcare. His academic contributions include directing the EnviroBrain Impact Case Study, leading the development of the MSc Artificial Intelligence conversion course and serving as its Course Leader until July 2026. He is an IEEE CertifAIEd Lead Assessor, a Fellow of Advance HE, a member of the EPSRC Peer Review College and First Secretary of the IEEE Systematic Innovation Special Interest Group.
His long-term objective is to establish an internationally recognised research programme in neuromorphic vision and bio-inspired intelligent systems. The programme aims to develop artificial perception technologies that emulate the efficiency and anticipatory capabilities of biological sensory systems while remaining scalable, reproducible and suitable for deployment in real-world autonomous and assistive applications.
Research areas
Pedro is a member of the Computational Intelligent and Applications group.
Research interests:
Neuromorphic engineering, edge computer vision, bio-inspired computing, robotics and intelligent sensors, retinal cell understanding, biological nervous system modelling, spiking neural networks, robotics and autonomous systems, and neuromorphic hardware, aquaculture, endangered/invasive underwater species.
PhD supervision:
Mr Feliciano Domingos (Director of Studies) - Elevating Internet of Things Devices for Underwater Communication Applications Employing Highly-Efficient Artificial Cognitive Devices –October2023 ~ Now [Writing stage]
Mr Dennis Monari (Director of Studies) –Pioneering Biodiversity Monitoring: Harnessing AI/ML with a Cognitive Sensor Network and Internet of Water (IoW) Devices – February 2023 ~ Now
Miss Chloe Boulter (Director of Studies) - Real-Time Stress and Anxiety Detection using Spiking Neural Networks (SNNs) for Efficient Healthcare Expenditures Reduction - October2024 ~ Now
Mr Jack Lewton (Co-Supervisor) – Using AI to monitor captive animal welfare. October 2024 ~ Now
Mr James Brereton (Co-Supervisor) – Wildlife Cryoconservation: Development of biobanking strategies to halt and reverse biodiversity loss. October 2024 ~ Now [Writing stage]
Mr JOSEPH ORIGBO - An interdisciplinary and inclusive co-designed solution for an autonomous musculoskeletal surgical platform. October 2024 ~ Now
Mr Daniel Shabanianalavi - Advancing Health and Well-Being with Smart Sensing and Computational Intelligence. July 2025~ Now
Mr Anthony Nonso Chimezie - Leveraging Artificial Intelligence and Process Mining to Enhance Governance and Compliance in Healthcare Sector: A Predictive and Explainable Approach. April 2026 ~ NOW
PhD Completions:
Dr Michael Gibbs (Co-Supervisor) – AI at the Edge for robotic development and applications. October 2022~May 2025
PhD Vivas:
Chair
- Dr Abha (2025)
- Dr Omoyemi Rebecca Ojo (2025)
- Dr Mohammadreza Lalegani Dezaki (2024)
- Dr James Hall (2024)
- Dr Victor Okenyi (2024)
Internal Examiner
- Dr Faiza Guerrache (2024)
External Examiner
- Dr Dominique Sanderson (Brunel University) (2025)
- Dr John Doherty (University of Ulster) (2024)
MPhil Vivas:
Mr Connor Farrell (Chair) - 2025
Income Generation:
- RehabAI: Guiding Therapy with Data-Driven Insights - Funded through EPSRC (Co-Investigator) (£49,000)- [2026 ~ 2027]
- AI-AFS - Funded through InnovateUK (£575,000) (Principal Investigator) - [2025 ~ 2027]
- SAMACT – Bionic Prosthetic Arm Funded by the DAIWA Foundation (£8000) (Principal Investigator) - [2024 ~ 2025]
- 11x Postgraduate Scholarships for the MSc Artificial Intelligence conversion Course funded by the OfS (£110,000) (Principal Investigator) - [2024/2025]
- NeuroLiquidFilter: Advancing Bio-Inspired Filtering with Neuromorphic Intelligence Funded by the Intel Corporation (£20,000 in-kind contribution) (Principal Investigator) - [2024-2025]
- STEM 4.0: Advancing Technology Education through AI-Driven and Adaptive Learning, Funded by the British Council (£29,374) (Co-Investigator) - [2025 - 2026]
- SmartBerry: Artificial Intelligence to Enhance Strawberry Farming In Developing Countries. Funded by Innovate UK grant agreement 10071867 (£245,725), (Co-Investigator) - [2024 - 2026]
- To Create an Innovative AI Approach for Enhancing Data Quality through Data Augmentation in the Finance Sector. Funded by Innovate UK (£235,710), (Co-Investigator) - [2024 - 2027].
- 10x Postgraduate Scholarships for the MSc Artificial Intelligence conversion Course funded by the OfS (£100,000) (Principal Investigator) - [2023/2024]
- Field Companion project, Grant agreement 600359 funded by the InnovateUK (£214,714) (Principal Investigator) [2018-2021].
Active Projects:
- RehabAI: Guiding Therapy with Data-Driven Insights - Funded through EPSRC (Co-Investigator) (£49,000)- [2026 ~ 2027]
- NeuroLiquidFilter: Advancing Bio-Inspired Filtering with Neuromorphic Intelligence Funded by the Intel Corporation (£20,000 in-kind contribution) (Principal Investigator) - [2024-2025]
- STEM 4.0: Advancing Technology Education through AI-Driven and Adaptive Learning, Funded by the British Council (£29,374) (Co-Investigator) - [2025 - 2026]
- To Create an Innovative AI Approach for Enhancing Data Quality through Data Augmentation in the Finance Sector. Funded by Innovate UK (£235,710), (Co-Investigator) - [2024 - 2027].
Past projects:
- SmartBerry: Artificial Intelligence to Enhance Strawberry Farming In Developing Countries. Funded by Innovate UK (£245,725), (Co-Investigator) - [2024 ~ 2026]
- SAMACT – Bionic Prosthetic Arm Funded by the DAIWA Foundation (£8000) (Principal Investigator) - [2025]
Peer Review College Memberships:
- EPSRC
- BBSRC
- NERC
Teaching:
Pedro is the course leader of the MSc AI course and the Independent End Point Assessor for the L7 Degree Apprenticeship at the NTU.
Pedro is currently the Module Leader of
- SOFT40051: Advanced Software Engineering.
- COMP40731: Artificial Cognitive Systems.
- COMP40771: Computational Intelligence
Pedro is currently teaching as Lab Tutor/Supervisor:
- COMP40321: Research Methods | COMP40311: Major Project
- COMP30151: Final Year project
Pedro's teaching experience includes:
- COMP20091: Systems Software (2020 ~ 2025)
- COMP30271: Cognitive Computing (2023-2024)
- SOFT37001: Advanced Analysis and Design (DTS) (2021-2023)
- SOFT27002: Software Engineering (DTS) (2021-2023)
- SOFT27001: Software Design and Implementation (DTS) (2021-2023)
- ISYS30221: Artificial Intelligence (2020 - 2021)
- SOFT20091: Software Design & Imp (2015 - 2021)
- COMP40321: Research Methods | COMP40311: Major Project (2015 - Now)
- COMP10082: Foundations of Comp & Tech (2018)
- COMP30151: Full Year project (2015 - Now)
- ITEC40091: Embedded Systems (2015 and 2016)
- SOFT20101: Info and Database Engineering (2015)
Publications
Journals:
KOBIR, M.I., MACHADO, P., LOTFI, A., HAIDER, D. and IHIANLE, I.K., 2025. Enhancing multi-user activity recognition in an indoor environment with augmented Wi-Fi channel state information and transformer architectures.Sensors, 25 (13): 3955. ISSN 1424-8220
KRISHNA, M.S., MACHADO, P., OTUKA, R.I., YAHAYA, S.W., NEVES DOS SANTOS, F. and IHIANLE, I.K., 2025. Plant leaf disease detection using deep learning: a multi-dataset approach.J, 8 (1): 4. ISSN 2571-8800
ALEX, A.J., BARNES, C.M., MACHADO, P., IHIANLE, I., MARKÓ, G., BENCSIK, M. and BIRD, J.J., 2025. Enhancing pollinator conservation: monitoring of bees through object recognition.Computers and Electronics in Agriculture, 228: 109665. ISSN 0168-1699
DOMINGOS, F.P.F., LOTFI, A., IHIANLE, I.K., KAIWARTYA, O. and MACHADO, P., 2024. Underwater communication systems and their impact on aquatic life—a survey.Electronics, 14 (1): 7. ISSN 2079-9292
IHIANLE, I.K., MACHADO, P., OWA, K., ADAMA, D.A., OTUKA, R. and LOTFI, A., 2024. Minimising redundancy, maximising relevance: HRV feature selection for stress classification.Expert Systems with Applications, 239: 122490. ISSN 0957-4174
FONTES, L., MACHADO, P., VINKEMEIER, D., YAHAYA, S., BIRD, J.J. and IHIANLE, I.K., 2024. Enhancing stress detection: a comprehensive approach through rPPG analysis and deep learning techniques.Sensors, 24 (4): 1096. ISSN 1424-8220
FERREIRA, J.F., PORTUGAL, D., ANDRADA, M.E., MACHADO, P., ROCHA, R.P. and PEIXOTO, P., 2023. Sensing and artificial perception for robots in precision forestry: a survey.Robotics, 12 (5): 139. ISSN 2218-6581
MACHADO, P., FERREIRA, J.F., OIKONOMOU, A. and MCGINNITY, T.M., 2023. NeuroHSMD: neuromorphic hybrid spiking motion detector.ACM Transactions on Reconfigurable Technology and Systems, 16 (3): 36. ISSN 1936-7406
MURRAY-HILL, N., FONTES, L., MACHADO, P. and IHIANLE, I.K., 2023. Secure video streaming using dedicated hardware.Journal of Signal Processing Systems. ISSN 1939-8018
MAGALHÃES, S.C., SANTOS, F.N., MACHADO, P., MOREIRA, A.P. and DIAS, J., 2023. Benchmarking edge computing devices for grape bunches and trunks detection using accelerated object detection single shot multibox deep learning models.Engineering Applications of Artificial Intelligence, 117 (Part A): 105604. ISSN 0952-1976
MACHADO, P., OIKONOMOU, A., FERREIRA, J.F. and MCGINNITY, T.M., 2021. HSMD: an object motion detection algorithm using a Hybrid Spiking Neural Network Architecture.IEEE Access. ISSN 2169-3536
YU, Z., MACHADO, P., ZAHID, A., ABDULGHANI, A.M., DASHTIPOUR, K., HEIDARI, H., IMRAN, M.A. and ABBASI, Q.H., 2020. Energy and performance trade-off optimization in heterogeneous computing via reinforcement learning.Electronics, 9 (11): 1812. ISSN 2079-9292
COSTALAGO-MERUELO, A., MACHADO, P., APPIAH, K., MUJIKA, A., LESKOVSKY, P., ALVAREZ, R., EPELDE, G. and MCGINNITY, T.M., 2018. Emulation of chemical stimulus triggered head movement in the C. elegans nematode.Neurocomputing. ISSN 0925-2312
See the full list in the DBLP database.