Role
Dr Pedro Machado is Associate Professor of Neuromorphic Computing at Nottingham Trent University, developing energy-efficient artificial intelligence systems inspired by the way biological nervous systems perceive and respond to the world. His research combines neuromorphic computing, spiking neural networks, edge AI and intelligent sensing to create technologies that can make decisions rapidly, locally and with substantially reduced computational requirements. His work addresses problems where conventional AI can be constrained by energy consumption, latency, connectivity or the need to process large volumes of sensor data. Applications span autonomous robotics, environmental and biodiversity monitoring, animal welfare, intelligent sensing and healthcare, with a particular emphasis on translating research from algorithms into deployable systems.
Pedro works across computer science, electronic engineering and applied AI, combining visual, acoustic and environmental sensing with reconfigurable hardware, edge computing and distributed AI. His research seeks to develop intelligent systems that are more responsive, energy-efficient, adaptive and practical for deployment outside controlled laboratory environments.During 2026–27, Pedro is a Royal Academy of Engineering / Leverhulme Trust Research Fellow. His REACT project investigates how five retinal motion-processing mechanisms can be recreated using spiking neural networks and reconfigurable hardware to support energy-efficient motion perception, object tracking and collision prediction. The research targets future applications in robotics, intelligent sensing, assistive technologies and longer-term bionic vision systems.
Teaching
Current teaching
Pedro is currently on research sabbatical during the 2026/27 academic year, supported by his Royal Academy of Engineering–Leverhulme Trust Research Fellowship, enabling him to focus on his neuromorphic computing research programme. During this period, his normal teaching responsibilities are suspended.
Academic Leadership and Professional Assessment
- MSc AI conversion course (2022-2026)
- Independent End Point Assessor for the L7 Degree Apprenticeship at the NTU (2023-2025)
Module Leadership
- SOFT40051: Advanced Software Engineering (2022-2026)
- COMP40731: Artificial Cognitive Systems (2023-2026)
- COMP40771: Computational Intelligence (2025-2026)
- COMP20081: Systems Software (2020-2025)
- SOFT37001: Advanced Analysis and Design (DTS) (2021-2023)
- SOFT27002: Software Engineering (DTS) (2021-2023)
- SOFT27001: Software Design and Implementation (DTS) (2021-2023)
- SOFT20091: Software Design & Imp (2015 - 2021)
Lab Tutor and supervisor
- COMP30271: Cognitive Computing (2023-2024)
- ISYS30221: Artificial Intelligence (2020 - 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)
Research areas
Neuromorphic computing and bio-inspired artificial intelligence
Pedro develops artificial perception systems inspired by biological nervous systems. His research investigates how specialised mechanisms found in the retina and other sensory systems can be reproduced using spiking neural networks, event-driven computation and neuromorphic hardware. Current research includes direction-selective vision, object-motion sensitivity, looming detection, transient visual responses and anticipatory motion processing.
Edge AI and energy-efficient intelligent systems
A major objective of Pedro's research is to reduce the computational, energy and communication requirements associated with AI. He develops machine-learning and neuromorphic systems for FPGAs, embedded computers and edge-computing platforms where low latency, energy efficiency and autonomous operation are important.
Multimodal sensing and distributed intelligence
Pedro combines computer vision, acoustics and environmental sensing with distributed AI and edge-to-cloud architectures. His work investigates how multiple sensors and intelligent agents can collaborate to interpret complex environments, share information and support real-time decision-making.
Animal behaviour, welfare and precision monitoring
Animal welfare is an important application area within Pedro's research. He develops AI-enabled approaches for the objective and longitudinal assessment of animal behaviour using computer vision, acoustics, environmental sensing and multimodal machine learning. His work spans different species and environments, including chimpanzees, dairy cattle, and fish, where intelligent monitoring can support the identification of behavioural changes, welfare indicators and potential signs of stress or abnormality. Rather than relying exclusively on episodic human observation, his research investigates how continuous sensing can provide quantitative evidence about movement, social interaction, feeding, vocalisation, environmental responses and other behavioural patterns.
Autonomous monitoring and environmental intelligence
Pedro applies AI and intelligent sensing to biodiversity, ecosystems and environmental monitoring. His research includes aquatic monitoring, invasive-species detection, wildlife observation, precision agriculture and resilient sensing infrastructures. The objective is to translate AI research into systems capable of sustained operation in complex real-world environments, where changing illumination, weather, connectivity, power availability and biological variability present challenges that are rarely represented adequately in laboratory datasets.
His work increasingly explores how networks of autonomous sensors, edge devices and intelligent agents can collectively detect environmental changes and support evidence-based management of natural and managed ecosystems.
Robotics and autonomous intelligent systems
Pedro's research also considers how neuromorphic perception, edge AI and distributed intelligence can improve autonomous robotic systems. Applications include real-time perception, motion estimation, obstacle and collision prediction, autonomous navigation and coordination between multiple agents. The emphasis is on architectures capable of responding rapidly to dynamic environments while operating within constrained energy, computational and communication budgets.
Healthcare and intelligent sensing
Pedro contributes AI, machine perception and sensing expertise to interdisciplinary healthcare research. Areas of interest include intelligent rehabilitation, physiological and behavioural monitoring, assistive technologies, human activity analysis and secure data-driven systems. His contribution focuses particularly on translating multimodal sensor data into computational models that can support objective assessment, personalised intervention and clinical or professional decision-making.
Research-group leadership and collaboration
Pedro is Deputy Leader of NTU’s Circular Digital Transformation Research Group (CDTRG), which brings together expertise in artificial intelligence, digital technologies and sustainable systems to support the transition towards more circular and resource-efficient models. His contribution focuses particularly on the application of AI, intelligent sensing, edge computing and distributed systems to environmental monitoring, sustainable infrastructure and data-driven decision-making. Pedro is also a member of NTU’s Computational Intelligence and Applications Research Group (CIARG), where his work contributes to the development and application of artificial intelligence, machine learning, neuromorphic computing and intelligent systems. His research within the group focuses on translating computational-intelligence techniques into deployable technologies for areas including robotics, environmental monitoring, animal welfare, healthcare and autonomous sensing.
External engagement, professional service and policy contribution
Pedro contributes to the wider research, innovation and policy communities through peer review, editorial service, professional networks and evidence-based policy engagement. He is a member of the EPSRC, BBSRC and NERC Peer Review Colleges, contributing to the assessment of UK research funding proposals. He is also Co-Guest Editor of the special issue “Sustainable AI and AI for Sustainability: Efficient, Trustworthy and Responsible Intelligence for Real-World Impact” in Intelligent Computing, focusing on the development and application of efficient, trustworthy and responsible AI for real-world sustainability challenges. Pedro is an Associate Member of NeuroSYNC, the UK Multidisciplinary Centre for Neuromorphic Systems and Computing, contributing to the national community advancing neuromorphic systems, brain-inspired computation and energy-efficient AI. His policy engagement includes participation in DCMS–UPEN policy activities, engagement with the Parliamentary Office of Science and Technology (POST) and contributions to calls for evidence concerning AI, data governance and public-sector data re-use. Recent policy discussions have included data regulation in the age of AI and other data-intensive technologies, trustworthy data flows, and the marginal-cost restriction on public-sector data re-use, linking his technical expertise in AI, data systems and cyber risk with emerging questions in regulation and public policy.
Research and Impact Leadership
Pedro co-leads, with Dr Isibor Kennedy Ihianle, the REF 2029 Impact Case Study EviroBrain: Monitoring Life Above and Below Water. The case study brings together NTU research in artificial intelligence, intelligent sensing, computer vision, acoustics, edge computing and autonomous monitoring, with a focus on demonstrating how these technologies generate measurable environmental, societal and economic impact. His role includes helping to coordinate the impact narrative, consolidate evidence across projects and external partnerships, and demonstrate how research outputs are translated into practical applications for biodiversity monitoring, environmental management and animal welfare.
Income Generation
- RAEng - Leverhulme Fellowship: REACT – Retina-Emulated Anticipatory Collision and Tracking System funded through RAEng (PI) (£54,980) - [Sep 2026 ~ Aug 2027]
- 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
- RAEng - Leverhulme Fellowship: REACT – Retina-Emulated Anticipatory Collision and Tracking System funded through RAEng (PI) (£54,980) - [Sep 2026 ~ Aug 2027]
- 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) - [2024-2027]
Past projects
- 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 - 2026].
- 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]
PGR Activity
PhD supervision
- 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 Adrian Axinte (Director of Studies) - Human-in-the-Loop Process Mining for Secure, Interoperable Clinical Workflows with Biometric Verification. October 2026 ~ NOW
- Mr Jack Lewton (Co-Supervisor) – Using AI to monitor captive animal welfare. October 2024 ~ Now
- Mr JOSEPH ORIGBO (Co-Supervisor) - An interdisciplinary and inclusive co-designed solution for an autonomous musculoskeletal surgical platform. October 2024 ~ Now
- Mr Daniel Shabanianalavi (Co-Supervisor) - Advancing Health and Well-Being with Smart Sensing and Computational Intelligence. July 2025~ Now
- Mr Anthony Nonso Chimezie (Co-Supervisor) - Leveraging Artificial Intelligence and Process Mining to Enhance Governance and Compliance in Healthcare Sector: A Predictive and Explainable Approach. April 2026 ~ NOW
- Mr Ehtesham Hussain (Co-Supervisor) - Traceability-enabled Business Innovation: Pathways to Sustainable Competitive Advantage in Internationalised Leather SMEs in India and China - September 2026 ~ NOW
- Ms Zunaira Qadus (Co-Supervisor) - TBC - October 2026 ~ NOW
PhD Completions
- Dr Michael Gibbs (Co-Supervisor) – AI at the Edge for robotic development and applications. October 2022~May 2025
- Mr Feliciano Domingos (Director of Studies) - Elevating Internet of Things Devices for Underwater Communication Applications Employing Highly-Efficient Artificial Cognitive Devices – October2023 ~ August 2026 [Awating VIVA]
- Mr James Brereton (Co-Supervisor) – Wildlife Cryoconservation: Development of biobanking strategies to halt and reverse biodiversity loss. October 2024 ~ July 2026 [Awating VIVA]
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)
- Ms Zakia Turabee (2026)
External Examiner
- Dr Dominique Sanderson (Brunel University) (2025)
- Dr John Doherty (University of Ulster) (2024)
MPhil Vivas
- Mr Connor Farrell (Chair) - 2025
External activity
Pedro contributes to national and international research, innovation and professional communities in artificial intelligence, neuromorphic computing and intelligent systems. 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. He also contributes to peer review activities associated with EPSRC, BBSRC and NERC. His knowledge-exchange activities include externally funded collaborations spanning industry, academia and applied research. Current and recent programmes include the £575,000 Innovate UK AI-AFS project, the EPSRC-supported RehabAI project, Intel-supported research in neuromorphic intelligence, British Council activity in AI-enabled education, and the Royal Academy of Engineering / Leverhulme Trust REACT Fellowship. Pedro welcomes collaboration with organisations interested in neuromorphic computing, low-power AI, edge intelligence, machine perception, autonomous sensing, environmental monitoring and the translation of AI research into deployable technologies.
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.
