Skip to content
Pedro Miguel Baptista Machado

Pedro Machado


Computer Science

Staff Group(s)
Computer Science


Pedro Machado received his MSc in Electrical and Computers Engineering from the University of Coimbra (2012) and his Ph.D. in Computer Science from Nottingham Trent University (2021).

Dr Machado is a Senior Lecturer in Computer Science and Course Leader for MSc Artificial Intelligence, first secretary for the IEEE Systemic Innovation Special Interest Group (SISIG) and the Events Coordinator for the BCS Nottingham and Derby branch.

Pedro’s expertise includes Neuromorphic Engineering, Edge Computer Vision, Bio-inspired Computing, robotics and Intelligent Sensors. His research interests in computer science are in retinal cell understanding, biological nervous system modelling, spiking neural networks, robotics and autonomous systems, and neuromorphic hardware.

Career overview

  • Research Associate with the University of Ulster (August 2013 - September 2014)
  • Research Associate with the University of Coimbra, Portugal (April 2012 - July 2013 and September 2009 - December 2009)
  • Research Associate with the Instituto Pedro Nunes, Portugal (January 2010 - March 2012).

Research areas

Pedro is a member of the Computational Intelligent and Applications group.

Research interests:

Modelling of Computational Models of the Retina, Neuromorphic Hardware, ADAS and AGVs, grasping, in-hand manipulation, hand-over algorithms. Robotic tactile and visual perception for completing challenging manipulation tasks, delicate with applications in industry and healthcare.

Keywords: OS SNN, Linux, OpenCV, OpenCL, ROS, PYNQ, Vitis AI, Computational Neurosciences, Edge Computing, Cognitive Robotics, Robotic Grasping, Visual Perception, Tensorflow, Keras, Python, C/C++, distributed computing.

PhD supervision:

Mr Dennis Monari (Director of Studies) - 2023 ~ Now

Mr Michael Gibbs (Supervisor) - 2022 ~ Now

Active Research Projects:

Internet of Water (IoW) (PI) [August 2022 - July 2023]

Underwater Plastic Detection (PI) [January ~ July 2022]

Plastics thrown away by humans are normally transported to the oceans by rivers. It is not clear how much plastic is transported every year from land to seas because of lack of metrics and standard monitorisation techniques. One of the approaches is on the monitorisation of plastics floating on the water surface. Only a small part of the plastic waste in rivers and oceans floats on the surface, the rest sinks to deeper waters or to rivers/oceans floor, threatening the local flora and fauna. So far, there is no way of detecting plastic at the bottom of rivers/oceans on a large scale. Traditional monitoring methods, in which divers manually collect image data along lines or taut cords (so-called transects) only allow for assertions about very limited areas. In addition, these methods are highly time-consuming, expensive and with very limited results. Generally, such methods neither provide georeferenced data that can be used to find locations again, for example to recover plastic or check its condition. A Xilinx Versal ACAP will be used to accelerate the AI inference algorithm at the edge.

In this research project, we propose a non-evasive approach where an underwater drone is used to collect visual data that will be georeferenced to assess how macro-plastics are transported underwater, how much of that plastic is deposited in the bottom of the Trent River and how those plastics affect the ecosystem. The multidisciplinary team involved in this project will analyse data from different perspectives and use the findings to prepare and submit a research proposal to BBSRC/UKRI to further explore and create understanding how much plastic is carried underwater, how much of the plastic deposits on the Trent’s River soil and how it affects the ecosystem. This research project aims to develop a proof-of-concept validator.


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:

  • COMP20091: Systems Software.
  • SOFT40051 - Advanced Software Engineering.
  • SOFT37001: Advanced Analysis and Design (DTS).

Pedro is currently teaching as Lab Tutor/Supervisor:

  • SOFT27002: Software Engineering (DTS).
  • COMP40321: Research Methods | COMP40311: Major Project
  • COMP30151: Full Year project
  • SOFT27001: Software Design and Implementation (DTS).

Pedro's teaching experience includes:

  • 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)



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. ISSN 1936-7406

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 all of Pedro Machado's publications...

Course(s) I teach on