Professor Malcolm Macdonald

Electronic and Electrical Engineering

Personal statement

I am the Director of the Centre for Signal and Image Processing (CeSIP), and also the Director of the Applied Space Technology Laboratory (ApSTL) within CeSIP, which I founded and where I lead a team of staff and PGR students.

I am a Visiting Professor at the Centre for Space Research (C-Space), University College Dublin, Vice-Chair of the Space Technology Advisory Committee of the UK Space Agency, and an Associate Editor of the AIAA Journal of Guidance, Control and Dynamics. I am also a member of the Industry Advisory Board of Seraphim Space Investment Trust PLC, and a Non-Executive Director of Weather Stream Inc.

Previously I was the Founding Director of the Scottish Centre of Excellence in Satellite Applications, SoXSA (2014 - 2020), and a Non-Executive Board Member of UK Space Agency (2017 - 2020).

Supporting science and engineering communication to make it more easily accessible, especially to under-represented groups, I provide comment to national & international, specialist & mainstream media, across written word, audio, & visual.


Has expertise in:

    • Space Mission Analysis & Design
    • Space Technology
    • Astrodynamics
    • Swarm Engineering
    • Network Systems
    • Systems Engineering
    • Technology Roadmapping and Analysis
    • Technical Foresight & Horizon Scanning
    • Advanced Concepts
    • CubeSats
    • Modelling & Simulation

Prizes and awards

Galileo Masters EUSPA Space for FUN Challenge (2nd place)
2nd place in Copernicus Masters 2020: BMVI Transport Challenge
Finalist in E&T Innovation Awards 2020

More prizes and awards


Observability analysis of cooperative orbit determination using inertial inter-spacecraft angle measurements
Zhou Xingyu, Qin Tong, Macdonald Malcolm, Qiao Dong
Acta Astronautica (2023)
Correction: Czerkawski et al. Deep internal learning for inpainting of cloud-affected regions in satellite umagery. Remote Sens. 2022, 14, 1342
Czerkawski Mikolaj, Upadhyay Priti, Davison Christopher, Werkmeister Astrid, Cardona Javier, Atkinson Robert, Michie Craig, Andonovic Ivan, Macdonald Malcolm, Tachtatzis Christos
Remote Sensing Vol 14 (2022)
An assessment of high temporal frequency satellite data for historic environment applications. A case study from Scotland
McGrath Ciara N, Cowley David C, Hood Sine, Macdonald Malcolm, Clarke Sheila
Archaeological Prospection (2022)
A journey of exploration to the polar regions of a star : probing the solar poles and the heliosphere from high helio-latitude
Harra Louise, Andretta Vincenzo, Appourchaux Thierry, Baudin Frédéric, Bellot-Rubio Luis, Birch Aaron C, Boumier Patrick, Cameron Robert H, Carlsson Matts, Corbard Thierry, Davies Jackie, Fazakerley Andrew, Fineschi Silvano, Finsterle Wolfgang, Gizon Laurent, Harrison Richard, Hassler Donald M, Leibacher John, Liewer Paulett, Macdonald Malcolm, Maksimovic Milan, Murphy Neil, Naletto Giampiero, Nigro Giuseppina, Owen Christopher, Martínez-Pillet Valentín, Rochus Pierre, Romoli Marco, Sekii Takashi, Spadaro Daniele, Veronig Astrid, Schmutz W
Experimental Astronomy Vol 54, pp. 157-183 (2022)
Identifying effective sink node combinations in spacecraft data transfer networks
Clark Ruaridh, McGrath Ciara Nora, Macdonald Malcolm
Applied Network Science Vol 7 (2022)
Investigation of Very Low Earth Orbits (VLEOs) for global spaceborne Lidar
McGrath Ciara, Lowe Christopher, Macdonald Malcolm, Hancock Steven
CEAS Space Journal Vol 14, pp. 625-636 (2022)

More publications

Research interests

The Applied Space Technology Laboratory is addressing global challenges by working at the boundaries between disciplines to deliver a step-change in the democratisation, exploration, and exploitation of space.

  • Developing new space and adjacent sector technologies in response to global challenges and anticipated demands, accelerating the democratisation of the use of space, and the data and services it provides.
  • Developing technologies and algorithms to process the exponentially increasing data available from space to help us better understand our Earth, and to make this knowledge available to all.
  • Developing insights to network systems across the natural sciences to engineer vastly more connected, efficient, and sustainable space systems.

My research has an end-to-end focus on the development and application of systems by ensuring an understanding of the end application. Challenging conventional ideas and working at the interface between disciplines I seek to advance new concepts in the exploration and exploitation of space, for which I was awarded the Royal Society of Edinburgh Sir Thomas Makdougall Brisbane Medal. Specifically, my research develops concepts in, and applications of space technology, including solar sailing, nanosatellites, and constellations, by developing research into astrodynamics, networked systems, swarming, and distributed and collaborative systems.

Professional activities

Alignments in functional connectivity networks
Integration of an LED/SPAD Optical Wireless Transceiver with CubeSat On-board Systems
The New Peers Review podcast episode 3
The New Peers Review podcast episode 2
"New markets: Scottish space industry grows in value to £4bn"
The New Peers Review podcast pilot

More professional activities


SAfe Passage through Shifting Sands
Macdonald, Malcolm (Principal Investigator) Clemente, Carmine (Co-investigator) McKee, David (Co-investigator) Tachtatzis, Christos (Co-investigator) Clark, Ruaridh (Research Co-investigator)
01-Jan-2023 - 16-Jan-2024
An aUtonomous DistrIbuted Time signal in-Space
Macdonald, Malcolm (Principal Investigator) Clark, Ruaridh (Research Co-investigator) Lowe, Christopher (Research Co-investigator)
01-Jan-2022 - 30-Jan-2025
Matryoshka Orbital Networks
Macdonald, Malcolm (Principal Investigator) Clark, Ruaridh (Research Co-investigator) Lowe, Christopher (Research Co-investigator)
01-Jan-2022 - 31-Jan-2025
EPSRC IAA Bridging the Gap
Owens, Steven Robert (Principal Investigator) McGrane, Scott (Co-investigator) Allan, Grant (Co-investigator) Macdonald, Malcolm (Co-investigator)
EPSRC Impact Accelerator Account funded Bridging the Gap project, awarded jointly with Dr Steven Owens, Professor Malcolm MacDonald and Dr Grant Allan to investigate connecting environmental remote observation data with socioeconomic data to inventory opportunities for project creation and data access options
01-Jan-2022 - 01-Jan-2023
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
IAA BtG: A new window into autism spectrum disorder from space research
Clark, Ruaridh (Principal Investigator) Macdonald, Malcolm (Co-investigator) Lu, Szu-Ching (Co-investigator) Delafield-Butt, Jonathan (Principal Investigator) Macdonald, Malcolm (Co-investigator)
Impact Accelerator Account: Bridging the Gaps project.

Network and dynamical systems analysis, developed by Clark and Macdonald within EPSRC-funded research, has enabled advances in autonomous drone control, brain neuroimaging analysis, dynamical system monitoring, and most recently the design of space systems. This research provides an analytical framework for evaluating swipe patterns from a recently completed, and world leading, autism diagnostic clinical trial of 760 pre-school children.

Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting at least 700,000 individuals in the UK with an aggregate annual healthcare and support cost of at least £28 billion. Early identification, proceeded by therapeutic intervention, can produce significant, lifelong health and economic benefit. An ASD diagnosis currently requires a trained clinician, but there is a long and growing waiting list for such assessments. To meet demand, and create more accessible means of assessment, bespoke touchscreen games have been developed for early autism detection and recently trialled for children aged 3–6 years.

Touchscreen games provide a scalable alternative for detecting autism, with machine learning analysis able to detect autism with up to 93% accuracy from children’s motor patterns. Machine learning detects differences in user swipe interactions but cannot reveal the nature of these discrepancies, in particular how swipe patterns differ. By employing network analysis, we can identify – for the first time – the specific pattern signatures of autistic users, which will improve the detection of ASD and the accuracy in differentiating ASD from other neurodevelopmental disorders. We will explore how the development of children with neurodevelopmental disorders differs from their typically developed counterparts. Crucial insights that will form the basis of effective diagnosis, supporting and tailoring therapeutic interventions to address the massive economic impact of mis- or late diagnosis.
01-Jan-2022 - 01-Jan-2022

More projects