Personal statement
A professional space technology engineer, academic & director with a strong, proven and international record of accomplishment. I am the Director of the Scottish Centre of Excellence in Satellite Applications, and a Non-Executive Board Member of the UK Space Agency.
I was awarded the 2016 Royal Society of Edinburgh Sir Thomas Makdougall Brisbane Medal in recognition of my “outstanding research work in the development and application of space mission systems to challenge conventional ideas and advance new concepts in the exploration and exploitation of space.”
Distinctively for an engineer, my publications are in journals such as Scientific Reports, and Physical Review E, as well as top-ranking engineering journals. I also led the development of “The International Handbook of Space Technology”, which has sixty contributing authors, including high-profile contributors from Japan, the USA and Europe.
I was the only non-US member of a National Academies of Sciences, Engineering, and Medicine’s committee on ‘Achieving Science Goals with CubeSats’, and I am one of only two European Associate Editors of the Journal of Guidance, Control and Dynamics, the top-ranked archival journal in Aerospace Engineering. I am a member of Committee on Space Research (COSPAR) Study Group on ‘Small Satellites for Space Sciences’, as well as providing expert advice to, amongst others, the Institute for Defense Analyses, Science and Technology Policy Institute, based in Washington, D.C.
Research interests
Distinctively my work spans both the upstream space sector (building and operating spacecraft), and downstream space sector (the services and data that come from the spacecraft). With a focus on the end-to-end development and application of space mission systems my work enables new space-derived data product concepts through advances in space technology.
My specific interests are in the use of advanced concepts, such as solar sailing, and multi-spacecraft platforms to enable new space services through the application of concepts from networked systems and swarm engineering, combined with astrodynamics and space system design. The recent development of small, low-cost spacecraft has led to increased interest in deploying large or even very-large constellations of spacecraft to enable new space-derived datasets and services. To-date, this remains challenging due to the limited resources on-board such platforms coupled with the limited payload capacity. By spanning the up and downstream my fundamental research is developing the means to maximise the performance of these resource-limited, low-cost platforms to enable radical enhancements of, or completely new space-derived services and data. As such my research seeks to develop 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
- Contributor
- 8/11/2022
- Integration of an LED/SPAD Optical Wireless Transceiver with CubeSat On-board Systems
- Contributor
- 28/9/2020
- The New Peers Review podcast episode 3
- Speaker
- 10/12/2019
- The New Peers Review podcast episode 2
- Speaker
- 3/12/2019
- "New markets: Scottish space industry grows in value to £4bn"
- Interviewee
- 15/3/2019
- The New Peers Review podcast pilot
- Speaker
- 21/2/2019
More professional activities
Projects
- 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 - 15-Jan-2022
- 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
- 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
Address
Electronic and Electrical Engineering
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