Academic Team

Muhammad is a Professor of Communication Systems leading the Communications Sensing and Imaging research group at the University of Glasgow. He has been PI/Co-I on £10m+ research portfolio in the area of wireless communication systems and was awarded IEEE Comsoc’s Fred Ellersick award in 2014. He has 400+ publications, 30+ invited and keynote talks, and 30+ graduated Ph.D. students. He is an Associate Editor of IEEE Transactions on Communications and IEEE Access. He has invented innovative techniques for energy-efficient and self-organised wireless communication devices with recent work on using RF and wireless signals for activity monitoring.

Daniele is a Royal Academy Chair in Emerging Technologies and Fellow of the Royal Society of Edinburgh. He joined the University of Glasgow in 2017 as Professor in Quantum Technologies and is adjunct professor at the University of Arizona, Tucson (USA). He was awarded an ERC fellowship in 2012, the Philip Leverhulme Prize in Physics in 2015, the Royal Society of Edinburgh Senior Public Engagement medal and the Royal Society Wolfson Merit Award in 2017. His research focuses on the use of quantum technologies in novel optical imaging modalities, e.g. cameras that can see around corners, next generation remote-sensing LIDAR, fluorescence lifetime and novel deep-tissue functional imaging.

Emma is a Professor of Health Economics at the University of Glasgow. Emma is currently director of the £2M Global Health Group addressing the economic impacts of Arthritis in Tanzania and co-I on the £3M MRC SNAP Antimicrobial resistance project. She has worked with Cooper on EPSRC EP/K027611 on assessing the impact of novel diagnostics. Emma has worked on a large number of methodological and applied health economics projects covering diverse disease areas and specialties and brings expertise in health economic methodology in assessing the impact of clinical interventions.

Rebecca is Professor of Statistics at Lancaster University and a world-recognised expert in analysing time series data for abrupt or gradual changes through the use of changepoints. The PELT algorithm Rebecca developed is an exact yet computationally efficient O(n) approach to detecting multiple changepoints in univariate time series and is used worldwide in all sectors. Rebecca’s contributions to time-series methodology and analysis, including in the context of digital healthcare, and their proven ability to implement industrial-strength analysis software makes them uniquely suited for this project.

Frances Mair is the Norie Miller Professor of General Practice (GP) and Head of GP and Primary Care at the University of Glasgow. She sits on the MRC Clinical Research Training Fellowship Panel; has >180 papers (h-index 56), led the evaluation of the £37m DALLAS digital health programme, and is Co-I on the £3.6m SELFBACK H2020 project. Her focus is chronic illness and multimorbidity with an emphasis on digital health technologies. Her mixed methods research considers the wider socioeconomic environment and social context, bridging research and clinical practice. She leads the West Node of the National Research Scotland Primary Care Research Network and has successfully recruited >12K participants into trials. 

Katie is a Senior Clinical Research Fellow at the University of Glasgow, funded under The Stroke Association’s Esteemed Lectureship Programme. Her PhD was funded by a competitive CSO Fellowship. She has a track record involving collaborating with international colleagues e.g. at the Mayo Clinic Rochester. She sits on the National Advisory Committee for Stroke and the UK Stroke Forum Steering Committee.  Dr Gallacher works as GP partner 50% of her time and therefore has good insight into how research translates into clinical settings. She uses mixed methods research to conduct research focussed at improving the lives of older people and those with long-term conditions. She will also contribute to the understanding of ethics in patient monitoring.

Terence holds the post of Senior Clinical Lecturer and Honorary Consultant Physician at the University of Glasgow. He has published >100 research papers including (e.g. in BMJ, JAMA and NEJM). He is coordinating editor of the Cochrane Dementia Group; chairs the Stroke Psychology SIG of the World Federation of Neurorehabilitation and is founder of the Scottish Care-Home Research Group. As Consultant in Geriatric Medicine, Terry brings clinical expertise and access to patients through his own practice and through his extensive network of collaborators. As Vice-Chair of Ethics for the University’s medical college he also brings experience in assessing ethics and communicating biomedical ethics to a broad audience.

Aleksandra is a Senior Lecturer in Rehabilitation Engineering, specialising in neuro-engineering and neurorehabilitation with over 10 years of experience in clinical trials including usability studies. She has 9 graduated PhD students and is an associate editor in IEEE TNRE and Brain Computer Interfaces as well as a founding member of the International Brain-Computer Interface Society. She was awarded a RAE/Leverhulme Senior Research Fellowship to develop and test Brain computer interface therapies for home use. As a specialist in clinical brain-computer interface for motor and sensory (chronic pain) deficits and medical software design, she will lead the clinical validation of new technologies.

Roderick is a Professor in the School of Computing Science in Glasgow, where he heads the Inference, Dynamics and Interaction research group. He is PI of the £3M EPSRC grant “Closed-loop Data Science for Complex, Computationally-, and Data-Intensive Analytics” (EP/R018634/1). He has a track record of publication in top scientific venues and of achieving practical transfer to commercial products with companies such as Bang & Olufsen, Nokia, Microsoft, Samsung and Moodagent. His research brings together modern inference and machine learning techniques, dynamic systems, control theory, closed-loop data science and sensor-based interaction design, integrating novel sensors enhanced with machine learning to support of everyday living.