Scholar
Marwa Mahmoud
Google Scholar ID: mYyG4p0AAAAJ
Associate Professor - University of Glasgow / Visiting Fellow - University of Cambridge
Citations & Impact
Citations
1,424
H-index
20
i10-index
26
Publications
20
Co-authors
14
list available
Contact
Publications
Resume (English only)
Academic Achievements
- She is the President of the Association for the Advancement of Affective Computing (AAAC), Network Member of Cambridge Trust & Technology Initiative, and a member of Cambridge Neuroscience. She has previously served as topic editor for 'Multimodal Behavioural AI for Wellbeing' issue in Frontiers in Computer Science and the special issue 'AI and Interaction Technologies for Social Sustainability' in the Journal of Sustainability.
Research Experience
- Before joining the University of Glasgow, she spent 10 years at the University of Cambridge. She is currently the director of the “Behavioural AI Lab – for human and animal behaviour modelling”, with a vision to build technologies for ‘AI for Social Good’, combining computer vision research and multimodal machine learning for human well-being and animal welfare applications.
Education
- PhD from the University of Cambridge in 2015; Postdoctoral Researcher at the University of Cambridge from 2015-2016; Awarded the prestigious King’s College Junior Research Fellowship (JRF) from 2016-2021.
Background
- Dr. Marwa Mahmoud is a Senior Lecturer (Associate Professor) in Socially Intelligent Technologies in the School of Computing Science at the University of Glasgow, and a Visiting Fellow in the Department of Computer Science and Technology at the University of Cambridge, UK. Her research interests focus on computer vision for social signal processing, multimodal perception and behavioural modelling, especially within the context of affective computing, behaviour analytics, human behaviour understanding, and animal behaviour understanding.
Miscellany
- She pioneered research directions on affective gestures analysis (especially hand-over-face gestures and self-adaptors/fidgeting behaviour) as well as vision-based AI for automatic detection of signs of pain in the facial expressions of sheep for early diagnosis of disease.
