Michael P. J. Camilleri
ACRC Research Fellow
- AIAI, School of Informatics, College of Science and Engineering
- CHAI Hub, School of Engineering, College of Science and Engineering
Contact details
- Email: michael.p.camilleri@ed.ac.uk
Address
- Street
-
IF 5.03, Informatics Forum
10, Crichton Street - City
- Edinburgh
- Post code
- EH8 9AB
Background
I obtained a B.Sc. in Communications and Computer Engineering (University of Malta, 2011), before coming to Edinburgh to study for an M.Sc. in Artificial Intelligence and Robotics (2012). Subsequently I worked in industry, returning to the University of Edinburgh in 2017 for a PhD in Data Science, supervised by Prof. Chris Williams. My Doctoral thesis centred around characterising the coupled and hierarchical nature of social interactions of group-housed lab mice. This also involved a collaboration with Prof. Andrew Zissermann and the VGG group at Oxford towards tracking and behaviour recognition from video. I subsequently gravitated towards Medical/Health Informatics, notably through my contribution on the SCANDAN project (prediction of all-cause dementia from clinical MRI on Scottish national data).
Apart from a strong technical background in probabilistic modelling and deep learning, I have experience in various applied fields, including Robotics, Transport Modelling, Radio-Telescopes, Behaviour Modelling and above all Medical Informatics. I have worked across Industry and Academia, and in various disciplines, giving me a strong collaborative network that expands the breadth of my work. More information is available on my personal website.
On the personal side, I am a full-time husband/father and enjoy volunteering at heritage railways (particularly Steam Locomotives).
Qualifications
Ph.D. in Data Science, University of Edinburgh, 2023
M.Sc. in Artificial Intelligence, University of Edinburgh, 2013
B.Sc. ICT (Hons) in Communications and Computer Engineering, University of Malta, 2011
Responsibilities & affiliations
ACRC Fellow (School of Informatics)
CHAI Scholar (School of Engineering)
Open to PhD supervision enquiries?
Yes
Current PhD students supervised
Mr Xingchen Zhai (Precision Medicine DTP)
Research summary
I develop probabilistic models for multimodal longitudinal clinical data from real-world health-care, with a focus on brain health. My work centres on disease progression modelling using electronic health records, clinical recordings, and medical imaging, often within Trusted Research Environments.
Current research interests
Probabilistic Longitudinal Modelling Causality from Observational Data (Electronic Health Records) Computer Vision for Medical Imaging, Prediction of NeurodegenerationKnowledge exchange
I prioritise working with real-world data especially health data that is collected in routine clinical settings. While this makes the tasks more challenging, it ensures that the data is already representative of what is already able to be collected, ensuring a faster path to deployment.