Dr Fani Deligianni
- Lecturer (Computing Science)
Dr Fani Deligianni’s holds a PhD in Medical Image Computing (Imperial College London), an MSc in Advanced Computing (Imperial College London), an MSc in Neuroscience (University College London) and a MEng (equivalent) in Electrical and Computer Engineering (Aristotle University, Greece).
Her PhD work was on augmenting 3D reconstructed models of the bronchial tree with 2D video images acquired during bronchoscopy. Bronchial deformation was modelled based on Active Shape Models (ASM) and a predictive tracking algorithm was incorporated to improve tracking of the endoscopic camera.
She was awarded an MRC Special Research Training Fellowship in Biomedical Informatics to explore links between structural connectivity as it is measured with Diffusion Weighted Imaging (DWI) and functional brain connectivity captured with resting-state (rs)-fMRI. She was based at the Biomedical Image Analysis group in Computing Department of Imperial College London. Her research work suggests a prediction framework to study the link between structural brain connectivity and functional brain connectivity.
She developed sophisticate computational approaches in machine learning, statistics and network analysis for the investigation of human brain structure and function. She applied her approach in functional data derived from simultaneous resting-state EEG-fMRI and microstructural indices obtained from neurite orientation dispersion and density imaging of the human brain. In particular, she uses graph theory, machine learning and statistics to describe and characterise complex interconnections between multi-modal brain networks.
Recently, her work has been focused on workload assessment based on neurophysiological signals. She has also done work on human motion analysis with wearable sensors and single rgb(d) camera.
She is part of the Information, Data & Analysis Section (IDA) section.
Fani Deligianni is the lead of the digital healthcare theme at the School of Computing Science. Her interests include:
- Medical image computing
- Statistical machine learning,
- Human Motion Analysis with wearable sensors
- Neuroimage analysis and neuroscience
- Brain Connectivity
- Human Machine Interaction
- Healthcare Informatics
For more information visit:
- Yola Jones (co-supervise with Pierpaolo Pelicori, John Cleland and Jeff Dalton)
- Tahani Aladwani (co-supervise with Christos Anagnostopoulos)
- Fransesco Dala Serra (co-supervise with Maciej Pajak/Canon Medical and Jeff Dalton)
- Philip McAdams (co-supervise with Mary Ellen Foster)
Research Assistant in Human Motion Analysis. This post suits particularly candidates that are interested to pursue a PhD in related area. (Deadline: 1st December - For more details email me with your CV)
Open PhD Positions:
Interested in a PhD at Glasgow University? It is required you have strong analytical skills. There are new funded PhD opportunities (Please email me with your CV and a project suggestion that is within my research interests):
- Funded PhD Scholarships: 'Artificial Intelligence in Modelling the Influence of Socio-Economic Factors on the Risk of Cardiovascular Events'
Deadline is on the 7th of January. Details for how to apply can be found here. (Please, contact me prior to your submission)
Lay summary: Barriers to education, housing, and a low income are associated with a high prevalence of cardiovascular risk factors and a greater risk of developing serious cardiac problems or die prematurely. Therefore, improving socio-economic factors might lead to better health and less health inequalities. We are a large multidisciplinary team of researchers, including experts in machine learning, cardiologists and statisticians. For this project, we will use a large set of anonymised healthcare records, such as blood tests, clinical appointments, and investigations that are routinely collected, every day in the West of Scotland. Advanced machine learning techniques will be developed to model interdependencies in longitudinal EHR and predict key adverse clinical events. We will investigate the relationship between socio-economic factors and the incidence and outcome of severe cardiovascular disease, including strokes, heart attacks, atrial fibrillation and heart failure This information will help doctors to target interventions that might prevent the onset or improve the outcome of serious cardiovascular disease, especially for those at the greatest socio-economic disadvantage.
The successful candidate is required to have prior knowledge in Machine Learning and/or a degree in related field.
- Aladwani, Tahani
Diagnosis of Diseases as Cloud Computing Service (DoDaaS)
- Object Oriented Software Engineering
- Professional Software Development and Team Project
Professional activities & recognition
Prizes, awards & distinctions
- 2019: IEEE BIBE (Paper Award)
- 2008 - 2011: MRC Training Fellow
- 2018 - 2020: Hamlyn Symposium on Medical Robotics