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:
Interested in a PhD at Glasgow University? There are four new funded PhD opportunities:
- How machine learning can help us answer questions about medical images based on both text and images. (This post has been covered)
- Evaluating and Shaping Cognitive Training with Artificial Intelligence Agents - CDT in Social Intelligent Artificial Agents (deadline for application is on 30th of June, 2020)
- Modulating Cognitive Models of Emotional Intelligence - CDT in Social Intelligent Artificial Agents (deadline for application is on 30th of June, 2020)
- Detecting Affective States based on Human Motion Analysis - CDT in Social Intelligent Artificial Agents (deadline for application is on 30th of June, 2020)
- Propose your own PhD project in any of the areas of my research interests. It is required you have strong programming skills and/or experience in deep learning (Deadline for project submissions are the 30th of June. Details for how to apply can be found here. Please, contact me to discuss topics and send along your CV.
- 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