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 Computing Technologies for 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)
- Long Qianyu (co-supervise with Christos Anagnostopoulos)
- Hester Huijsdens (Human Motion Analysis)
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 via the UKRI CDT in Socially Intelligent Artificial Agents:
- Evaluating and Shaping Cognitive Training with Artificial Intelligence Agents
- Modulating Cognitive Models of Emotional Intelligence
- Detecting Affective States based on Human Motion Analysis
- Deep Learning feature extraction for social interaction prediction in movies and visual cortex
- Bridging the Uncanny Valley with Decoded Neurofeedback
- Object Oriented Software Engineering
- Professional Software Development and Team Project
Professional activities & recognition
Prizes, awards & distinctions
- 2019: Best Paper Award (IEEE 19th International Conference on Bioinformatics and Bioengineering)
- 2020: Best Paper Award in Bioengineering (IEEE 20th International Conference on Bioinformatics and Bioengineering)
- 2008 - 2011: MRC Training Fellow
- 2018 - 2020: Hamlyn Symposium on Medical Robotics