Dr Fani Deligianni

Fani Deligianni
PhD, MSc, MSc, MEng
Senior Lecturer at School of Computing Science, Glasgow University

News

Excited that as per 1st of August 2022, I am promoted to Senior Lecturer. I'm thankful to my students, colleagues and collaborators.
Delighted that my EPSRC New Investigator award and semi-flex Royal Society grants on Privacy-Preserved Human Motion Analysis for Healthcare Applications have been funded.
Our paper on Development of a semi-automated database for adult congenital heart disease patients, has been accepted for publication at the Canadian Journal of Cardiology.
Our paper on Delirium and the risk of developing dementia: a cohort study of 12 949 patients has been accepted for publication at the Journal of Neurology, Neurosurgery & Psychiatry.
Our paper on Improving Image Representations via MoCo Pre-Training for Multimodal CXR Classification has been accepted at MIUA, 2022.

Short Bio & Research Interests

Her research interests are in the field of Healthcare Informatics with emphasis on:

Medical Image Computing,
Statistical machine learning,
Human Motion Analysis with wearable sensors
Neuroimage analysis and Brain Connectivity
Human Machine Interaction

Fani Deligianni 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 modeled based on Active Shape Models (ASM) and a predictive tracking algorithm was incorporated to improve tracking of the endoscopic camera.

Fani is also a former member of the Centre for Brain and Cognitive Development (CBCD) at Birkbeck. She worked at CBCD from 2006 to 2008 and she developed a toolbox for contingent eyetracking, Talk2Tobii, to investigate the development of social skills in toddlers. The toolbox is used in several labs and is now integrated in T2T Package and SMART-T.

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. 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 AI and cognitive workload assessment based on neurophysiological signals. She has also done work on human motion analysis with wearable sensors and single rgb(d) camera.

Contact Details

Email: fadelgr at gmail dot com
Twitter: @fd301
ORCID
LinkedIn Profile
Publon Profile
Github Page