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 Alison ONeil/Canon Medical and Jeff Dalton)
- Long Qianyu (co-supervise with Christos Anagnostopoulos)
- Fatima Ghanduri (co-supervise with Christos Anagnostopoulos)
- Narinder Kaur (co-supervise with Pierpaolo Pelicori, John Cleland)
- Muhammet Alkan (co-supervise with Ke Yuan)
- Elizabeth Jacobs (co-supervise with Frank Pollick)
- Nicole Lai (co-supervise with Marios Philiastides)
- Dominik Szczepaniak (co-supervise with Monika Harvey)
- Samuel Leighton (co-supervise with Jonathan Cavanagh, Rajeev Krishnadas)
- Qianying Liu (co-supervise with Christos Anagnostopoulos)
Funding and Collaborations:
- UKRI CDT on Socially Intelligent Artificial Agents with three PhD Scholarships
- Coursera course on Deep Learning for Clinical Decision Systems based on EHR, 2020-2021, PI. F. Deligianni
- 2021 Summer Scholarship, School of Computing Science, University of Glasgow (Matthew Malek-Podjaski)
- Human Data Interaction EPSRC Network - Human Motion Analysis – Agency, Negotiation and Legibility in Data Handling (EPSRC EPR0451781), PI Dr. Fani Deligianni, 2020-2021.
- CogniHealth is a healthcare technology company, which support people with dementia and their families.
- CanonMedical with funding for an EngD student via the CDT on Photonics.
Distinguished Students' Projects:
- Machine Learning Applications for the Detection and Disentanglement of Emotional States in Human Motion, 2020-2021, Matthew Malek-Podjaski. (This project is linked to Explainable, Privacy-Preserved Human Motion Analysis)
- Development of Machine Learning Models to Detect Arrhythmia Based on ECG Data-Interpretability, 2020-2021, Shourya Verma.
- Data Mining of Clinical Databases
- Deep Learning in Electronic Health Records
- Explainable Deep Learning Models for Healthcare
- Clinical Decision Support Systems
- Aladwani, Tahani
Diagnosis of Diseases as Cloud Computing Service (DoDaaS)
- Dalla Serra, Francesco
Answering Questions about Medical Images
- Ghanduri, Fatima H M
Predictive Intelligence of Interpretable Models in the Financial Domain
- Kaur, Narinder
- Long, Qianyu
Distributed Statistical Learning over Data Streams at the Network Edge
- 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)
- 2021: Best Runner Up Award - IEEE Brain (IEEE Symposium Series on Computational Intelligence)
- 2008 - 2011: MRC Training Fellow
- 2018: Hamlyn Symposium on Medical Robotics
- 2018: Workshop on BCI and Human AI augmentation - HSMR
- 2019: Hamlyn Symposium on Medical Robotics
- 2019: Workshop on BCI and Human AI augmentation - HSMR
- 2021: Computational Intelligence for Brain Computer Interfaces at IEEE SSCI