Dr Fani Deligianni
- Senior 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:
- EPSRC New Investigator Award on 'Privacy-Preserved Human Motion Analysis for Healthcare Applications', 2022-2025, PI. F. Deligianni
- Royal Society Semiflex grant on Human Motion and Privacy for Radar/Quantum Technology, 2022-2023, PI. F. Deligianni
- 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.
Informed Clinical Decision Making using Deep Learning: Students can enroll to the coursera MOOC here.
- Data Mining of Clinical Databases
- Deep Learning in Electronic Health Records
- Explainable Deep Learning Models for Healthcare
- Clinical Decision Support Systems
Vacancies (Closing date - 24th of April):
We are looking for postdoctoral researchers (Grade 6/7) to make a leading contribution to an EPSRC project on ‘Privacy-Preserved Human Motion Analysis for Healthcare Applications’ working with Dr. Fani Deligianni. The aim is to develop machine learning models that preserve privacy of the patients as well as their social cycle while they enhance their ability to identify and quantify abnormalities. The candidate should have experience in developing advanced deep learning generative models. It is also desirable to have previous experience with privacy and leakage of deep learning models. The project is also linked to collaborative projects with the School of Psychology and Neuroscience as well as the QUEST project. Along with other colleagues we aim to develop transformative healthcare technologies to enable patients’ independence, improve healthcare quality and ensure privacy. The successful candidate will also be expected to work with PhD students, collaborators, clinical researches and NHS as appropriate. Furthermore, he/she should contribute to the formulation and submission of research publications and research proposals as well as help manage this complex and challenging project as opportunities allow.
For more details email me (Dr. Fani Deligianni, firstname.lastname@example.org).
Submit your application via the UoG portal. Closing date: 24th of April.
- 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
- Jones, Yola
- Kaur, Narinder
- Long, Qianyu
Distributed Statistical Learning over Data Streams at the Network Edge
- Nguyen, Thuy Trinh
Advanced Deep Neural Networks for Sequence-based COVID-19 Cough Detection
- Szczepaniak, Dominik
Evaluating and Shaping Cognitive Training with Artificial Intelligence Agents
- 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
- 2022: IEEE Engineering in Medicine and Biology Society - Novel Sensing and Applications
- 2022: IEEE 8th World Forum on Internet of Things (WF-IoT)