Our AI for Healthcare Centre based at Imperial College London creates a unique environment for research and aims to focus on AI in healthcare which drives current AI research avenues such as interpretable AI, privacy-preserving learning, trust in AI, data-efficient learning, and safety in autonomy. Our healthcare application areas range from discovery research through diagnosis, imaging, precision medicine, clinical interventions, robotics, rehabilitation, public health, and long-term care.

Multi-disciplinarity is at the heart of all CDT research projects. PhD students work with a team of supervisors, each of whom brings complementary expertise to the project and the training programme. Research happens through high-quality projects with stretch and ambition in both AI and health research domains. The AI research is original in used methods and types of inputs or outputs tackled, and the majority of the projects have the potential to have an impact post-PhD.

Each CDT student has at least two supervisors – one AI supervisor and one Healthcare supervisor who are normally from two different Imperial College Faculties or Departments. The true cross-disciplinary nature of the projects is represented in the large pool of supervisors which currently comprises most Imperial College Engineering departments including Computing, Electrical and Electronic Engineering, Mathematics, Bioengineering, Mechanical Engineering, Dyson School of Design Engineering and all 8 departments of the Imperial College Faculty of Medicine. Additional healthcare supervisors can be from clinicians in the Imperial College Healthcare NHS Trust or Royal Marsden NHS Trust.

Our Clinical PhD Fellows

Sumeet Hindocha, MD
(PhD awarded 2023)

Project:
RadiotherapAIsER: Radiotherapy AI
to see Early Recurrence

Supervisors:
Eric Aboagye
Richard Lee

Adam Marcus, MD

Project:
ABCD: Acute Brain
CT Deep-learning

Supervisors:
Daniel Rueckert
Paul Bentley

Myura Nagendran, MD

Project:
Explainable AI in Critical Care

Supervisors:
Aldo Faisal
Anthony Gordon

Simon Williamson, MD

Project:
Developing neuromorphic AI-based closed-loop control brain stimulation to treat Alzheimer’s disease

Supervisors:
Pedro Mediano
Nir Grossman
Christopher Butle

Kavitha Vimalesvaran, MD

Project:
A real-time AI approach to improve the efficiency and quality of image acquisition

Supervisors:
Anil Bharath
Graham Cole

Sameer Zaman, MD
(PhD awarded 2023)

Project:
Machine learning to predict death from cardiac MRI images and reports

Supervisors:
Nick Linton
Darrel Francis

Samuel Channon-Wells, MD

Project:
Development of a new data driven molecular taxonomy of infectious and inflammatory disease

Supervisors:
Mauricio Barahona
Jethro Herberg


All our Doctoral Researchers


Cohort 1

James Batten

Project:
Automating the co-registration between CTA-derived coronary artery geometry and X-ray Angiography

Supervisors:
Ben Glocker
Declan O’Regan
Heartflow

Digby Chappell

Project:
Using machine learning and augmented reality to improve prosthetic hand control

Supervisors:
Nicolas Rojas
Petar Kormushev
Fernando Bello

Sumeet Hindocha, MD
(PhD awarded 2023)

Project:
RadiotherapAIsER: Radiotherapy AI to see Early Recurrence

Supervisors:
Eric Aboagye
Richard Lee

Annalaura Lerede

Project:
Developing machine learning methods for deriving individually optimised markers of pathology for people with Multiple Sclerosis

Supervisors:
Adam Hampshire
Richard Nicholas

Rachel Lee Mekhtieva

Project:
Cross-Modal Deep Learning with Symbolic Reasoning for Patient-Centric Personalised and Precision Healthcare and Clinical Decision Support

Supervisors:
Alessandra Russo/Dalal Alrajeh
Brendan Delaney

Benjamin Post, MD

Project:
AI-based identification of serious acute illness risk from routine primary care data

Supervisors:
Aldo Faisal
Stephen Brett

Margherita Rosnati

Project:
Predicting outcomes following traumatic brain injury (TBI) using deep learning

Supervisors:
Ben Glocker
David Sharp

Ruby Sedgwick

Project:
Using Bayesian inference to guide the experimental design and interpretation of a novel amplified molecular assay

Supervisors:
Ruth Misener
Mark van der Wilk
Molly Stevens

Reneira Seeamber

Project:
To develop a method to perform self-directed back exercises using novel wearable sensors, haptics and interactive mobile exercise-games

Supervisors:
Ravi Vaidyanathan
Paul Bentley

Alina-Irina Serban

Project:
Improving Life Satisfaction in Independent Living: An Intelligent IoT approach

Supervisors:
Rafael Calvo
David Sharp

Kavitha Vimalesvaran, MD

Project:
A real-time AI approach to improve the efficiency and quality of image acquisition

Supervisors:
Anil Bharath
Graham Cole

Sameer Zaman, MD
(PhD awarded 2023)

Project:
Machine learning to predict death from cardiac MRI images and reports

Supervisors:
Nick Linton
Darrel Francis


Cohort 2

Ioannis Afentakis

Project:
Machine Learning Algorithms for Diabetes Management

Supervisors:
Pantelis Georgiou
Nick Oliver

Asem M. Alaaeldin Abdelaziz

Project:
Data Science for Cancer Pathway Analysis

Supervisors:
Mauricio Barahona
Erik Mayer

Nur Aizaan Binti Anwar, MD

Project:
Using longitudinal speech samples to predict disease recurrence in patients with brain tumours

Supervisors:
Lucia Specia
Matthew Williams

Doga Basaran

Project:
Characterisation of Multiple Sclerosis Lesions with Image Modality Transfer and Graph-Based Learning

Supervisors:
Wenjia Bai
Paul Matthews

William Bolton

Project:
Addressing multi-morbidity in the NHS: Optimising antibiotic therapy in obesity using intelligent, personalised clinical decision support systems

Supervisors:
Pantelis Georgiou
Alison Holmes

Xavier Cadet

Project:
Multi-modal machine learning of large-scale genetic, biologic, and brain imaging data for identification of therapeutic targets and early prediction biomarkers for dementia

Supervisors:
Hamed Haddadi
Sara Ahmadi Abhari

William Dudley

Project:
Human not monkey robotics: Neuro-inspired object manipulation with dextrous hands

Supervisors:
Aldo Faisal
David Franklin

Ariane Duverdier

Project:
AI-based design of personalised treatment strategies for eczema and allergic march

Supervisors:
Reiko Tanaka
Adnan Custovic

Stefano Falini, MD

Project:
Understanding and Embracing Uncertainty in Clinical Decision Making

Supervisors:
Lucia Specia
Anthony Gordon

Paul Festor

Project:
Learning clinical decision support systems in acute care settings

Supervisors:
Aldo Faisal
Matthieu Komorowski

Christoforos Galazis

Project:
Prediction of stroke and heart failure risk using dynamic CINE MRI and deep learning of cardiac structure and function

Supervisors:
Anil Bharath
Marta Varela Anjari

Agnese Grison

Project:
Wearable Interfaces for the 21st Century: Subcutaneous high-density electrode arrays to connect with the human spinal cord

Supervisors:
Dario Farina
Stephen Wang

Annika Guez

Project:
Sensory Motor Interface for Lower extremity Exoskeletons (SMILE)

Supervisors:
Ravi Vaidyanathan
Klaus Drechsler

Megan Hutchings

Project:
Unsupervised Machine Learning for the Inference and Rehabilitation of Cortical Injury in Stroke Patients

Supervisors:
Dario Farina
Paul Bentley

Adam Marcus, MD

Project:
ABCD: Acute Brain CT Deep-learning

Supervisors:
Daniel Rueckert
Paul Bentley

Myura Nagendran, MD

Project:
Explainable AI in Critical Care

Supervisors:
Aldo Faisal
Anthony Gordon

Amr Nimer, MD

Project:
Behaviour Analytics to quantify Neurosurgical Skill to Enhance Training and Economy of Movements

Supervisors:
Aldo Faisal
Dipankar Nandi

Edoardo Occhipinti

Project:
Hearables: AI for in-ear doctorless cardiovascular care in the community

Supervisors:
Danilo Mandic
Nicholas Peters

William Plumb

Project:
Understanding and optimising patient journeys within an integrated care system

Supervisors:
Giuliano Casale
Alex Bottle

Neophytos Polydorou

Project:
A Virtual Reality environment with emotion recognition and coaching capability for psychotherapy of depression and anxiety

Supervisors:
Abbas Edalat
Dasha Nicholls

Hadrien Reynaud

Project:
Medical image acquisition guidance with applications in free-hand ultrasound

Supervisors:
Bernhard Kainz
Paul Leeson

Joshua Southern

Project:
Computational personalisation and generation of health-promoting hyper foods using graph deep learning

Supervisors:
Michael Bronstein
Kirill Veselkov

Michael Tanzer

Project:
Artificial Intelligence enabled highly efficient Diffusion Tensor Cardiac Magnetic Resonance

Supervisors:
Daniel Rueckert
Sonia Nielles-Vallespin
Guang Yang

Michael Thornton

Project:
Decoding selective attention to speech for a mind-controlled hearing aid

Supervisors:
Danilo Mandic
Tobias Reichenbach

Alistair Weld

Project:
Confidence-driven Robotic Ultrasound Tissue Scanning for Surgical Resection Guidance

Supervisors:
Stamatia Giannarou
Alin Albu-Schaeffer

Kai Zhang

Project:
Developing interactive explanatory models for cancer prognosis

Supervisors:
Francesca Toni
Matthew Williams

Blanka Zicher

Project:
Learning to control individual motoneurons with AI-based decoded spinal cord output

Supervisors:
Dario Farina
Dan Wetmore


Cohort 3

João Binenbojm De Sa Pereira

Project:
Closed-loop brain stimulation for induction of targeted functional brain plasticity in neurological patients

Supervisors:
Dario Farina
Paul Bentley

Lucille Cazenave

Project:
AI Therapist: Personalised Data Driven Robot-Assisted Neurorehabilitation

Supervisors:
Etienne Burdet
Paul Bentley

Dimitar Georgiev

Project:
Deep learning for cerebral organoid phenotyping and drug discovery

Supervisors:
Mauricio Barahona
Molly Stevens

Simon Hanassab

Project:
Using AI to improve decision making during in vitro fertilisation (IVF) treatment

Supervisors:
Thomas Heinis
Waljit Dhillo

Alexander Jenkins

Project:
Individualising patient treatment for atrial fibrillation based on the fibrillation mechanism and electrophenotype

Supervisors:
Danilo Mandic
Fu Siong Ng

Federico Nardi

Project:
Data-Driven Neuroscience for Diagnostics & Neurorehabilitation of Parkinson’s with Virtual Reality

Supervisors:
Aldo Faisal
Shlomi Haar

Alexander Ranne

Project:
AI-Driven Robotic Catheter System for Ultrasound-Guided Endovascular Surgery

Supervisor:
Ferdinando Rodriguez y Baena
Nassir Navab

Avish Vijayaraghavan

Project:
Multi-modal deep learning and domain knowledge integration to aid multidisciplinary teams in diagnosing in idiopathic pulmonary fibrosis

Supervisors:
Joram M. Posma
Philip Molyneaux

Weitong Zhang

Project:
Motion-Corrected Quantitative Body MRI (MoCoQ)

Supervisors:
Bernhard Kainz
Dimitrios Karampinos



Cohort 4

Pauline Bourigault

Project:
Pose Estimation of body motion for brain disease Diagnosis (DPosED)

Supervisors:
Danilo Mandic
Barry Seemungal

Samuel Channon-Wells

Project:
Development of a new data driven molecular taxonomy of infectious and inflammatory disease

Supervisors:
Mauricio Barahona
Jethro Herberg

Sarah Cechnicka

Project:
Deep Learning–Based Segmentation and Classification for Outcome prediction in Kidney Transplant

Supervisors:
Bernhard Kainz
Candice Roufosse

Cosima Graef

Project:
AI System for Optimal Selection of Deep Brain Stimulation (DBS) Parameters

Supervisors:
Ravi Vaidyanathan
Yen F. Tai
Shlomi Haar



Kevin Horeau

Project:
Data-driven Digital Twins for the discovery of the link between shopping behaviour and onset of cancer

Supervisors:
Aldo Faisal
James Flanagan

Ruoyu Hu

Project:
An AI platform for learning to laugh: New approach to develop a sense of humour

Supervisors:
Abbas Edalat
Dasha Nicholls


Edison Liu

Project:
Reinforcement learning based management and control of paediatric intensive care ventilation

Supervisors:
Aldo Faisal
Padmanabhan Ramnarayan


Nina Merino Miralles

Project:
Inferring states of the central nervous system from muscle signals to study neural degeneration

Supervisors:
Dario Farina
Paul Strutton

Nina Moutonnet

Project:
Automatic seizure detection and monitoring in hospitalised patients using artificial intelligence

Supervisors:
Danilo Mandic
Gregory Scott

Georgios Papadopoulos

Project:
Deep learning for hyperplexed biosensor diagnostics


Supervisors:
Mauricio Barahona
Molly Stevens

Giulia Sanguedolce

Project:
Automated community assessment of aphasic stroke (The ACOUSTICS study)

Supervisors:
Patrick Naylor
Fatemeh Geranmayeh

Niro Yogendran

Project:
Radar passive monitoring of movement symptoms in Deep Brain Stimulation patients

Supervisors:
Tim Constandinou
Shlomi Haar
Yen F. Tai



Cohort 5

Barbora Barancikova

Project:
Machine learning algorithms to model brain tumour patients’ dynamics using rough analysis

Supervisors:
Matthew Williams
Christopher Salvi
Seema Dadhania

Nicolas Calvo Peiro

Project:
Using AI for finding digital biomarkers for closed-loop deep brain stimulation

Supervisors:
Shlomi Haar
Anastasia Borovykh
Yen Tai

Daolong Chen

Project:
BehaviourGPT: Large Behaviour Models to measure health state in neurological diseases

Supervisors:
Aldo Faisal
Richard Festenstein

Connor Daly

Project:
Intra-Operative Sensing for Human-Robot Interaction in Neurosurgery

Supervisors:
Ferdinando Rodriguez Y Baena
Daniel Elson
Jinendra Ekanayake

Oskar Fraser-Krauss

Project:
Dynamic graph machine learning for early detection and characterisation of antimicrobial resistant outbreaks from acute care data

Supervisors:
Mauricio Barahona
Alison Holmes

Adam Gould

Project:
Arguing with physical activity data from a real-world wearable clinical trial with patients with a primary brain tumour

Supervisors:
Francesca Toni
Matthew Williams
Seema Dadhania

Isaac Hayden

Project:
Artificial neural networks for carboplatin dose optimisation for children with cancer

Supervisors:
Anil Bharath

Leo Huang

Project:
Automated evaluation of eczema severity scores for any skin colour in clinical and research practice in children and adolescents

Supervisors:
Reiko Tanaka
Claudia Gore
Adnan Custovic

Oliver Pitts

Project:
Physics informed machine learning to improve imaging-based quantification of airway structural and functional heterogeneities in lung disease

Supervisors:
Rossella Arcucci
Anand Shah
Salman Siddiquie

Jonathan Rubin

Project:
Lineage Inference and lineage-regularized embedding for studying metastatic cancer evolution during therapy

Supervisors:
Nick Jones
Trevor Graham

Marco Visentin

Project:
Trustworthy Machine Learning for Thyroid Image Analysis and Diagnosis

Supervisors:
Wenjia Bai
Aimee Di Marco
Neill Tolley

Simon Williamson, MD

Project:
Developing neuromorphic AI-based closed-loop control brain stimulation to treat Alzheimer’s disease

Supervisors:
Pedro Mediano
Nir Grossman
Christopher Butler

Fiona Kekwick

Project:
Robust, Trustworthy and Efficient AI for Clinical Neuroimaging Data Analysis

Supervisors:
Wenjia Bai
Paul Matthews


Our Affiliates

Patrik Bachtiger, MD

Project:
Point-of-care (digital stethoscope) diagnosis of heart disease using artificial intelligence applied to single-lead ECG and heart sounds

Supervisor:
Nicholas Peters

Finneas Catling, MD

Project:
Early diagnosis and improved treatment of critical illness, using methods from Bayesian statistics and machine learning

Supervisor:
Stephen Brett

Manish George, MD

Project:
Artificial Intelligence Stratification of Thyroid
Ultrasound

Supervisors:
Lord Ara Darzi
Neil Tolley

Anenta Ramakrishnan, MD

Project:
Cardiovascular trials
and epidemiology

Supervisors:
Anil Bharah
Jamil Mayet
Peter Weinberg

Arunashis Sau, MD

Project:
Artificial intelligence to
identify mechanisms an
electrophenotypes of atrial
arrhytmias

Supervisors:
Danilo Mandic
Fu Siong Ng
Nicholas Peters