Earlier Cohorts
Our cohort-based approach to doctoral training fosters an inclusive and supportive environment, promoting networking and peer learning opportunities.
We have welcomed 20 students in our 2024 cohort, and 18 in our recent 2025 intake. Click on their profiles below to find out more about their research interests and their chosen EPSRC DRIVE-Health PhD project.
Cohort 2023
| Project No | Student Name | Project Title | Supervisors |
|---|---|---|---|
| 1 | IN3-001: Evaluating the impact of medication side effects and increasing their recognition in clinical practice | Professor Robert Stewart / Dr Christoph Mueller | |
| 2 | IN3-002: Machine learning in electronic health records for prognosis and diagnosis of rare neurological disorders | Professor Richard Dobson / Dr Zina Ibrahim | |
| 3 | CO4-004: Identification of modifiable risk factors for increased health care use in people with dementia and developing opportunities for intervention | Dr Latha Velayudhan / Dr Christoph Meuller | |
| 4 | CO4-010: Feasibility and acceptability of speech and mood data collection in daily life after traumatic brain injury (TBI) using digital technology | Dr Nicholas Cummins / Dr Sara Simblett / Professor Dame Til Wykes | |
| 5 | CO4-011: Enhancing psychosis prevention through dynamic refinement of a clinical prediction model using machine learning | Professor Daniel Stahl /Professor Paolo Fusar-Poli /Dr Dominic Oliver | |
| 6 | CO4-023: Predicting Outcomes for Infants with Early-Onset Epilepsy: Combining mother and baby Brain and Health Data | Dr Charlotte Tye / Dr Michael Absoud | |
| 7 | CO4-026: Dissecting the Epigenetic Basis of Eating Disorders in EDGI and Nanopore DNA Methylation Sequencing Data | Dr Chloe Wong /Professor Gerome Breen | |
| 8 | CO4-027: Identifying Drug Repositioning Opportunities Through Leveraging the Core Neurogenetics of Major Depressive Disorder | Professor Gerome Breen / Dr Jonathan Coleman | |
| 9 | CO4-033: Using deep phenotyping informatics to map the prevalence, process and outcomes of people with neuropsychiatric disorders | Professor James Teo / Professor Mark Edwards | |
| 10 | CO4-036: Flexible machine learning models to capture the dynamics of patient outcomes at scale: learning from routinely- and remotely-collected health data | Dr Ewan Carr / Professor Kimberley Goldsmith | |
| 11 | CO4-037: Cohort identification for mental health clinical trials: a knowledge graph approach | Dr Angus Roberts /Dr Tao Wang /Professor Fiona Gaughran | |
| 12 | CO4-038: Developing an open-source speech analysis toolkit for clinical applications | Professor Richard Dobson / Dr Nicholas Cummins | |
| 13 | CO4-045: A combinatorial approach to the multi-omics study of the biological basis of neurodegenerative diseases | Dr Alfredo Iacoangeli /Professor Ammar Al-Chalabi |
Cohort 2022
| Project No | Student Name | Project Title | Supervisors |
|---|---|---|---|
| 1 | Nour Kanso | Prognostic predictors of outcome following psychological treatment for anxiety or depression: a longitudinal statistical learning approach using electronic health record data | Thalia Eley / Ewan Carr |
| 2 | Jonah Hugh Logan Ellis | Clinical Utility of Adverse Outcome Prediction in Hospital Admissions | Zina Ibrahim / Daniel Davis |
| 3 | Oluwanifewa Laleye | Statistical and machine learning approaches to developing predictive tools for identifying risk of preterm birth and poor neonatal outcome | Rachel Tribe / Yanzhong Wang / Andrew Shennan |
| 4 | Kanyakorn Veerakanjana | A longitudinal remote monitoring study of adults with ADHD using a novel wearable device ‘EmbracePlus’ | Jonna Kuntsi / Richard Dobson |
| 5 | Sabaoon Zeb | Clinical Decision Support in Management of Thoracic Malignancy through Multi-omic Data Science | Sophia Tsoka / Vicky Goh / Gary Cook |
Cohort 2021
| Project No | Student Name | Project Title | Supervisors |
|---|---|---|---|
| 1 | Dylan Clarke | Improving Healthcare for All – Realising Value from Health Data | Ingrid Wolfe / Jeremy Yates |
| 2 | Felix Dransfield | The use of artificial intelligence techniques to enhance prescription exercise programmes | Nick Cummins / Zina Ibrahim |
| 3 | Lachlan Gilchrist | Investigating the presence of shared genetic and pathophysiological mechanisms between Major Depressive Disorder and dementia | Petroula Proitsi / Cathryn Lewis / Sulev Koks |
| 4 | Guy Hunt | Exploring the regulatory role of transposable elements in Motor Neuron Disease | Zina Ibrahim / Alfredo Iacoangeli / Sulev Koks |
| 5 | Luke Marney | Identification of new classes of genetic susceptibility to myalgic encephalomyelitis | Nick Cummins / Alfredo Iacoangeli / Renata Kabiljo |
| 6 | Adam Syanda | Towards a better understanding of the natural history of the inherited metabolic liver disease: Alpha-1 Antitrypsin Deficiency (A1ATD) | Mariam Molokhia / Richard Thompson |
| 7 | Milly G Wilson | Blood pressure (BP) variability and pregnancy outcomes | Laura A. Magee / Peter von Dadelszen |
| 8 | Xinyue (Leo) Zhang | Can clinical decision making in Barrett’s oesophagus surveillance be automated? A study using natural language processing gastrointestinal endoscopy reports | Angus Roberts / Sebastian Zeki |
| 9 | Linglong Qian | A Computational Framework for Precision Medicine | Zina Ibrahim / Richard Dobson |
Cohort 2020
| Project No | Student Name | Project Title | Supervisors |
|---|---|---|---|
| 1 | Alessio Giacomel | Normative PET neuroimaging for precision medicine applications in brain disorders | Mattia Veronese / Ottavia Dipasquale |
| 2 | Davide Ferrari | Optimising the management of patients with respiratory illness including influenza and COVID-19 in emergency and acute pathways | Yanzhong Wang / Vasa Curcin / Jonathan Edgeworth |
| 3 | Dimitria Brempou | Using multi-omic data for neuroendocrine cancer diagnostics and metastatic predictions | Rebecca Oakey / Cynthia Andoniadou / Louise Izatt / Stephen Young |
| 4 | Dina Farran | Stroke prevention in patients with atrial fibrillation (AF) and co-morbid physical and mental health problems | Fiona Gaughran / Mark Ashworth |
| 5 | Giulio Scola | Emulating trials using EHR and Cogstack | Sabine Landau / Daniel Bean |
| 6 | Heather Marriott | A whole-genome sequencing approach to advance precision medicine and study patient heterogeneity in ALS | Ammar Al-Chalabi / Alfredo Iacoangeli / Ahmad Al Khleifat / Patrick Schwab |
| 7 | Jaya Chaturvedi | Combining statistical and knowledge-based methods for clinical modelling of electronic health record text | Angus Roberts / Sumithra Velupillai |
| 8 | Julianna Olah | Can online assessment of speech predict clinical and sub-clinical psychotic symptoms? | Kelly Diederen / Tom Spencer / Nicholas Cummins |
| 9 | Mary Abichi | IAPT care pathways and treatment outcomes for people with long-term conditions | Sam Norton / Rona Moss-Morris / Joanna Hudson |
| 10 | Miquel Serna Pascual | Extraction of novel signatures to improve the diagnosis of sleep apnoea and other respiratory disorders | Manasi Nandi / Gerrard Rafferty / Joerg Steier |
| 11 | Tareen Dawood | Learning to Trust AI Models in Cardiology | Andrew King / Reza Razavi / Esther Puyol Anton |
| 12 | Thomas Godfrey | Participatory Agent-Based Modelling of Emergency Department Patient Flow | Steffen Zschaller / Simon Miles / Jonathan Edgeworth / Andrew Krentz |
| 13 | Tianyi Liu | Use of machine learning and clinical phenotyping to identify determinants and predict CVMD risk using data from registries and electronic medical records | Vasa Curcin / Jorge Cardoso / Abdel Douiri |
| 14 | Flevin Marattukalam | Using machine learning to understand prognosis and multi-morbidity progression in patients with heart failure | Zina Ibrahim / Rosita Zakeri / Rebecca Bendayan / Serge Umansky |
| 15 | Gregory Kell | Advancing explainable human in the loop NLP analytics for clinical applications | Iain Marshall / Angus Roberts |