I am Junior Research Fellow in Biological and Medical Sciences at Christ’s College Cambridge and a Research Associate in Neuroinformatics at the Department of Clinical Neurosciences (University of Cambridge).
My current work centers around utilizing real-world memory clinic data from the NHS and combine it with machine learning and network neuroscience methods to develop early markers for dementia detection and prognosis. By developing a low-cost, non-invasive, and integrated AI tool for the NHS, we can help doctors detect and diagnose dementia early, ultimately leading to better treatment and management options for patients. BBC news coverage of the project can be found here. I am also involved in the PASSIAN Project, which aims to implement federated learning in the NHS.
I recently completed my PhD in Neuroimaging at King’s College London working with Prof Mehta and Prof ffytche on a project exploring the neural and cognitive correlates of Parkinson’s Disease psychosis. In my doctoral work, I employed multimodal imaging techniques (resting state fMRI, structural and diffusion imaging) and combined them with network neuroscience, transcriptomics, and receptor maps approaches. I have also gained further expertise on graph theoretical and machine learning analyses as part of my NIHR SPARC Award at the University of Cambridge under the amazing supervision of Prof Ed Bullmore and Dr Sarah Morgan.
My other projects include computational modelling of cognitive performance and cognitive decline, the investigation of psychotic symptoms across diagnostic categories, and the exploration and classification of multimodal hallucinations (i.e. hallucinations occurring across multiple sensory modalities).
Beyond my research, I am passionate about promoting open access to coding and AI resources. Recently, I was selected as an ambassador for the Women Techmakers Initiative powered by Google, where I work to promote diversity and inclusion in the tech industry.
PhD in Neuroimaging, 2018-2022
King's College London
MPhil in Computational Psychiatry, 2017-2018
University of Cambridge
BSc in Psychology and Neuroscience, 2014-2017
University of Cambridge
Constructing and investigating Morphometric Similarity Networks from structural and diffusion weighted imaging data in a large, longitudinal cohort of patients with Parkinson’s Disease Psychosis.
Using multi-level meta-analytic tools to investigate if a specific profile of impaired cognition and visual function is linked to vulnerability to visual hallucinations in Parkinson’s Disease. The overall aim is to better understand the complex relationship between psychosis and cognitive decline in Parkinson’s patients.
Using graph theoretical approaches and resting fMRI data to better characterise the neural fingerprints of visual hallucinations in Parkinson’s Disease. This includes (i) evaluating group differences in FC in terms of both Von Economo cytoarchitectonic principles and well-established functional connectivity networks, (ii) NBS analyses, and (iii) machine learning approaches to identify patterns of covariance between rsfMRI networks and cognitive and clinical biomarkers of interest (cognitive tasks, MCI tests, cerebrospinal fluid biomarkers such as β-Amyloid, T-Tau and α-Synuclein)
Developing targets for understanding current treatments and developing novel treatments The aim of this pharmachological intervention is to implement and enhance a neuroimaging protocol to test for whole brain impairment in PD patients with and without psychosis to (i) enhance understanding of the neural basis of PD psychosis, (ii) estimate the magnitude of impairment both in predefined brain regions and across brain networks and (iii) test for drug effects (5-HT2a inverse agonism) in these networks.
This work focuses on the application of computational models of reinforcement learning (RL) in patients with schizophrenia, in addition to investigating the link between RL task performance and molecular genetic risk for the disorder.
Hallucinations can occur in different sensory modalities, both simultaneously and serially in time. Hallucinatory experiences occurring in multiple sensory systems—multimodal hallucinations (MMHs)—are more prevalent than previously thought and may have greater adverse impact than unimodal ones, but they remain relatively underresearched.