EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Sarah Gascoigne

I have an undergraduate degree in Mathematics and Psychology from Newcastle University, where I received the Mary McKinnon Prize for outstanding performance in stages two and three. I am very interested in the applications of machine learning in medicine, particularly tailoring treatments to each individual, with the hope that this may improve prognoses and reduce the probability of side effects.  

In my spare time, I enjoy cycling, yoga and attending the theatre. For me, the main selling point of the CDT was the cohort aspect; learning in a collaborative setting is where I tend to thrive.  

I am hoping that this CDT gives me the opportunity to form strong relationships with students within my cohort and the wider CDT community, through which we can support each other in our research endeavours.   

PhD Title

Determining seizure modulating processes

Epilepsy is a neurological condition impacting over 65 million individuals worldwide and is characterised by recurrent, spontaneous seizures. A subset of epilepsy syndromes, known as focal epilepsy, is where pathological activity originates in one region of the brain from which it may propagate to other regions.

Previous work has shown that occurrence and characteristics of seizures can differ on various time scales (e.g., circadian, monthly). Therefore, the overarching question I aim to answer in my PhD thesis is whether it is possible to use biomarkers to investigate variability in seizures and potential modulating processes in focal epilepsy.

The severity of seizures is currently measured using one of a library of scales, all of which are somewhat based on qualitative assessments from patients and clinicians. The first project of my PhD seeks to develop a library of objective, quantitative markers of seizure severity obtained from intracranial electroencephalography (iEEG) recordings. This will be created with the hope that the proposed markers can be used to complement existing measures.

I will use iEEG data made available from two epilepsy monitoring units to develop markers and conduct statistical analysis. I intend to branch out into using wearable devices to measure biomarkers of seizures and combine them with EEG information to form a multimodal approach.


Yujiang Wang, Kevin Wilson, Yu Guan