I graduated from Teesside University with a BSc (Hons) in Internet and Microsystems Technology, then I achieved a Distinction in MSc Data Science from Newcastle University. I have over 10 years of experience working as an Information Technology professional at Newcastle University.
My MSc project involved working with Newcastle University Biosciences Institute (NUBI) and the Research Software Engineering (RSE) teams to improve and redevelop a complex Deep Learning model using PyTorch to detect cells in microscopy images.
I joined the CDT programme for its collaborative research environment and links with industrial partners which will enable me to build on the skills I gained in my master’s with a focus on solving real data science issues. Most importantly, the opportunity to work alongside other researchers in the same area.
I am passionate about travelling and visiting new places. I like to discover new places and spend time walking in the landscape.
PhD Title
Deep Learning for generating and up-scaling Digital Twins of real- world Microbial Systems
Given the vast range of environments and bacteria species, it is an almost impossible task to understand all possible interactions between multiple bacteria. My research will provide a more accurate simulations of how bacteria interact within an environment by using a Deep Learning Emulators (DLE) to predict bacteria function of unseen bacteria genomes, using a large and growing sets of bacterial experiment data, containing the bacteria genome sequencing, along with interaction data.
The goal of my research is to accelerate the creation of unique, and world leading, expertise that will deliver the AI advances needed to make a step change in the modelling of Engineered Microbial Systems (EMS).
