I am a PhD student at Ilan Davis lab at the Department of Biochemistry at the University of Oxford. I am under the supervision of Ilan Davis and Stephen Taylor and I am collaborating with Charlotte Deane head of the Department of Statistics at the University of Oxford.

I am funded by the UKRI BBSRC and by Zegami, an Oxford University spin out company for data visualisation and pattern recognition using artificial intelligence.

Previously, I studied Physics at the University of Cambridge where I obtained a MSci and a BA.

I am an avid runner and I love participating in running events.

Research Interests

My research interests lie in the application of machine learning algorithms on answering complex biological questions related to small and sparse biological datasets.

I study how gene expression in tissue can provide insights into post-transcriptional regulation. To do so, I use machine learning to uncover patterns in a combination of data including, mRNA and protein expression data, gene features and pathway information.

News

2022

I presented a poster at the EMBO workshop: RNA localisation and local translation based on my preprint “Systematic analysis of YFP gene traps reveals common discordance between mRNA and protein across the nervous system”. I discussed our data and the use of annotations to train a machine learning model to predict RNA localisation in the Drosophila nervous system.

I am very pleased to announce that my first first co-author paper “Systematic analysis of YFP gene traps reveals common discordance between mRNA and protein across the nervous system” is now on bioRxiv! Access it here.

2021

Selected for DS4A fellowship program by Correlation One - Autumn 2021.

2020

I organised a virtual Python course for begginners in collaboration with the Oxford Foundry, Codesoc and OxWoCS. For this I created and delivered content and was teaching for 6 weeks. More details in the Teaching section.

I interned in Zegami where I implemented data visualisation techniques and explored machine learning algorithms to analyse a histological image datasets.

Checkout our paper, published in RNA Ribo-Pop: Simple, cost-effective, and widely applicable ribosomal RNA depletion.

2019

I was a winner of the competition “What is the biggest challenge or greatest opportunity for AI in the future?”. I presented my idea on the AI@Oxford conference in September 2019.

I was selected to attend MLSS 2019 (Machine Learning Summer School) where I presented a poster on my work.