What is your role at the BSU?

I’m currently a first year PhD student, jointly supervised by John Whittaker and Sach Mukherjee.

The aim of my research is to develop novel machine learning algorithms for large single-cell datasets to map the network of causal relationships between genes. By aiding in the discovery of cellular mechanisms, I hope that this work will further our understanding of disease progression and facilitate personalised medicine, especially in settings with limited resources. Furthermore, studying the biological mechanisms that underlie disease is essential to designing effective and efficient drugs.

This research is motivated by recent advancements in gene editing and single-cell experimental methods, which enable collection of high-dimensional data such that the role of thousands of genes can be inferred simultaneously.

What did you do before joining the BSU?

Before starting my PhD, I mostly studied! I recently obtained an MSc in Statistical Science at the University of Oxford in 2022 and a BSc in Mathematics with Computer Science at the University of Southampton in 2021.

My interest in statistics first started during my BSc, for which I took a module called Statistical Distribution Theory. Learning about how random variables can be derived, combined and manipulated was something I found fascinating! This drove me to pursue research experience following my second year. Here, I had the opportunity to work on epidemiological forecasts of COVID-19. After this, I became interested in studying causality and so pursued an internship at the MRC BSU, working on response adaptive clinical trials with Pavel Mozgunov. This was when I realised I was keen to continue to do research at the MRC BSU and so applied for the PhD during my MSc!

What does a typical day in your role involve?

Being in the first year of my PhD, my time is typically split evenly between reading papers, writing and programming in Python or R.

The first half of this year revolved around developing a strong understanding of the theoretical aspects of causal structure learning. Alongside this, learning about cell biology has also been essential to developing an understanding of how the methodology I’m developing could be useful in practice.

I spend a lot of time developing my understanding of the theory underlying causal structure learning and the properties of gene perturbation experiment data, which can have lots of unexpected and distinct characteristics.

What keeps you motivated?

Keeping the end-user in mind helps keep my research grounded and ensures that I’m not trapped in a methodological bubble. My aim is always to develop a useful, productive and approachable methodology that will provide real utility in biological research.

Doing research on the intersection of two of my favourite fields, genetics and causal learning, has always been my goal for my PhD. Causality is a topic I have been greatly fascinated by after reading Judea Pearl’s ‘The Book of Why.’ It introduced me to the idea of causal reasoning and drawing causal inferences from data! On the other hand, deciphering the complex mechanisms that comprise genetics has the potential to influence characterisation, prognosis and treatment of diseases.

Aside from work, being in the office always makes me excited about my research. Having discussions with other researchers of all fields and types always leads to new ideas and the motivation to move forward!

How do you keep your work open and accessible?

When writing code or running experiments, I always try to make it as easy as possible to come back to it. This doesn’t always work, however, and sometimes I have to return to the code to clean it up! Having reproducible code makes it much easier to share work.

I try to post the legible code that I’ve created on my GitHub site. I’m also keen to spend more time writing up work as shortHow blog posts as writing is one of my favourite parts of research, although having the time to do it is always a challenge!

What is the best part of your job?

The best part about my job is the discussions and collaborations with diverse individuals! Working in cell biology and causal inference means that I get to have productive conversations with biologists, geneticists, econometricians, statisticians, computer scientists and even philosophers! Being part of a college like Clare Hall allows me to meet students and fellows with fascinating backgrounds.

The work of the BSU is also incredibly varied, making it an exceptional place to do research. As mentioned previously, coming into the BSU and having face-to-face interactions is what keeps my research going.

I feel very lucky to have such a warm, helpful and inspiring community around me!