I’m a graduate student at the University of Oxford studying Social Data Science. I am interested in using computational methods to answer questions about human behaviour. I am especially interested in applying natural language processing and network analysis to understand information diffusion and cooperation dynamics online.

• Applying NLP to identify social norms on Reddit communities.
• Combining network science and NLP to examine the diffusion of sentiment on Reddit's political community.
• Using machine learning algorithms (e.g., decision-tree) to predict anxiety and depression diagnoses.
• Cross-cultural research on temporal discounting published in Nature Human Behaviour.

You can find samples of my work in my repositories: I'll be adding past and present work over time.

I graduated from the LSE with a First-Class Honours in Psychology and Behavioural Science in 2023. My BSc thesis used multilevel modelling to understand the association between religion, cooperation and environmental stability. The thesis found that religious identity promoted cooperative behaviour across religious groups, but is constrained by highly unstable environments: the findings suggest that religious diversity does not inherently lead to intergroup conflict! The thesis was jointly awarded the Best Performance award.

I have 3+ years of experience in programming, including in Python, R and SQL.

Inferential Statistics: Single & multivariate regression, binary & multinomial logistic regression, factor analysis, multi-level models, latent growth curve models, structural equation models.

ML: Decision-tree, random-forest, Naive Bayes, k-means clustering, principal components analysis, support-vector machine, neural networks, optimization (e.g., gradient descent), regularization (e.g., L1, L2), hyper-parameter tuning (e.g., k-fold cross-validation, test-train-validation split).

Computational Social Science: NLP -> Sentiment analysis, topic modelling (LDA), TFIDF, Naive Bayes text classifiers. Networks -> Community detection, network metrics.

The gif above reminds me of the adjacent possible: a concept from evolutionary biology suggesting that the evolution of cells, groups and ideas occurs through incremental changes to what already exists!