I am a Lecturer in Data Science for Life Sciences at the University of Edinburgh.

Modelling neural activity with machine learning and statistics

In our group, we focus on developing flexible probabilistic and machine learning methods for modelling and analysing neural activity.

Deep learning models for predicting brain activity

We developed a Vision Transformer model to predict primary visual cortex activity based on visual stimuli and behaviour.

model block diagram on the left with illustration of synthetic receptive fields on the right
Model block diagram and synthetic receptive fields.

Probabilistic models of neural relationships

We use techniques like copulas, Gaussian processes and normalizing flows to model varying interactions within neural activity and their correlation with external variables.

eight density plots on a two by four grid
Copula dependencies between two example neurons (top) and a neuron and the lick variable (bottom) changing over time (left to right).

Matrix and tensor factorizations for neural dimensionality reduction

We decompose neural matrix and tensor representations to extract interpretable structure from large datasets.

a big three-dimensional box on the left with an approximate sign, a smaller three-dimensional box in the centre and three matrices on the left
Decomposition of a tensor representing neural population activity into a small core tensor and several matrices.

Opportunity to do a PhD

If you would like to join my lab as a PhD student, please contact me and tell me something about your background and research interests. Funding opportunities available!