Explore projects

Exploring machine learning methods, with special focus on the deep learning methods in order to attempt Neuroimage segmentation and analysis

Pandas tutorial for the analysis group meeting on November 21

Educational material for the WIN Analysis Group

CUSP : A C++ Templated Sparse Matrix Library

Computational modelling course taught as part of the WIN graduate programme.

Python-based asymmetric fibre orientation distributions (aFODs) estimation library

Pipeline for brain imaging processing of UK Biobank.

Pipeline developed by Fidel Alfaro Almagro, Steve Smith and Mark Jenkinson.

Contributions by FMRIB Analysis Group, University of Oxford.

Additive Signal Change inference tool.

My first python package, random utilities

Jupyter notebook-based practicals for the 2018 WIN PyTreat

Jupyter notebook-based practicals for the 2018 WIN PyTreat

This folder contains practical sessions from a course taught in the Experimental Psychology department at the University of Oxford on computational modelling of behavioural data, building on the paradigm of Behrens et al. (NN, 2007).

Pipeline for brain imaging processing of UK Biobank.

Pipeline developed by Fidel Alfaro Almagro, Steve Smith and Mark Jenkinson.

Contributions by FMRIB Analysis Group, University of Oxford.

Experimental paradigm from "Associative Learning of Social Value", Behrens et al., Nature 2008, coded in Presentation.

Matlab/Cogent version of Information Seeking paradigm from Hunt et al., PLoS Biology 2016.

Code for a particle filter.