- Developing uncertainty quantification techniques for Bayesian neural networks in partnership with the Alan Turing Institute, the Ministry of Defence and GCHQ.- Conducting large-scale automated literature reviews in partnership with the Defence Science and Technology Laboratory and the US Army Research Laboratory.- Developing parallel numerical Bayesian inference algorithms that allow Stan to capitalise on multi-GPU clusters.- Application of scalable Numerical Bayesian Inference to longitudinal healthcare studies.

- Co-funded by the EPSRC Centre for Doctoral Training (CDT) in Distributed Algorithms and IBM Research.- Developed and implemented novel, opportunistic SMC samplers that capitalise on commodity compute hardware.- Implemented parallel SMC samplers on multi-CPU architectures using MPI and GPU using TensorFlow and PyTorch.- Developed and implemented SMC samplers that interface with Stan (via cmdStan and BridgeStan) and NumPyro.- Automated large-scale COVID-19 analysis on high-performance computing facilities using Slurm.- Contributed to SMC-NUTS, HTTPStan and PyStan open-source software packages.- Student Representative on the Strategic Advisory Board: Actively participated in high-level discussions and decision-making processes, providing the student perspective on strategic decisions within the CDT.- Member of the CDT Leadership Team: Collaborated with leaders and members of the CDT to enhance the training and support provided to doctoral candidates.

- Delivered three lectures for MSc module Multi-core and Multi-processor Programming (COMP528).- Served as a laboratory demonstrator and coursework marker for BSc module High Performance Computing (COMP328).- Served as a laboratory demonstrator and coursework marker for MSc module Multi-core and Multi-processor Programming (COMP528).- Served as a laboratory demonstrator and coursework marker for MSc module Machine Learning and Bioinspired Optimisation (COMP532).- Designed and delivered a training session on distributed computing with HTCondor for first-year PhD students in the CDT.

- Developed and implemented Bayesian sampling methods using PyTorch.- Integrated Travis CI and Github Actions for continuous integration and development to ensure robust and maintainable code.- Utilised Git for version control, collaboration, and maintaining code quality.