• Designing and applying AI models to accelerate drug discovery for biologics and small molecules.• Led a team building novel generative methods for de novo antibody design.• Developer in the team that built Exscientia’s virtual screening pipeline for developable antibody binders.• Developed AbMPNN, an antibody specific inverse folding model. Deployed and used AbMPNN for hit expansion in the Biologics pilot study.• Created an antibody-specific diffusion model, IgDiff by fine-tuning a public model on antibody data. Demonstrated that IgDiff generates novel and designable antibody structures.• With collaborators published a study benchmarking the performance of docking tools for predicting antibody-antigen binding poses when using AI predicted antibody models.• Published workshop papers on AbMPNN and IgDiff at ICML and NeurIPS respectively.

Responsibilities included:• Conducting world leading research in early Universe physics, with a focus on numerical simulations and relativistic hydrodynamics.• Maintainer and developer for several HPC applications for studying gravitational wave production in the early Universe. These codes have been used to produce publications in some of the leading journals in the field, with the results being widely used within the community.• Presenting research seminars and conferences internationally, including as a plenary speaker at the inaugural European Consortium for Astroparticle Theory Symposium. • Full member of the LISA Consortium, an upcoming European Space Agency mission.• Co-supervisor for BSc and MSc students, with research projects leading to publications.• Teaching assistant for courses on General Relativity and Classical Mechanics.

• Researching first-order phase transitions in the early Universe.• Lead developer of a massively parallel MPI C++ simulation code studying vacuum transitions.• Core developer of a massively parallel MPI C simulation code studying a thermal phase transition using relativistic hydrodynamics.• Experienced with using Python to analyse terabytes of data generated during production runs comprising millions of CPU hours.• Presented research at multiple high-level international conferences and universities around the world.• Published in prestigious journals, Physical Review Letters and Physical Review D.

Delivered workshops and marked coursework on the Master level module "Programming in C++" and the second year undergraduate module "Thermal and statistical physics".

Implemented and explored the utility of several deep learning models to identify fields of coffee in Brazil using Sentinel 2 satellite imagery. Performed exploratory data analysis for monitoring coffee crop health and fruit ripeness.Technologies included AWS, TensorFlow, keras, geopandas, eo-learn, and sentinel-hub.

Took part in research placements over the Summers in undergraduate degree at the University of Sussex.First research placement: • Studied the wavefunction of a graviton travelling in a 5-dimensional Anti-de Sitter metric and Kaluza-Klein theory.Second research placement: • Worked with a professor on a smoothed-particle hydrodynamic code intended to simulate flooding.• Developed visualisation methods for a smoothed-particle hydrodynamics simulation program.• Created a comprehensive user manual detailing installation and how to use the program.• Ran tests comparing program simulations for various fluid flows against theoretical predictions.