Hi!

I am a final-year PhD candidate in Statistical Science at UCL, actively seeking full-time research or engineering roles in machine learning and computational statistics, starting October 2025.

My doctoral research focuses on Bayesian calibration of complex computer models, with a strong emphasis on Gaussian Processes (GPs) and high-performance computing. I developed KOH-GPJax, a JAX-based Python package that accelerates Bayesian calibration by orders of magnitude through GPU parallelisation. This tool has been successfully applied to challenging real-world problems in weather and climate modelling in collaboration with the UK Met Office and Stanford University.

I am passionate about applying cutting-edge statistical and machine learning methods to solve complex scientific and industrial problems. My current work is being extended in partnership with IIT Delhi to tackle more complex climate models, and will involve deploying advanced Gaussian Process methods on state-of-the-art NVIDIA GH200 Grace-Hopper superchips.

Core Competencies

• Machine Learning: Gaussian Processes (GPJax, GPyTorch), Bayesian Optimisation.
• Software Engineering: Python (JAX, PyTorch, NumPy, SciPy), R, CI/CD, HPC.
• Statistical Modelling: Bayesian Inference, Uncertainty Quantification, Experimental Design.
• Domain Expertise: Climate and Weather Modelling, Scientific Computing, Statistical Computing.