Every year, G-Research runs a number of different PhD prizes in Maths and Data Science with academic institutions in the UK, Europe and beyond.
Each prize is worth up to £10,000 and is open to final or penultimate year PhD students at specific universities, working across areas including Machine Learning, Quantitative Finance and Mathematics.
We’re pleased to announce the winners of the first prize of 2025, which ran in conjunction with the University of Oxford.
Learn more about our prizes“My research focuses on randomised algorithms in numerical linear algebra.
“As the volume of data continues to grow rapidly in our data-driven world, scalable algorithms are becoming increasingly important. I develop and analyse randomised methods that efficiently enhance fundamental linear algebraic tasks, enabling their application to large-scale computational problems.
“My primary interest lies in randomised low-rank approximation techniques, such as the Nyström method and CUR decomposition, which are widely applicable in areas such as kernel-based methods, matrix differential equations, and data streaming.”
“My research focuses on developing neuro-symbolic methods that integrate background knowledge constraints into neural networks during training and enforce them at inference time.
“I am particularly interested in incorporating such constraints into deep generative models for synthesising realistic tabular data. During my PhD, I demonstrated that linear constraints and even more expressive ones, such as disjunctions over linear inequalities that model non-convex and disconnected spaces, can be integrated during training to enhance the quality of synthetic data.
“My broader research vision is to bring neuro-symbolic AI to real-world applications, taking a step closer to building reliable systems.”
“I am interested in the mathematical study of spatial branching processes in inhomogeneous environments.
“Spatial branching processes model spatial growth and the spread of genes or diseases in populations. Here, I am interested in what strategies allow particles be successful, that is to be at the frontier of the growth process.
“This question becomes even more interesting when considered in inhomogeneous environments where I study the interplay of effects caused by random motion, random reproduction and random environment.”
“I am a final-year DPhil student at the University of Oxford. I am broadly interested in both the empirical and theoretical aspects of deep learning.
“My current research focuses on the intersection between graph machine learning and sequence modelling.
“I am also interested in machine learning applications to spatio-temporal data, such as that found in quantitative finance or physical simulations.”
All shortlisted candidates were of excellent calibre, spanning a range of topics from algebra to machine learning, and all demonstrating excellent potential as evidenced by the breadth and depth of their submissions.
