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 next PhD prize winners, which ran in conjunction with University College London
Learn more about our prizes“I am a Cancer Research UK-funded MBPhD student at the UCL Great Ormond Street Institute of Child Health. My research sits at the intersection of systems neuroscience and computational oncology, specifically aiming to develop new and improved treatments for patients with brain tumours by understanding how brain tumours interact and integrate with otherwise healthy brain networks.
“During my PhD, I led the development of a computational approach, termed ‘tumour network mapping’, which enabled us to map the neural dependencies of glioma growth in the living human brain for the first time. By integrating human brain connectomics with intra-operative neural recordings and genomic analyses of tumour specimens, our work identified how the brain networks within which tumours arise shape important clinical behaviours, including their pattern of growth.
“These findings have since informed a prospective clinical trial for children with diffuse midline glioma, the most common cause of childhood cancer-related death.
“In the longer term, I aim to establish an independent clinician-scientist programme focused on translating quantitative models of human brain organisation into novel therapeutics for patients with brain tumours.”
“My research focuses on the statistical foundations of machine learning: what can we learn from limited data, and how can we design methods that make the best use of the information available?
“During my PhD at UCL, I studied learning problems involving infinite-dimensional outputs, the estimation of nonlinear causal effects in the presence of unobserved confounding and learning from related tasks to improve predictions on new ones.
“My work combines guarantees for practical algorithms with fundamental limits on what any method can achieve.”
“My research focuses on statistical physics, random matrix theory and quantitative finance.
“I am interested in using methodologies developed for disordered physical systems to understand emergent risks in financial markets, from a single leveraged household to the stability of an entire market.
“My PhD at UCL explored how instability arises and spreads across traditional and decentralised finance. I developed exactly solvable models showing that leveraged equity strategies undergo phase transitions between success and default, and used the replica method from spin glass theory to study the largest eigenvalues of sparse covariance matrices, turning systemic risk into a computable instability threshold.
“I also introduced the notion of “crosstagion”, the two-way transmission of instability between crypto and traditional markets, and measured its direction empirically using information theoretic methods.”
