Every year, G-Research runs a number of different PhD prizes in Maths and Data Science at universities 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 this year, run in conjunction with the University of Cambridge.
We’re proud to announce that Julius von Kügelgen was unanimously elected as the winner of our Cambridge PhD prize. Julius will receive £10,000.
Julius’ research interests lie at the intersection of machine learning and causal inference. Much of his PhD research has focused on causal representation learning, a recently emerged field that aims to leverage machine learning methods to extract causal structure from complex high-dimensional data, for example by describing low-level measurements such as pixels in terms of abstract high-level latent concepts such as objects and their relations.
Potential applications of this methodology include learning gene interactions from protein expressions or better understanding macro climate phenomena from low-level sensory data.
The prize money will contribute to paying off a part of his student loan from his BSc and MSci maths studies at Imperial College London.
As well as awarding a first prize, we are proud to announce two runner-ups, with each receiving £4,000.
Giovanni’s research primarily focuses on developing stochastic control and analysis methods for the theoretical models underpinning real world problems and systems. By exploiting the structure of Continuous Time Markov Chains and Decision Processes his thesis uncovers some fundamental variance bounds and feedback control limitations that apply to relevant problems in the life science.
In order to address these issues, Giovanni has developed important properties for a class of Kolmogorov equations and established a novel set of sufficient conditions on the minimisation of sub/super modular function on lattices.
Giovanni said: “The prize from G-Research provides a very strong encouragement to keep pursuing my work and provides valuable support for my research expenses.”
Gabriel is a PhD student in statistical learning and information theory at the University of Cambridge. His current research focuses on detecting heterogeneities in high-dimensional data and on expanding existing high-dimensional statistical methodologies to provably succeed when heterogeneous data is present.
In particular, Gabriel develops novel algorithms and theory to tackle data heterogeneity in the form of “change points’’, a form of temporal heterogeneity that naturally arises in applications ranging from causality to genomics and finance.
Hear more from our Head of Forecasting Engineering and learn how to keep your projects on track by embracing constant change and acting quickly.
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