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 Imperial College London
Learn more about our prizes“My research lies at the intersection of deep learning and brain-computer interfaces (BCIs), an emerging technology with the potential to revolutionise health and human-computer interaction.
“During my PhD, I focused on advancing motor-imagery BCIs through deep learning, culminating in my dissertation titled “Enhancing motor-imagery brain-computer interfaces through deep learning.” This work explored the convergence of differentiable signal processing, geometric deep learning and causal reasoning.
“Currently, as a Postdoctoral Researcher at Imperial, I am developing generative foundation models for biosignals, with the goal of creating versatile, task-agnostic representations. The aim is to design models capable of generalising across subjects and recording conditions, enabling efficient adaptation to a wide range of downstream tasks.”
“In next-generation mobile networks (6G), smart surfaces will be deployed throughout urban environments to help electromagnetic signals reach places where the signal is usually weak or blocked. These surfaces will make mobile connectivity even more reliable and faster, enabling new applications and use cases.
“My PhD thesis focused on the modeling and analysis of these surfaces, known as reconfigurable intelligent surfaces (RISs). I am now a Postdoctoral Researcher investigating how electromagnetic signals can be used to compute operations in the analog domain, breaking the fundamental limits of digital computing.”
“I am a final year PhD student, advised by Professor Ben Glocker at Imperial College London. I’m interested in work that combines ideas from deep learning and causality to solve problems in medical imaging.
“My research focuses on causal and statistical structures of dataset bias that may induce failure in machine learning methods. Today, these issues are poorly understood and can lead to unexpected failures in real-world machine learning applications, such as disease detection. By illuminating these issues, I aim to understand why current methods fail and build methods that may be safely deployed in high-stakes settings such as medical imaging.”
