Each month, we provide up to £2,000 in grant money to early career researchers in quantitative disciplines.
Our aim is to support and assist PhD students and postdocs conducting research, particularly with costs that may be difficult to get funding for elsewhere, for example, travel for those who are caring for children, or expenses for volunteer work related to research.
Learn more about our grant programme, including how you can apply and the work we support.
Read on to hear from our latest winners, their research and how our grants will aid their work.
January grant winners
Fabiola Espinoza (Université Paris-Saclay)
“I’m a final-year PhD student in Artificial Intelligence at Université Paris-Saclay, with research interests in data heterogeneity in federated learning.
“Federated learning is a framework that enables multiple participants to collaboratively train a model without sharing their data to reinforce privacy and reduce communication costs. It is known that when participants have different data distributions, the global model may not perform as well for all participants. During my PhD, I proposed two methods to enhance the performance of a global model while dealing with participants with different data distributions. During my PhD, I also investigated defenses for backdoor attacks in a federated context.
“I am genuinely grateful to G-Research for their grant, which enabled me to attend the IEEE ICASSP conference and present my work on dictionary learning for federated domain adaptation.”
Danilo Đorđević (ETH Zurich)
“My research focuses on the intersection of signal processing and machine learning, with a particular emphasis on exploring novel deep neural network architectures and design patterns, self-supervised representation learning for information retrieval, geometric deep learning, graph neural networks, and the application of Transformers in the image and language domain.
“I am keen on exploring multidisciplinary applications of deep learning models, particularly graph neural networks, to finance. I find joy in exploring novel architectures, learning about inductive biases they introduce, and the interplay between optimization methods and their structure.
“I am very grateful for the generous support provided by the G-Research grant, which will help cover the costs of my attendance at the AAAI 2024 conference in Vancouver, Canada. There I will present my research paper where we push the boundaries of knowledge distillation by training shallow feed-forward networks to replicate the behavior of attention mechanisms in the Transformer architecture. I believe that this grant’s support is crucial for disseminating research findings within the academic community.”
Vukasin Bozic (ETH Zurich)
“I am a masters student at ETH Zurich, focused on Computer Vision and Natural Language Processing, and finalising my studies at Disney Research working on my thesis on Diffusion Models.
“I am interested in Generative AI and investigating the multimodal generative models at the intersection between Vision and Language. I published a paper on the redundancy of the attention layers in the Transformer architectures and their replacement with simpler modules at the AAAI 2024 conference.
“The G-Research grant will help me to attend the conference in Vancouver in February, present my work and expand my knowledge on the relevant topics in AI.”
Luke Taylor (University of Oxford)
“My research interests are in computational neuroscience where I study the neural code of the visual system, for which I have just handed in my DPhil thesis.
“The G-Research grant will allow me to complete my remaining manuscripts for publication, and allow me to explore further funding opportunities for a post-doc.”
Xell Brunet Guasch (University of Edinburgh)
“I am a maths PhD student in Edinburgh, working on probabilistic models of cancer evolution. My research focuses on developing stochastic models of mutation accumulation and tumor growth, which, together with genetic data from large patient cohorts, can help us understand the initiation and progression of cancer.
“This grant from G-Research will support me visiting the Naxerova Lab in Harvard Medical School, in Boston, where I will work on a mathematical framework to study metastasis evolution from phylogenetic data.”
Cécilia Aubrun (Ecole Polytechnique)
“I’m a third year PhD student at Ecole Polytechnique in Paris, where I work on the endogenous dynamics of price movements and on markets microstructure.
“Thanks to this grant, I’ll be able to compose and invite the perfect jury for my defense, which is a major milestone in the life of a researcher.”
Pierre Clavier (Ecole Polytechnique)
“I am a PhD candidate working on both theoretical and practical aspects of Reinforcement Learning that enable practical deployment on real-world applications, especially Robotics of Reinforcement Learning.
“G-Research’s grant will enable me to visit The Computing and Mathematical Sciences (CMS) Department of the California Institute of Technology to collaborate on these subjects and particularly about the sample complexity of Robust Reinforcement Learning.”
Stephano Sarao Mannelli (University College London)
“My research focuses on the intersection of machine learning and neuroscience, aiming to understand the intricate parallels and distinctions between learning mechanisms in animals and machines. My aim is twofold; enhancing neural networks and unravelling the enigmatic workings of the human brain. By probing into these learning processes, I seek to improve artificial neural networks and gain deeper insights into the underlying mechanisms governing our brains.
“The generous support from G-Research will allow me to participate in COSYNE 2024 in Portugal, where I will present my research findings and actively engage in a workshop that I am jointly organizing. As I will be a visiting researcher in South Africa during the conference, obtaining travel grants has proven challenging, given that I am not travelling from my home institution in the UK. I deeply appreciate the flexibility of the G-Research grant, which accommodates situations that other grants may not cover.”
Victoria Sivill (The Alan Turing Institute)
“As a final year PhD student, having (finally!) completed my thesis on Explainable AI, I have developed various algorithms which make time series models more interpretable. However, these are currently embedded in inaccessible research articles and implemented as scrappy research code.
“This grant from G-Research will support me in transforming my PhD research outputs into open-source software which can be easily understood by anybody, not just machine learning engineers.”
Ludvig Hult (Uppsala University)
“My research focuses on the intersection of statistics and machine learning, developing methods that are robust and give statistical guarantees even under distribution shift.
“Thanks to the grant of G-Research, I am able to extend my research visit to further develop a collaboration with ETH Zürich Seminar for Statistics.
“This research explores application of causal inference methods for medical applications in neuro critical care, at the intersection of machine learning and causal inference.”
Congratulations to our grant winners.
