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.
August grant winners
Louis Sharrock (Lancaster University)
“I am a senior postdoctoral research associate in statistical machine learning at Lancaster University, working on topics at the intersection of computational statistics, optimisation, and machine learning.
“My current research focuses on developing learning-rate free algorithms for Bayesian inference, online parameter estimation in interacting particle systems and mean-field equations, and score-based methods for simulation based inference,
“This generous grant from G-Research will enable me to present my recent work on learning-rate free Bayesian inference at the 25th International Symposium on Mathematical Programming in Montreal, Canada.”
Alvaro Arroyo (University of Oxford)
“I am a second year PhD at the Oxford-Man Institute of Quantitative Finance at the University of Oxford, where I work under the supervision of Álvaro Cartea and Xiaowen Dong.
“Broadly, my research focuses on deep learning, and its uses to process data in the form of time series and graphs. Furthermore, I am interested in the applications of these techniques in the context of market microstructure.
“The G-Research grant will allow me to attend NeurIPS 2023, which will take place in New Orleans this year.”
Harel Israel Berger (Georgetown University)
“I am a post-doctoral fellow in computer science at Georgetown University. My main research interests are in machine learning, artificial intelligence, natural language processing and networks.
“In addition to various other research endeavors, I am engaged in a collaborative effort with scholars from, inter alia, the University of Cambridge. Our mutual research project centers around investigating internet censorship, employing the methodologies of machine learning and artificial intelligence.
“Supported by the G-Research grant, I will be able to acquire the necessary hardware resources that will play a crucial role in ensuring the smooth progression of the above research project.”
Stephen Villejo (University of Glasgow)
“I am a PhD student in Statistics at the University of Glasgow. My research focuses on developing models for spatially misaligned data. We are primarily looking into the application of these models on spatial epidemiology.
“Currently, we are investigating the link between climatological variables (such as temperature, rainfall, humidity) and Dengue fever cases in the Philippines using spatio-temporal models.
“I am hugely grateful to G-Research for the grant which allows me to do a research placement at the Department of Mathematical Sciences of the Norwegian University of Science and Technology (NTNU).
“The research placement will allow me to work with our collaborator at NTNU and will also be an opportunity to network and engage with other researchers in the field.”
Veselin Manojlovic (City, University of London)
“My PhD research at City, University of London revolves around mathematical modelling of cancer evolution.
“I am grateful to G-Research for awarding me their academic grant which will help cover the cost of a new laptop for my research.
“This will provide me with greater efficiency in running agent-based simulations locally, as well as testing my approximate Bayesian computation workflow on a smaller scale and machine learning methods for calibrating agent-based models to cancer data, all of which will then be scaled up for use on an HPC cluster.”
Caroline Lawless (University of Oxford)
“I am a final year PhD student in statistics at the University of Oxford. My research focuses on the topics of Bayesian nonparametrics and Bayesian asymptotics.
“In particular, I have been working on mathematical proofs to validate statistical methods that are commonly used in practice, such as approximate Bayesian computation (ABC) methods, and Bayesian nonparametric mixture methods.
“My funding will end right before I submit my thesis, but my viva will take place six weeks later. I am truly grateful for the G-Research grant, which will fully fund my living expenses for this six week period. This will allow me to focus completely on my research at that time.”
Daniel Bussell (University College London)
“I am a final year Mathematics PhD student at UCL specialising in Deep Learning Methods for Partial Differential Equations and Stochastic Differential Equations.
“I am particularly interested in the applications of such methods to problems arising in finance such as portfolio optimisation, utility indifference pricing and derivatives hedging.
“This grant will allow me to present my recent paper on Multistep Deep Learning Methods for PDEs in Finance at the International Congress of Industrial and Applied Mathematics in Tokyo.”
Congratulations to our grant winners.