From theory to real-world impact
Nail’s research is shaped by one of the most consequential open questions in modern machine learning – understanding why generative models work and when they fail.
“A development that has shaped my thinking is the realisation that as generative models become infrastructure, used to produce the data that trains the next generation of models, questions about error accumulation and model collapse become unavoidable. I want to understand these failure modes at a mathematical level, not just observe them empirically.”
He hopes his work will strengthen the theoretical underpinnings of generative modelling while shedding light on the long-term stability of AI systems trained in increasingly self-referential settings.
“I want my research to give practitioners and theorists alike a clearer picture of what diffusion models are actually doing, connecting them to optimal transport, to geometry, to classical probabilistic ideas, so we can build systems that are not just powerful, but understood.”
Opening Doors Through NextGen
For Nail, the G-Research Scholarship has opened the door to new opportunities – making his PhD possible while connecting him to a wider research community.
“The G-Research Scholarship means a great deal to me; it opens doors that would otherwise be hard to access and turns that aspiration into reality. It also offers the chance to engage with like-minded students and to learn from the people and ideas driving the firm behind it.”
He’s looking forward to contributing his curiosity to a community united by a shared depth of thought.
“I admire that G-Research’s researchers strive for a profound understanding of the tools they use, approaching challenges in ways that are often non-intuitive and unconstrained by convention. I’m eager to contribute my own curiosity to that environment.”
