About Me | Robert Jenkinson Alvarez

BACKGROUND

My name is Robert Jenkinson Alvarez and my research spans machine learning, quantitative finance, and pure mathematics, with an interest in geometric methods and structure-preserving models.

I am a BSc in Mathematics (First-Class Honours) graduate from the University of Bath and an MSc Financial Mathematics (Distinction) graduate from the London School of Economics. I also completed a derivatives trading and market-making training programme at Optiver.

Since graduating, I have been pursuing independent research across these fields, including a paper accepted to the NeurIPS 2026 Main Track.

RESEARCH

Investigating how geometry can reshape the way we think about different problems.

Machine Learning: I study representation learning and world models, looking into how Hamiltonian and symplectic approaches to self-supervised learning can move beyond isotropic assumptions.
Quantitative Finance: I am interested in visualising financial markets as geometric systems that evolve over time, with option surfaces and assets interacting through shared dynamics. I explore this perspective through arbitrage-free surface construction, Chebyshev methods, and models of volatility and cross-asset relationships.
Pure mathematics: I am interested in rigidity problems, especially when apparently flexible systems are forced to obey stronger constraints.

PERSONAL INTERESTS

I enjoy playing the piano and chess, particularly giveaway chess (2220 Elo, top 100 worldwide). I also enjoy playing and watching basketball and watching football.

Links / contact

GitHub → LinkedIn → Google Scholar → OCRiD → arXiv Author → Zenodo → Email →

Best reached by email for research discussions or opportunities and collaborations.

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