Robert G. C. Smith

PhD

I am a theoretical physicist by training, with expertise at the intersection of fundamental physics, foundational mathematics, and computation. A central theme of my research is the search for new and hidden mathematical structures. My PhD explored deep connections between string theory, perturbative quantum field theory, analytic number theory, and algebraic geometry, while drawing more broadly on ideas from across mathematics and physics. My thesis, “At the edges of infinity and the finite: Charting a path to UV completion from number theory to quantum fields and strings”, provides a sample of this work.

During my PhD, I also developed growing interest in mathematical modelling, particularly the modelling of complex and stochastic systems, with a focus on combining machine learning and computational methods to uncover and formalise hidden structure. This interest later broadened to encompass the application of advanced mathematical techniques in combination with data-driven analysis.

Quantitative research and machine learning

I am now currently applying this background to quantitative finance. Of particular interest is the use of data-driven research, rigorous mathematics, modelling, and computation to solve complex practical problems in financial markets. As one example, I am currently working on developing a number of AI and machine learning models in combination with market structure methodology, statistical modelling, and systematic strategy development to identify new patterns and robust signals in noisy, high-dimensional data. Another project applies advanced methods to quantify geometries of liquidity as the mathematical basis for a complete end-to-end system.

What attracts me to quantitative research more broadly, is its combination of conceptual depth, mathematical creativity, technical precision, and empirical discipline. In many ways, it shares with frontier theoretical physics the challenge of extracting well-defined structure from highly complex systems — but with a direct connection to practical problem solving and decision making.

I am also interested in mathematical and computational biology. This interest started with developing reaction-diffusion models of morphogenesis, and later evolved into a broader interest in machine learning applications and in the use of fundamental physics concepts in evolutionary biology, including genotype-phenotype mapping, mutational robustness, phylogenetics, gene regulation, and epigenetics. I am especially interested in applying computational and machine learning methods to research problems in bioenergetics and structural biology.

Projects

A selection of my projects can be viewed here.

I am also actively looking for collaborative projects and opportunities.

My blogs

• The Stochastic Ledger — A blog in quantitative finance, covering topics across market structure theory, mathematics and statistical modelling, machine learning, and algorithmic design.
• TracingCurves — A research blog in mathematical physics, string/M-theory, and a few choice diversions.
• Dialogues at Still Points — Reflections across literature, history, and philosophy.