Robert G. C. Smith

PhD

I am a theoretical physicist by training, with expertise at the interface of fundamental physics, foundational mathematics, and computation. One of my main research interests is searching for new and hidden mathematical structure. My PhD research explored deep connections between string theory, perturbative quantum field theory, analytic number theory, and algebraic geometry, while also drawing broadly on other areas across mathematics and physics. My PhD thesis, “At the edges of infinity and the finite: Charting a path to UV completion from number theory to quantum fields and strings” offers a sample of these efforts.

Alongside this work, I developed a strong interest in machine learning and computational methods, primarily as tools for discovering and formalising hidden structure. This began through problems in number theory and numerical analysis (e.g., the search for numerical patterns), and later developed into broader interests in applications of modelling, inference, and data-driven research in the context of complex systems.

Quantitative research and machine learning

I am now 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.

What attracts me to quantitative research and trading 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

My current 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.