PhD Candidate in Pure Mathematics
Research: Number Theory · p-adic Chabauty–Coleman methods
I am a PhD candidate in pure mathematics working in number theory, with a focus on p-adic Chabauty–Coleman methods. Alongside research, I build high-performance software (primarily in Haskell) and write educational material in abstract algebra and probability.
My current research direction centers on p-adic methods for studying rational points, especially Chabauty–Coleman style techniques.
I am particularly interested in extending Chabauty–Coleman methods using \(p\)-adic differential equations, aiming to broaden the scope of problems where these methods apply.
I am also interested in multi-disciplinary researches. Other interests include:
Probability theory
Category theory
Haskell libraries: author of multiple Haskell libraries. In particular, I developed monad-effect, a fast, lightweight effect system.
Meowbot (Haskell): developed an AI chatbot application used by thousands of users.
Arch Linux btw: daily driver
I have strong skills in probability theory and Monte Carlo methods, including:
Good experience with measure-theoretic probability, taught online courses on the subject.
MCMC and dynamic causal modelling.
During the COVID pandemic, I built a prediction model (MCMC + dynamic causal modelling) to estimate pandemic peaks, supporting travel decision-making for thousands of people.
I also conducted a personal study of ~800 participants on the effectiveness of masks and practical techniques for reducing infection risk.
Wrote online textbooks in abstract algebra and measure-theoretic probability.
Taught online courses on probability.
Number theory · p-adic methods · Chabauty–Coleman · p-adic differential equations · probability · MCMC · causal modelling · Haskell · functional programming