James Chok
James Chok
Associate Researcher
University of NSW
Associate Researcher
University of NSW
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University of NSW
I am currently a Research Associate at the University of New South Wales (UNSW), working remotely from Cambridge, UK. My current project focuses on ocean forecasting and optimization for reducing emissions from maritime transport and supply chains.
I am currently a Research Associate at the University of New South Wales (UNSW), working remotely from Cambridge, UK. My current project focuses on ocean forecasting and optimization for reducing emissions from maritime transport and supply chains.
I completed my PhD in Applied and Computational Mathematics at the University of Edinburgh. I began the PhD at the University of Sydney under the supervision of Dr Geoffrey M. Vasil. When Geoffrey moved to Edinburgh midway through my PhD, I followed him halfway across the world (like Ishmael).
I completed my PhD in Applied and Computational Mathematics at the University of Edinburgh. I began the PhD at the University of Sydney under the supervision of Dr Geoffrey M. Vasil. When Geoffrey moved to Edinburgh midway through my PhD, I followed him halfway across the world (like Ishmael).
My research interests span optimization, sampling, numerical analysis, and stochastic analysis. The common thread is the development of numerical methods for challenging computational problems, together with the mathematical analysis needed to understand why those methods work.
My research interests span optimization, sampling, numerical analysis, and stochastic analysis. The common thread is the development of numerical methods for challenging computational problems, together with the mathematical analysis needed to understand why those methods work.
I am particularly interested in Langevin dynamics and McKean–Vlasov processes, and in how ideas from stochastic differential equations and interacting particle systems can be used to design and accelerate computational algorithms.
I am particularly interested in Langevin dynamics and McKean–Vlasov processes, and in how ideas from stochastic differential equations and interacting particle systems can be used to design and accelerate computational algorithms.
Recent Publications and Preprints
Recent Publications and Preprints
J. Chok, M. Colbrook, M. Embree, and J. Fillman (2026). Contamination-free resolution of Cantor spectra and magic-angle localization in moiré quasicrystals and aperiodic media. Physical Review Letters [link]
J. Chok (2026). From Continuous Dynamics to Practical Gradient-Based Samplers. arXiv [link]
J. Chok, M. W. Lee, D. Paulin, and G. M. Vasil (2025). Divide, Interact, Sample: The Two-System Paradigm. arXiv [link]
Academic Positions & Education
Academic Positions & Education
September 2026 – Present · Research Associate, UNSW Sydney
Researching optimization, path planning, ocean forecasting, data assimilation, and reinforcement learning for maritime route optimization.
September 2022 – Present · PhD Candidate, University of Edinburgh
PhD thesis: Topics in Optimization, Sampling, Rational Function Approximation, and Operator Theory.
My research spans the mathematical foundations of optimization and sampling, rational approximation, operator theory, and high-performance scientific computing.
Teaching: High-Performance Computing for Mathematicians (PhD), Research Skills for Computational Applied Mathematics (MSc), Statistical Computing, and Python Programming.
August 2025 · Visiting Researcher, Nanyang Technological University, Singapore
Visited Dr Daniel Paulin to study Markov chain Monte Carlo methods for Bayesian modeling and extreme-value theory.
This collaboration resulted in the paper Divide, Interact, Sample.
September 2024 – August 2025 · Research Assistant, University of Cambridge
Worked with Dr Matthew Colbrook on spectral and fractal computations for twisted bilayer graphene, twisted bilayer Penrose quasicrystals, and Socolar quasicrystals.
This work resulted in the paper Cantor-Set Spectra in Moiré Quasicrystals and Beyond.
Led example classes for the Part III course Spectral Computations in Infinite Dimensions.
March 2020 – August 2022 · PhD Candidate, University of Sydney
Began my PhD in Applied Mathematics before transferring to the University of Edinburgh in 2022.
Researched polynomial-transform methods for improving the robustness of neural networks to adversarial attacks.
Teaching: Statistical Learning and Data Mining (MSc), Financial Time Series and Forecasting (MSc), Data Analytics for Business (MSc), and Mathematical Modelling.
March 2016 – December 2019 · BSc (Hons) in Applied Mathematics, University of Sydney
First Class Honours.
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