Education
2021 — 2022
University of Oxford
Postgraduate study in Mathematical Sciences
2019 — 2021
University of Manchester
Undergraduate study in Mathematics
Numerical & Applied Mathematician · Quantitative Researcher
Numerical and applied mathematics for uncertain systems.
Numerical and applied mathematician and quantitative researcher working across Bayesian inference, statistical modeling, machine learning evaluation, and production-aware research systems. I care about how mathematical ideas survive contact with data, uncertainty, and real constraints.
01
Numerical & applied mathematics
02
Bayesian inference & uncertainty
03
Quantitative research systems
Based in
Hong Kong
Quantitative researcher at Grow Asset Management.
Trained in
Mathematics
Oxford (postgraduate) and Manchester (undergraduate).
Working on
Inference
Bayesian methods, model evaluation, and research systems.
Mathematical background
Numerical mathematics, Bayesian thinking, and research systems.
The academic thread behind the work: mathematical training, collaborative projects, and a continuing interest in how models meet uncertain data.
Collaborations & influences
Collaboration
Mathematical projects with Prof. Kody Law
Collaborated with Prof. Kody Law on several mathematical projects, working across probabilistic and computational questions about models, data, and uncertainty.
Research neighborhood
Bayesian inference, uncertainty, and models with data
My mathematical interests are closely related to Andrew M. Stuart’s work on Bayesian inverse problems, data assimilation, uncertainty quantification, and scientific machine learning.
Andrew M. Stuart at Caltech →Selected work
Research I can talk about openly.
Partial-order ranking, evolving networks, and nonlinear filtering — each note states the problem, the method, and what it demonstrates.
Professional systems
How I build research systems at work.
Described at the level of engineering judgment, since the implementation belongs to my employer.
Infrastructure
Research infrastructure
from data quality to model review
Workflows that connect source hygiene, signal review, model evaluation, and reproducibility checks.
data reliability reproducibility model evaluation
Guardrails
Decision-system guardrails
constraints before cleverness
Controls that make automated decision systems easier to monitor, constrain, and reason about when conditions shift or assumptions degrade.
guardrails monitoring operational safety
Observability
Evidence loops
runtime behavior back to research
Feedback paths that separate research assumptions from observed behavior, with emphasis on traceability, diagnosis, and clean post-hoc review.
observability diagnostics evidence trails
Experience
Quantitative research roles in Hong Kong and Shanghai.
Statistical modeling, machine learning evaluation, and the systems that keep both honest.
Quantitative Researcher
Grow Asset Management Limited · Hong Kong
2023 — Present
- Develop research workflows for systematic decision-making using statistical modeling and machine learning.
- Build data and model evaluation pipelines with an emphasis on reliability, reproducibility, and risk controls.
- Translate research ideas into production-aware systems that hold up under real operational constraints.
Quantitative Research Analyst, Financial Engineering Department
China Great Wall Securities · Shanghai
2021
- Researched market timing and fund allocation questions using statistical similarity and persistence signals.
- Produced weekly market reports and supporting analysis across equity indices, futures, and exchange-rate data.
Intern
Tencent · Internship
Mar 2020 · 1 mo
- Assisted the development of recommendation system algorithms for social network services and data mining techniques for customer relationship management.
Outside work
Ollie, Tigercrisp and Miyuki.
Three cats, each with a different opinion about how the work should be going.
Meet the cats →
Contact
Get in touch.
Email is best. LinkedIn and my CV are here too.