Education
2021 — 2022
University of Oxford
Postgraduate study in Mathematical Sciences
2019 — 2021
University of Manchester
Undergraduate study in Mathematics
Quant research · model evidence · reliable systems
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
Public evidence
3 notes
research projects that can be discussed openly
Private systems
sanitized
capability cards without proprietary implementation detail
Contact surface
direct
email, LinkedIn, and CV only; no noisy form funnel
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
Evidence first, details only where they can be public.
The page separates open research from professional systems work. Public projects get direct notes; private systems are kept deliberately abstract.
Sanitized systems
Capability without leaking the machinery.
No proprietary logic, performance numbers, integration details, or internal names. Just the shape of judgment.
Capability · abstracted
privateResearch infrastructure
from data quality to model review
Workflows that connect source hygiene, signal review, model evaluation, and reproducibility checks without exposing proprietary implementation details.
data reliability reproducibility model evaluation
Abstracted by design
Capability · abstracted
privateDecision-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
Abstracted by design
Capability · abstracted
privateEvidence 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
Abstracted by design
Experience
Research background with production instincts.
A quieter timeline: enough context to establish credibility, without turning the homepage into a full CV.
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 while keeping sensitive professional details private.
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 the lab
Meet the small personalities behind the research lab.
Ollie, Tigercrisp, and Miyuki each have their own approach to supervising the work.
Meet the cats →
Contact
Useful conversations only.
Email, LinkedIn, or CV. No form, no funnel, no unnecessary surface area.