About Joe Bunster

What interests me

I’m interested in how we model uncertainty, how assumptions shape the answers we get, and how mathematical ideas become useful tools for real decisions.

My interests include optimal transport, optimal control, probability, stochastic modeling, time-series analysis, and machine learning. My master’s thesis explored optimal transport in financial time series; my undergraduate thesis examined optimal control in retirement portfolio management. Both reflect a question that continues to motivate me: how can we make better decisions as conditions change?

Today, my work spans portfolio regimes, model risk, and the evaluation of AI reasoning. I’m particularly interested in understanding when models are reliable, where they break down, and how to evaluate and improve them.

Through Regime Alpha, I explore how portfolio risk behaves across market conditions. Through HomeOnCue, I bring the same interest in clear, useful decision-making to a practical consumer product.

I enjoy work that connects mathematical depth with something people can inspect, use, and improve. I welcome conversations with researchers, technical teams, and builders working on thoughtful problems in quantitative finance, applied mathematics, and AI.

How I like to work

I enjoy turning an open-ended problem into something people can use. I use AI for research, prototyping, and development, while staying responsible for the assumptions, technical choices, and quality of the result.

I’m drawn to startups and established teams where I can own meaningful work from idea to launch. I’m comfortable moving between research, engineering, product, and strategy as the problem requires—defining what matters, building a first version, and improving it through feedback.

Professional experience

Education & research foundations