Position
I work at the intersection of stochastic analysis and computation, and coordinate the MSc in Stochastics and Financial Mathematics.
For questions, or if you are looking for a thesis project, write to me.
Research
Our research combines stochastic analysis, data-driven modelling, and computational finance. In energy and rates markets, forward curves are functions of maturity, so their volatility is an operator rather than a number. We develop affine processes on cones of Hilbert–Schmidt operators to keep these infinite-dimensional models tractable, with results on existence and uniqueness, stationary covariance regimes, finite-rank approximations, and Fourier pricing through Riccati equations.
In power markets, we develop models and algorithms for optimal execution under Hawkes processes with transient impact, measure-valued CARMA dynamics for forward curves, and the pricing and semi-static hedging of pay-as-produced power purchase agreements.
Further, we research high-frequency time-series and optimal trading using path-signatures.
Affiliations
- Stochastics group, Korteweg–de Vries Institute for Mathematics
- Computational Science Lab, Informatics Institute
- AI4Fintech, bridging AI and financial technology
Supervision
- Robust finance in infinite-dimensional models
with Diogo Sousa Franquinho - Hedging and trading in energy markets
with Konstantinos Chatziandreou - Machine learning for high-frequency financial time series
with Gianmarco Morbelli
Funding
- 2025
€25,000 - Deep Spatio-Temporal Hedging for Weather and Climate Risk
Mitigation in Renewable Energy Markets
Amsterdam University Fund. Announcement.
- 2025
€35,000 - Risk Networks of Renewable Energy Markets
With Simon Trimborn, under the UvA Energy Transition research priority area. Announcement.
Teaching
I teach two MSc courses at the University of Amsterdam: Portfolio Theory, on the stochastic foundations of mathematical finance in discrete time, and Computational Finance, on the numerical methods that turn those models into prices and hedges. Both syllabi are on the teaching page.
Elsewhere
For fun, I scraped the official draw of the 2026 World Cup, built a Monte-Carlo simulator of the full 48-team tournament, and calibrated team strengths from bookmaker odds, the betting exchange, an Elo model, the FIFA ranking and squad transfer values. Of the six forecasts on the resulting dashboard, mine gave the eventual champion the highest title probability — Spain at 24.99% against a market consensus of 15.13% — and the best log score of the six. It also held the biggest miss: runner-up Argentina at 4.63%, the lowest of any system.
The dashboard, and a short film about how it was built. Forecasts were frozen on 8 June 2026 and have not been refitted.