United Kingdom
Marie Curie PhD Researcher in mathematics, specialized in nonlinear/stochastic dynamics, climate science, and weather applications. Passionate about solving real-world problems using mathematical modelling, data analysis, machine learning, and open-source software development. Advised an economics think tank on climate risks and supervised a master’s student. Proactive interdisciplinary team player (e.g., in climate economics) who convened multiple international conferences and enjoys communicating research to diverse audiences.
- Developed methods to analyse and predict extremes and transitions in stochastic/nonlinear/complex systems (like the climate) using stochastic modelling and chaos theory - Co-development of an open-source software package for transitions and extremes in stochastic systems. - Convened multiple international conferences on mathematics and machine learning for climate science.
- Supervised a master's student on their thesis on modelling the dynamics of the Atlantic Meridional Overturning Circulation (AMOC) using chaos theory and large deviations theory. We derived the "chaotic Kramers' law" - currently in peer review for publication - Developed a new climate economics model (IAM) and used it to compute optimal policies under climate model uncertainty and robustness concerns. The model: Response theory and machine learning-based CMIP6 temperature emulators are coupled to a DICE-type economic model, and a decision theory framework addresses the climate model uncertainty and the robustness concerns.
- Advised an economics think tank on risks from climate tipping points.
- Analysed and explained chaotic dynamics in models of the Atlantic Meridional Overturning Circulation (AMOC)
Taught tutorials, marked and contributed to setting exercise sheets for the courses: - "Classical Field Theory" (Oct 2020 - Mar 2021) - "Mathematics for Physicists 2" (Apr - Sept 2018)