Curriculum Vitae

Conducting research in structure-preserving machine learning for dynamical systems, with a focus on methods that encode invariants and symmetries to improve reliability, interpretability, and long-horizon prediction.

The focus of my thesis was structure-preserving model reduction and inference for high-dimensional dynamical systems.

• Developed data-driven algorithms for extracting, identifying, and enforcing invariants and symmetries in time-series data.
• Developed data-driven methods for structure-preserving approximation of the Koopman operator, enabling spectral analysis and estimation of dynamical systems with continuous symmetries.
• Constructed thermodynamically consistent reduced-order models via the Mori–Zwanzig formalism, supporting stochas- tic coarse-graining of high-dimensional dynamical systems.

Awarded Master of Engineering with First Class Honours.

Designed the mechanical, electronic, and embedded control systems for a closed-loop fast steering mirror used in a 10 Gb/s air-to-air laser communication demonstrator, enabling precise beam alignment with a fiber optic receiver.

Designed the electronics and STM32 embedded firmware for a skydiving altimeter, implementing a Madgwick filter sensor fusion algorithm for real-time orientation estimation from accelerometer and gyroscope data.

Developed algorithms for symmetry detection in dynamical systems by reconstructing infinitesimal generators from trajectory data (ODEs and PDEs), enabling dimensionality reduction, invariant solution construction, and phase-space analysis.