Postdoctoral researcher at the Professorship of Computer Science in Mechanical Engineering, Helmut Schmidt University, Hamburg, Germany. PhD in Mathematics. Her current research focuses on enhancing the performance of real-world systems modelling (physics, biology, chemistry, finance and engineering) by exploring the symbiosis of differential equations and neural networks and the integration of expert-knowledge, in the form of explicit constraints, into neural networks. Her research topics are: Theoretical Machine Learning; Optimisation for Machine Learning; Scientific-Machine Learning; Differential Equations; Numerical Analysis; Climate AI. Publication in major AI venues including ICLR 2023/2024 and workshops at ECML PKDD 2023 and ECAI 2023/2024. Actively contributes to the academic community as a reviewer for major AI conferences, including NeurIPS, ICLR, and IJCAI. Organizer of the ECAI 2024 (mlde-ecai-2024.github.io) and 2025 (mlde-ecai-2025.github.io) Workshop [ML-DE] "Machine Learning Meets Differential Equations: From Theory to Applications", and the Mini-Colloquia "Integrative approaches in physics: using machine learning to explore magnetism, disordered media, and materials science." at the General Conference of the Condensed Matter Division (CMD31) 2024 (cmd31.sci-meet.net/mini-colloquia). Organiser and chair of the ECAI 2024 (symbiosisnn-des.github.io) and 2025 (mlde-ecai-2025.github.io) Tutorial "The Symbiosis of Neural Networks and Differential Equations: From Physics-Informed Neural Networks to Neural ODEs" and ECML 2025 (nnsdestutorial.github.io) Tutorial "Neural Networks and Differential Equations: From Infinite Layers to Continuous Modelling.".

Research Interests:  Neural Networks; Scientific-Machine Learning;  Neural Ordinary Differential Equations; Physics-Informed Neural Networks; Optimization for Machine Learning; Differential Equations;  Real Systems Modelling, Time Series; Climate AI;