In an era marked by complex and interconnected global challenges, the integration of mathematical modelling, analysis, and simulation with data-driven methodologies provides a powerful framework for understanding and managing real-world systems. This talk explores research at the intersection of applied mathematics, machine learning, and computational science, demonstrating how classical modelling-particularly motivated from epidemiology-can be reimagined and extended through modern computational tools.
The presentation will highlight a range of mathematical and computational strategies, including optimal control theory to design effective intervention policies and Physics-Informed Neural Networks (PINNs) for solving inverse and forward problems modelled by differential equations. Emphasis will also be placed on interdisciplinary integration, showcasing how tools from dynamical systems, control theory, and deep learning can be combined to build robust, interpretable, and scalable models. In addition to research developments, the talk will also spotlight recent efforts in developing innovative academic programs and pathways in Data Science and Computing Education, aiming to equip the next generation of scientists and engineers with the necessary skills for data-centric problem solving.
