DrAnton van Beek

My career goal is to become a leading expert in data-driven methods that accelerate scientific discovery and to translate those insights to design systems that address pressing societal challenges. Scientists and engineers increasingly rely on costly, time-consuming experimental data to drive discoveries. Integrating such data with advances in machine learning and computational infrastructure offers unprecedented opportunities, fueling a technological revolution across disciplines. However, care must be taken when using these methods to infer fundamental principles, especially given heterogeneous data sources (e.g., experimental uncertainties, mixed variable types, and varying fidelities) and regularization strategies that control model complexity. These factors can distort inference and lead to misleading insights. My research centers around developing statistically rigorous, data-driven methods that produce reliable conclusions from complex data and support sustainable, adaptive system design. At its core, my work views science and engineering as sequential decision-making processes where data informs models, guides actions, refines understanding, and accelerates discovery.