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About

Lea Bold is an emerging researcher at the intersection of data-driven control theory and robotics, with a particular focus on Koopman operator methods and nonholonomic systems. Her work addresses a fundamental question in modern robotics: can data alone replace the mathematical rigor of geometric modeling? Through her investigations into Koopman-based surrogate models, Bold has demonstrated that while data-driven approaches offer compelling advantages, the underlying geometry of nonholonomic systems remains indispensable for effective control design. Her most influential contribution, "Data-driven predictive control of nonholonomic robots based on a bilinear Koopman realization," has accumulated 6 citations since its 2025 publication, reflecting rapid uptake within the robotics and control communities. By developing bilinear Koopman realizations that capture the multiplicative coupling between states and controls inherent to nonholonomic robots, Bold has advanced the theoretical foundations of extended dynamic mode decomposition beyond its conventional linear formulations. Her research offers a nuanced perspective that bridges machine learning enthusiasm with classical systems theory, making her work particularly valuable for roboticists seeking principled, data-informed control strategies. Bold represents a new generation of researchers thoughtfully interrogating the limits and opportunities of purely data-centric paradigms.