Ivo Couckuyt

Prof. dr. ir. Ivo Couckuyt leads the data-efficient Machine Learning (DE-ML) research effort of the Surrogate Modelling (SUMO) cluster in IDLab at Ghent University. At the same time, he is affiliated with imec, a world-leading R&D and innovation hub in nanoelectronics and digital technologies. Ivo Couckuyt contributes more than 10 years of experience in surrogate modeling, data-efficient machine learning (DE-ML), active learning, digital twins, and design space exploration and optimization techniques. During his career, he published more than 100 peer-reviewed papers and abstracts in international conference proceedings, journals, and books. He supervised more than 15 Ph.D. students, holder of multiple patents, and has won several paper awards. His interests include surrogate modeling, surrogate-based optimization, and physics-informed Artificial Intelligence for complex engineering problems, including techniques such as Bayesian optimization and Gaussian processes. Surrogate models, or digital twins, are fast-running approximations that mimic the complex behavior of a system, and they can be used for various engineering tasks, including design, modeling, optimization, uncertainty quantification, and control.