Carlos García Meixide's research focuses primarily on causal inference under hidden confounding and its implications for statistics and machine learning. He has extensive experience in neurological disease progression analysis from the pharmaceutical industry and collaborates with Mark van der Laan at CTML as part of his PhD, which he pursues in Madrid at the National Research Council of Spain. He completed his MSc in Statistics at ETH Zürich and his BSc in Mathematics and Physics at Universidade de Santiago de Compostela and the University of Southampton. He aims to innovatively guide theoretical statistics toward maximum simplicity in practical applications, bridging rigorous mathematical foundations with straightforward implementation. Beyond research, he is passionate about teaching and maintains active interests in music performance and fitness.
Empirical processes, high-dimensional statistics, semiparametric theory, data fusion.
