ROSA MARIA
CRUJEIRAS CASAIS

Rosa M. Crujeiras is a professor in the Department of Statistics and Operations Research (Department of Statistics, Mathematical Analysis and Optimization) at the University of Santiago de Compostela (USC). She holds a degree in Mathematics (2001), a Diploma of Advanced Studies (2003), and a PhD in Statistics and Operations Research (2007, thesis entitled "Contributions to Spectral Spatial Statistics"). She completed her studies at USC, completing predoctoral stays in the United States and the United Kingdom. After a postdoctoral position at the recently created Statistical Consulting Service of USC, in 2008 she obtained another position as a postdoctoral researcher at the Universitè catholique de Louvain, Belgium. In January 2009, she returned to USC as an interim professor, developing her academic career there. She is a researcher in the Optimization, Decision-Making, Statistics and Applications Models (MODESTYA) group. Since September 2021, she has been Scientific Director of the Center for Mathematical Research and Technology of Galicia, CITMAga. Her career was recognized with the 2023 Young Scientific Talent Award from the Royal Academy of Exact, Physical and Natural Sciences, and in 2024, together with professors M. Alonso and I. Gijbels, she received the Award for the Best Methodological Contribution in Statistics from the Society of Statistics and Operations Research (SEIO) and the BBVA Foundation (FBBVA). Her research activity has been recognized with three six-year terms (the maximum possible). Rosa M. Crujeiras's research has focused on the development of nonparametric methodology for data analysis in complex contexts: spatially dependent processes, variables observed in non-Euclidean media (circular and directional data), and, more recently, data with partial information (doubly truncated). She primarily worked on spatial dependence in her thesis and initial postdoctoral period, before embarking on a new line of research on directional data, a topic unprecedented in the research group and one that has earned her significant international recognition. Merging these recent advances with the topic on which she began her academic career, she has also worked on the estimation and testing of spatial circular processes. These fields of statistics require the use of advanced tools of mathematical analysis and Riemannian geometry, which lays the groundwork for the theoretical understanding of stochastic models for complex data and constitutes an excellent example of the need for interdisciplinarity to achieve relevant scientific advances. His most recent contributions in the field of directional data offer proposals for nonparametric density estimation and regression with circular data, using in the latter context both regression to the mean and modal regression (a completely novel approach for this type of data), as well as local likelihood regression. She has advised 8 doctoral thesis in this area. He has published in prestigious and high-impact journals in Statistics, of a methodological nature such as Journal of the American Statistical Association, Biometrics, Annals of Applied Statistics, Scandinavian Journal of Statistics, Electronic Journal of Statistics, Test, Stochastic Environmental Research and Risk Assessment or Journal of Statistical Software, among others.