Beatriz Piñeiro Lamas, visiting Ph.D. student from Universidade da Coruña
Beatriz Piñeiro Lamas, a Ph.D. student in Statistics at Universidade da Coruña, recently joined ENSAI for a 3-month visit. Currently working on mixture cure models in biomedical applications, she hopes to make the most of her interactions with professors and fellow Ph.D. students.
ENSAI encourages research stays for international Ph.D. students and professors, as mobility and face-to-face contacts broaden academic horizons.
Could you tell us more about your home university?
Beatriz Piñeiro Lamas: I started my PhD two years ago at Universidade da Coruña. Coruña is one of the main cities of Galicia, a beautiful region in the Northwest of Spain. There, I belong to the Modeling, Optimization and Statistical Inference group (MODES). I work at CITIC (Center for Information and Communications Technology Research) with other predoctoral and postdoctoral researchers. I also teach classes.
What did you do before starting a Ph.D.?
I obtained my undergraduate degree in Mathematics at Universidade de Santiago de Compostela in 2018, and my final project was about survival analysis. I started getting interested in statistics and biomedical applications, so after my degree, I decided to join the Interuniversity Master in Statistical Techniques. I also worked as a research support technician at ITMATI (Technological Institute for Industrial Mathematics) and CINBIO (Biomedical Research Centre, in Universidade de Vigo). Finally, I did a Biomedical Research Master, also in Santiago de Compostela, with the aim of improving my background beyond mathematics and statistics. I did the final projects of both Masters degrees at CIMUS (Center for Research in Molecular Medicine and Chronic Diseases), as a member of the Genomics and Bioinformatics group.
What is the subject of your Ph.D. thesis?
Its title is ‘High dimensional single-index mixture cure models’. Mixture cure models are used to study time to event data when there is a fraction of cure. For example, I focus on time until cardiotoxicity (a side effect that some oncological treatments can cause in the heart). Fortunately, not every patient receiving the treatment is susceptible to develop that health condition, and for this reason, we say that we have a proportion of cured patients in that sense. The aim here is to study two functions: the probability of being cured (not suffering cardiotoxicity) and the survival function of those susceptible to developing that side effect. In addition, we are interested in analyzing the effect that some covariates can have on both functions. There is literature available in this context in the presence of a univariate covariate, and we are trying to extend it to the presence of vector and functional ones. We consider a single-index approach to avoid the curse of dimensionality.
My supervisors are Ana López-Cheda and Ricardo Cao. Ana is a distinguished postdoctoral researcher (Beatriz Galindo program). Her main research area is survival analysis and, in particular, cure models. Ricardo is a full professor in Statistics and Operational Research and he has a lot of experience in many fields such as nonparametric statistics, bootstrap, and survival analysis, among others.
My main research area is survival analysis. In particular, cure models. In my thesis, I also have to deal with functional data. Although I focus on biomedical applications, the methodology of my research can be useful in other fields.
In what ways do you think visiting ENSAI will enrich your research?
I think that working with Professor Valentin Patilea will have a great impact on the functional part of my thesis. He has a lot of experience in that area, so I hope to take advantage of these three months with him and learn a lot. Moreover, doing an international stay also helps me to be more fluent in communicating my results in English. And, of course, it’s a perfect opportunity to improve and practice my French!
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