• Data Mining techniques

I am interested in Machine Learning and Statistical Learning algorithms for pattern analysis. My research focuses on their application within different fields as insurance, bank, marketing, customer intelligence, environmental sciences, biomedicine… During the last few years I have been involved in the internals of Random Forests learning mechanism, for which I am conducting research to develop new facilities and utilities.       

• Bioinformatics (high-dimensional data)

An in fashion domain where data mining has found challenging applications is bioinformatics, where high throughput technologies have provided the scenario for harvesting large amounts of biological and genetic data. Classical procedures are not well suited for the analysis of these data sets; hence, new research must be developed to deal with them. My research is concerned with gene expression and proteomics data analysis, where one of the main issues is the identification of hidden genomic patterns with biological and medical implications that shed light on the internal molecular mechanisms involved in diseases. The main feature of this type of data is its high dimensional - low sample structure, which makes the analysis difficult and cumbersome. I am developing algorithms and procedures able to extract relevant and useful information from these data sets, which are expected to point out to new biological findings and medical advances. I have been involved with mRNAs expression data in breast cancer as well as with proteins and gene expression data in bladder and colon cancers.

• Multivariate statistics

My focus is on the study of flexible models that account for non-normality in many real life applications where the data at hand does not fit a normal distribution. I am interested both in the theory of multivariate non-normal models, like skew-normal, elliptical or skew-elliptical distributions, as well as in their applications for data modeling in real life problems.

• Asymptotic Statistics

Perhaps, this is the most theoretical branch of my research. It traces back to the foundations of asymptotic statistics. I am quite interested in saddlepoint approximations, which was the topic of my Ph.D Thesis. I have explored the connection between Edgeworth expansions and saddlepoint approximations, as well as the link between the inversion of the former ---i.e. the Cornish-Fisher approximations--- and the inversion of the latter.