My research interests are supervised and unsupervised Statistical Learning, and includes:

Classification Methods. Including statistical-based techniques such as Logistic Regression or Linear Discriminant Analysis (LDA) and machine learning approaches, such as Classification Trees, Support Vector Machines (SVM) and ensemble methods. I'm interested specially in Boosting (AdaBoost); one of the aims of my research is to apply these methods in data with ordinal response.

Multidimensional data analysis and kernel methods. I'm interested in methods for data analysis in high dimensional space, including clustering and reduction dimension methods, such as Principal Component Analysis (PCA) and Projection Pursuit techniques
. Generally one tries to find nonlinear structures in data, and this can be done by using implicit transformations , which maps the data into a higher dimensional space where we can use linear methods. The latter is called kernel methods, and I'm specially interested in Kernel PCA, a nonlinear version of PCA and the use of Gaussian kernels.


Another topic of interest (which was the topic of my master thesis) is component and system reliability, which includes repairable and nonrepairable systems (it's not software engineering!).