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Machine Learning

Computers can learn like us, from observations, examples, images, sensors, data, and experience. Machine learning is a field of artificial intelligence that involves the design and implementation of algorithms for computers to evolve their behavior from observations, examples, images, sensors, data, and experience, among many other sources. My main research in machine learning is on pattern recognition, a field of machine learning that involves a decision-making process based on pre-defined or learned patterns (one of the many possible definitions).

The main focus of my research in pattern recogntion is to devise efficient algorihtms for classification, clustering, feature selection and performance evaluation, with applications to interactomics, transcriptomics and data integration. Over the past 15 years, I have contributed quite a bit in many application areas (mainly in transcriptomics and interactomics) as can be seen below. In fundamental pattern recognition I have worked in statistical pattern recognition. A summary of my contributions is given below. More details about this research as well as relevant references will be posted soon.