Formal Languages and Automata Theory
Our research is focused in the Formal Language Theory mainly frameworked in the Chomsky's Hierarchy.
We work on closure properties of formal languages, grammatical approaches to characterize new (sub)classes of
formal languages and computational devices to define them. Particularly, we are interested in the
class of regular languages characterized through the Finite Automata Theory. We work on the study between
the relations of deterministic and non-deterministic finite automata, structural characterizations of
classes of regular languages, descriptional complexity of regular languages and their relationship to
grammatical inference. In addition, we work in semigroup theory applied to formal language theory, particularly
we study positive varietes and other characterizations of regular languages through an algebraic approach.
Our research is focused in the areas of natural computing inspired by biochemical and biocellular
processes. This is also a part of what is called unconventional computation. We are interested
in the computation models induced by DNA, RNA and protein processes (such as recombination, complementarity,
replication through PCR, etc.) With respect to biocellular processes we work on Membrane Computing (P systems),
Networks of Biologically-Inspired Processors and other related models. In addition, we are interested in the
relationship between these new models of computation and other areas of natural computing such as evolutionary
Processing of biosequences and Bioinformatics
Our research is focused in the design of efficient algorithms to solve problems related to the
analysis and prediction in biosequences. We mainly work on sequences of nucleotides for
DNA/RNA in order to solve problems such as multiple alignment, functional prediction, and gene expressions,
among others. We also work on sequences of aminoacids for proteins
in order to solve problems such as motif prediction and secondary structure prediction, among others.
We usually apply Grammatical Inference techniques in order to build efficient models to carry out the
previously referred tasks. In addition, we work on grammar construction and kernel methods to achieve
some of our goals.
Grammatical Inference and Applications
Our research is focused in the design and the study of efficient grammatical inference methods.
We work in the framework of Gold's Identification in the Limit. We characterize
new language classes under the learnability criterium and we study the properties of the hypothesis representations such
as finite automata or formal grammars. We study the performance of the proposed grammatical inference algorithms with
respect to the time and space complexities. Our methods have been succesfully applied in different tasks such as OCR,
speech recognition, dialogue modelling, machine translation and biosequences processing.