Algorithmic, Operations, Research, Bioinformatics and Statistical Learning

Team Leaders: Jean-Marc DELOSME and Feng CHU

The team is developing fundamental and applied research activities in the algorithmic and in the machine learning fields with different scopes among which bioinformatics occupies a prominent place. The works are organized into two sections: on the one hand Algorithmic and Operations research and on the other Machine Learning and Bioinformatics.

The research of the first section is about developing efficient algorithms to provide optimal or close to optimum solutions with a guaranteed performance in order to solve complex (NP-hard) problems coming from various fields. We can especially quote scheduling issues for which we try to reduce the energy consumption, problems coming from graphs, transport and production systems, embedded systems and of bioinformatics. Another part of research concerns the algorithmic game theory (games design, study of Nash equilibrium, etc.) which enables to design complex systems in which a large number of users are interacting and are making decisions autonomously.

The research of the second section deals with analysis and prediction of structured data, modeling of dynamic systems and bioinformatics. Concerning the utilization of structured data, two types of approaches are developed: on the one hand, machine learning algorithms and models based on scalar kernels or operator-valued kernels in the regularization framework and on the other, algorithms of sequences modeling in the framework of grammatical inference. Modeling of high dimensional dynamic systems is also approached through the assessment of dynamic graphic models and differential equations. In bioinformatics and in systems biology, the team is interested in the prediction of micro RNA and in the inference of biological networks and develops efficient algorithms to solve these problems.

Last modified: 2015/02/19 08:55