Despite the remarkable unity of the basic components of the living world (DNA, RNA, the genetic code), we are probably still not aware of the diversity of genome structures nor of the diversity of means genomes use to evolve. In addition to local mutations affecting genome sequences, various global mutations also affect their overall gene order and content: rearrangements, horizontal gene transfer, hybridization, losses, duplications ranging from single genes to the whole genome.

By comparing complete or partial genomes it is possible to infer evolutionary scenarios for gene families, gene clusters or entire genomes, and to predict ancestral characteristics. This has important consequences, not only for documenting the evolutionary history of life on earth, but also for answering many fundamental biological questions regarding gene function, adaptation processes and variations on the genetic and physiological specificities of species. Each problem, each type of mutation (or set of mutations), has its own model and gives rise to specific algorithmic, combinatorial, statistical and mathematical developments. Our research projects are related to these computational biology aspects of comparative genomics.

My research interests are centered around discovering fundamental computational principles that underlie the extraordinary capabilities to learn from the environment, understand it and adapt to it that characterize intelligence. The development of novel machine learning algorithms based on such principles, and trained on very large data sets, is at the heart of the latest technological breakthroughs in artificial intelligence.

More specifically, I research how higher level representations that carry meaning can be constructed autonomously, starting from streams of raw sensory input (such as images and sounds). Similarly to what our brain's neural networks naturally know how to do, this amounts to intelligently modeling the structure of the observed reality, by discovering and exploiting hidden and complex statistical regularities that it follows.