My research aims at the study of Adaptive algorithms: overall, my main concern over the last 15 years (and more) has been about the study, design, implementation, ... of algorithms that adapt to their time varying environment. The algorithm may be either dedicated to control an agent, or predict some quantity, or whatever. In this endeavor, I shifted from genetic algorithms to the field of machine learning, and particularly the sub-field of reinforcement learning, also known as approximate dynamic programming.
My mid-term objective is to study adaptive algorithms in time-varying environments. There is a paradox in current main stream research in reinforcement learning for instance: RL is fundamentally dedicated to adatation to changing environements; however, the very large majority of papers deals with non varying environments! This is clearly a bias towards what we know how to study, rather than studying what really matters.
A closely related theme is that of non asymptotic behavior of algorithms; in a time varying environment, we are interested in the adaptivity of an algorithm to its current environement, and before the environment changes too much; so, asymptotically optimal algorithms are really not what we care of in changing environments.
To reach this goal, an algorithm has to represent its environment and I think that this representation should also adapt to the changes of the environment. This led me to get interested in non parametric function approximators, such as neural networks which architecture changes over time, and kernel machines; these functions approximators let us discover useful feature in a very natural way; thus, I got a strong interest in sparse representation, l1 regularization, LARS, kernelized-LARS, compressed sensing, ...
As I am a computer scientist, I am really interested in working algorithms, effective, and efficient. Theorems are all nice, but an algorithm that really does something useful, in a reasonnable amount of time, is much better to my eyes. And if the algorithm rests on sound principles, and a sound theory, and works efficiently in practice, and is able to scale up with the amount of data, it is a beautiful algorithm; there are not so many...
I have a strong interest towards web application, such as search engine, recommendation systems, and computational advertizing. These problems may easily be formulated as sequential decision making problems.
Ok, now you have to shift to french, sorry about that(-(: