Section: Application Domains

Application Domains

This short section is only concerned with the list of concrete application domains developed by our team project on Bayesian inference and unsupervised learning, nonlinear filtering and rare event analysis. Most of these application areas result from fruitful collaborations with other national institutes or industries.

Three application domains are directly related to evolutionary computing, particle filtering and Bayesian inference. They are currently investigated by our team project:

1. Multi-object tracking. Multi-object tracking deals with the task of estimating the states of a set of moving objects from a set of measurements obtained sequentially. These measurements may either arise from one of the targets or from clutter and the measurement-to-target association is generally unknown. This problem can then be recast as a dynamic clustering one where the clusters are the clutter and the different targets. The targets actually move in time, some targets may appear/disappear over time and the number of targets is generally unknown and time-varying. The ALEA team has been involved in the ANR project Propagation with DCNS, Thalés and Exavision.

2. Finance. The Team ALEA is interested in the design and analysis of new advanced particle methods for option pricing, partial observation problems, and sensitivity measures computation. An international workshop has been jointly organized by ALEA and CMAP (Polytechnique) on this topic in October 2012 (organizers: E. Gobet, P. Del Moral, P. Hu).

3. Epidemiology. Our team is interested in the development and analysis of particle mean field models for the calibration and uncertainty propagations in complex kinetic population models. The ALEA team is involved in an interdisciplinary exploratory research project with Laboratory Ecologie & Evolution, and co-organized an international workshop on this topic in 2011.