DS
Data Science & Artificial Intelligence
A huge amount of knowledge is hidden in the data, waiting to be extracted and exploited.
Objective:
To develop new statistical, machine learning and optimisation methods that can extract knowledge from the large amount of data generated nowadays.
Description:
In the applied statistics field, the main topics of our research are semi-parametric regression, multidimensional smoothing, (Bayesian) hierarchical models, computational statistics... Regarding Machine learning, we work on supervised and unsupervised classification of massive data, probabilistic graphical models, time series, Bayesian optimisation, etc. In optimisation we pursue the developments of efficient metaheuristics methods.
Applications:
Massive data and optimisation problems from financial to social media, marketing, medical domains (diagnosis and prognosis), genetics, environmental modelling, demography and biostatistics, logistics, scheduling and planning.
