This group focuses on Machine Learning approaches,
with the aim of establishing new mathematical foundations of intelligent systems.
Machine learning is the academic field concerned with the development of algorithmic techniques that can extract knowledge from available data, and, in doing so, can provide principled approaches to analyze new, unseen data.
The members of our group investigate theoretical foundations and implementations of such tools, using different approaches such as: computation theory; non-parametric statistics; optimization theory.
We are also interested in developing coherent systems for knowledge discovery, and clarify relationships between machine learning, mathematical logic, and algebra.