Who we are

We are a research group at the University of Turin with two main interests: computational biology and theory of neural networks (and sometimes these two topics overlap!)

Computational Biology

We apply methods from physics and mathematics to the study of many different topics in biology, including gene differentiation and reprogramming, whole genome duplication, and sequencing. By integrating bioinformatics, statistical modeling, and machine learning, we aim to understand the complex networks governing these different phenomena. Additionally, one of our main interests is the study of the scale laws that govern biological systems, trying to find the universal principles underlying biological organization. Another research topic that we pursued is the intrinsic dimensions of biological datasets, using dimensionality reduction and manifold learning methods to find meaningful trends from complex biological data.

Neural Networks

Our neural networks section is dedicated to the theoretical explorations of modern machine learning architectures. We believe that it is crucial to focus our efforts on the theoretical aspects of neural networks, given that their development relies not on a deep understanding but on trial and error. We use tools from physics, such as statistical mechanics and dynamical systems theory, and apply them to these systems to (hopefully) better understand their underlying principles.