Machine intelligence and network science for complex systems big data analysis

In this seminar Professor Carlo Vittorio Cannistraci will present the research at the Center for Complex Network Intelligence (CCNI) that he recently established in the Tsinghua Laboratory of Brain and Intelligence at the Tsinghua University in Beijing. They adopt a transdisciplinary approach integrating information theory, machine learning and network science to investigate the physics of adaptive complex networked systems at different scales, from molecules to ecological and social systems, with a particular attention to biology and medicine, and a new emerging interest for the analysis of complex big data in social and economic science.
The theoretical effort is to translate advanced mathematical paradigms typically adopted in theoretical physics (such as topology, network and manifold theory) to characterize many-body interactions in complex systems.
They apply the theoretical frameworks they invent in the mission to develop computational tools for machine intelligent systems and network analysis. They deal with: prediction of wiring in networks, sparse deep learning, network geometry and multiscale-combinatorial marker design for quantification of topological modifications in complex networks.

This talk will focus on two main theoretical innovation. Firstly, the development of machine learning for topological estimation of nonlinear relations in high-dimensional data (or in complex networks) and its relevance for applications in big data, with a particular emphasis on brain connectome analysis. Secondly, they will discuss the Local Community Paradigm (LCP) and its recent extension to the Cannistraci-Hebb network automata, which are brain-inspired theories proposed to model local topology-dependent link-growth in complex networks and therefore are useful to devise topological methods for link prediction in sparse deep learning, or monopartite and bipartite networks, such as molecular drug- target interactions and product - consumer networks.

Biography

Carlo Vittorio Cannistraci is a theoretical engineer, Zhou Yahui Chair Professor, Chief Scientist at the Tsinghua Laboratory of Brain and Intelligence (THBI), Director of the Center for Complex Network Intelligence (CCNI) at THBI. His research embraces information theory, machine learning and physics of complex systems and networks, with applications in systems biomedicine and neuroscience. For his contributions, he has received several awards, including the Young Investigator Award 2016 in Physics from the Technical University Dresden, the Shanghai 1000 talents plan award in 2019, and the National high-level talent program award from the Ministry of Science of China in 2021.