Dr. Cannistraci is a theoretical engineer and computational innovator. He is a Professor in the Tsinghua Laboratory of Brain and Intelligence (THBI) and an adjunct professor in the Department of Computer Science and in the Department of Biomedical Engineering at Tsinghua University. He directs the Center for Complex Network Intelligence (CCNI) in THBI, which seeks to create pioneering algorithms at the interface between information science, physics of complex systems, complex networks and machine intelligence, with a particular focus in brain/life-inspired computing for big data analysis. These computational methods are often applied to precision biomedicine, neuroscience, social and economic science.
His area of research embraces information theory, machine learning and physics of complex systems and networks including also applications in systems biomedicine and neuroscience.
I will present our research at the Center for Complex Network Intelligence (CCNI) that I recently established in the Tsinghua Laboratory of Brain and Intelligence at the Tsinghua University in Beijing.
We 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. Our 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.
We apply the theoretical frameworks we invent in the mission to develop computational tools for machine intelligent systems and network analysis. We 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 and computational solutions for network geometry, 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, we 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.