Machine intelligence and network science for complex systems big data analysis

Prof. Dr Carlo Vittorio Cannistraci, Center for Complex Network Intelligence Tsinghua University, Beijing, China

Abstract: 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. In particular, 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 for topological estimation of nonlinear relations in high-dimensional data1 (or in complex networks2) and its relevance for applications in big data3, with a particular emphasis on brain connectome analysis4. Secondly, we will discuss the Local Community Paradigm (LCP)5,6 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 monopartite5 and bipartite6 networks, such as molecular drug-target interactions7 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 and member of the Department of Computer Science and the Department of Biomedical Engineering at the Tsinghua University, Beijing, China. Carlo’s area of research embraces information theory, machine learning and physics of complex systems and networks including also applications in systems biomedicine and neuroscience. Nature Biotechnology selected Carlo’s article (Cell 2010) on machine learning in developmental biology to be nominated in the list of 2010 notable breakthroughs in computational biology. Circulation Research featured Carlo’s work (Circulation Research 2012) on leveraging a cardiovascular systems biology strategy to predict future outcomes in heart attacks, commenting: “a space-aged evaluation using computational biology”. The Technical University Dresden honoured Carlo of the Young Investigator Award 2016 in Physics for his work on the local-community-paradigm theory and link prediction in monopartite and bipartite networks. In 2017, Springer-Nature scientific blog highlighted with an interview to Carlo his study on “How the brain handles pain through the lens of network science”. The American Heart Association covered this year on its website the recent chronobiology discovery of Carlo on how the sunshine affects the risk and time onset of heart attack. In 2018, Nature Communications featured Carlo’s article entitled “Machine learning meets complex networks via coalescent embedding in the hyperbolic space” in the selected interdisciplinary collection of recent research on complex systems. In 2019, Scientific Reports selected Carlo’s interview between all their Editors to represent the journal in the social media. In 2019, Carlo won the Shanghai 1000 talents plan award, sponsored by CAS-MPG Partner Institute for Computational Biology. In 2020, Carlo was awarded of the Zhou Yahui Chair Professorship of Tsinghua University. In 2021, Carlo’s won the National high-level talent program award from the Minister of Science of China.

References: Google Scholar