会议讲座

2017年09月04日 08:37 点击：[_showDynClicks("wbnews", 1191798723, 4568) ]

报告题目：Machine learning and complex networks for complex systems big data analysis

报告人：德累斯顿工业大学 Dr. Carlo Vittorio Cannistraci

报告时间及地点：2017/09/06（周三） 上午10:00-11:30，管理楼416

报告简介：The talk will present our research at the Biomedical Cybernetics Group that I established about three years ago in Technical University Dresden. We adopt a transdisciplinary approach integrating information theory, machine learning and network science to investigate the physics of adaptive processes that characterize complex interacting systems at different scales, from molecules to ecosystems, with a particular attention to biology and medicine, and a new emerging interest for the analysis of complex big data in economy and finance. 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 systems and network analysis. In particular, we deal with: prediction of wiring in networks and multiscale-combinatorial marker design for quantification of topological modifications in complex networks. Our attention for precision biomedicine is aim to subjects with important impact from the economical point of few such as development of tools for disease biomarker discovery, drug repositioning and combinatorial drug therapy.

This talk will focus on three main theoretical innovation. Firstly, Minimum Curvilinearity, which is a theory for topological estimation of nonlinear relations in high-dimensional data (or in complex networks) and its relevance for applications in big data. The new topic on the impact of Minimum Curvilinearity for network embedding in the hyperbolic space will be also treated, and the idea to develop quantitative markers in the latent geometry space will be introduced. Secondly, we will discuss the Local Community Paradigm (LCP), which is a theory proposed to model local-topology-dependent link-growth in complex networks and therefore it is useful to devise topological methods for link prediction in monopartite and bipartite networks such as product-consumer networks. Finally, we will discuss the importance of a new model we developed for the detection of rich-clubs in complex networks and the relevance of this topic for analysis of complex systems in biological, social and economic sciences.

个人简介：Dr. Carlo Vittorio Cannistraci is a theoretical engineer, head of the Biomedical Cybernetics Group and faculty member of the Department of Physics in the Technical University Dresden, which is a member of the TU9excellence-league that consists of the nine most prestigious technical universities in Germany. Carlo’s area of research embraces information theory, machine learning and complex network theory including also applications in computational network science theory, systems biomedicine and network neuroscience. Nature Biotechnologyselected 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 recent work on the local-community-paradigm theory and link prediction in bipartite networks. Nature Communication is going to publish soon his new theory on Coalescent Embedding of complex networks in the hyperbolic space.

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