报 告 人：Edwin R. Hancock教授

单位：英国约克大学计算机科学学院

报告时间：2019年7月8日（周一）14：00～15:00

报告地点：宝山校区计算机大楼1104室

邀请人：王健嘉

报告摘要：

Computing the entropy of a network has proved to be an elusive problem, with potentially enormous impact on the fields of machine learning, complex systems and big data. In this talk I will present an overview of recent work that has shown how ideas from spectral graph theory and statistical physics can be brought to bare on the problem, yielding simple methods for computing network entropy. The topics covered include detecting anomalies in network time series, modelling the time evolution of networks and decomposing networks into frequently occurring substructures, referred to as motifs. I will furnish examples from the financial and medical domains to illustrate the application of these techniques.

报告人简介:

Edwin R. Hancock教授，任职于英国约克大学，是世界计算机视觉与模式识别领域的著名专家，国际模式识别协会（International Association for Pattern Recognition, IAPR）副主席，IEEE Fellow，IAPR Fellow，IET Fellow，Fellow of Institute of Physics，同时是国际模式识别领域权威期刊Pattern Recognition的主编。曾任IEEE Transactions on Pattern Analysis and Machine Intelligence，Computer Vision and Image Understanding，Image and Vision Computing，the International Journal of Complex Networks等国际期刊编委会委员，BMVC1994大会主席，BMVC2016程序主席，ECCV2006，CVPR2008，CVPR2014，ICPR2004，ICPR2016领域主席。