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学院“互联”学术沙龙通知(第1期)——Time-varying Graph Modelling Using Sequential Monte Carlo
( 发布日期:2019-10-18 阅读:次)


主讲内容:

Most current methods used for network identification are dedicated to inference of steady state graphs which are prevalent over all time instants. However, various multivariate data evolve over time. For example, Information about the gene interactions in different stages of a life cycle is of high importance for biology in understanding of protein production, human diseases and in designing personalized treatment plans. Similarly, in seismology it is important to understand the time varying interactions between fault lines leading to seismic displacements or various variables such as gas emission and seismic displacement. In fMRI imaging, it is of importance to understand the time varying response relations between different parts of the brain.

In the statistical graphical models literature one can find a number of methods for studying steady-state network structures while the study of time varying networks is rather recent. A sequential Monte Carlo method namely particle filtering (PF) provides a powerful tool for dynamic time series analysis. In this work, the PF technique is proposed for dynamic network inference and its potentials in time varying gene expression network tracking is demonstrated.  We would like to stress that the proposed methodology is applicable in any type of time varying network including various other biological or non biological processes where variables evolve in relation to each other. 


    时间:10月22日(周二)下午13:30-15:00
    地点:信电楼204
    讲座人:Ercan E. Kuruoglu
    讲座人简介:
     男,1969年生,1998年获英国剑桥大学博士学位。现任意大利比萨国家研究委员会信息科学与技术研究所高级研究员、教授、博士生导师。现任ElsevierDigital Signal Processing》期刊主编,曾任《IEEE Transactions on Image Processing》、《IEEE Transactions on Signal Processing》期刊的副主编;及多个知名期刊的客座编辑。2019年被提名为爱因斯坦基金会访问奖学金得主,以及IEEE Fellowship。在国际期刊及知名国际会议上发表论文百余篇。获得3项美国授权专利,2项欧洲授权专利,1项日本授权专利。


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