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卡内基梅隆大学Kun Zhang博士应邀到我院作学术报告

作者:DMIR    来自:    发表时间:2016-01-05    浏览量:1659


2016年1月5日上午,卡内基梅隆大学Kun Zhang博士应蔡瑞初教授邀请到访我院,并为我院师生作了题为“Machine Learning and Causal Modeling: How They Benefit Each Other?”的学术报告,与我院师生们就报告的相关研究内容展开了热烈的学术探讨。本次报告会不仅得到我院师生的踊跃参与,同时吸引了其他校内外师生的参加。

张博士以日常生活中存在因果关系的例子作为引入,分别讲解了基于约束和基于评分的因果发现方法,并阐述了其优点和缺陷,接着还以典型应用为案例,形象地说明因果发现方法在实际数据中的运用。最后,他从因果关系的角度阐释了半监督学习和领域自适应这两类机器学习方法,以及因果信息如何帮助解决这些机器学习的问题,为到场的师生在机器学习领域的研究提供了新的角度和观点。  

张博士作学术报告

  

报告会现场听众在认真听讲

报告会后,张博士分别与DMIR实验室的老师以及同学们合影留念。

  

张博士与DMIR实验室参会老师合影

  

张博士与DMIR实验室学生合影

张博士简介:

Kun Zhang is an assistant professor in the philosophy department at Carnegie Mellon University (CMU). Before joining CMU, he was a senior research scientist at Max-Planck Institute for Intelligent Systems, Germany, and a lead scientist at Information Sciences Institute, University of Southern California. He obtained his Ph.D from Chinese University of Hong Kong and then worked at University of Helsinki as a postdoctoral fellow. His main research interests include causal discovery, machine learning, and large-scale data analysis, and has organized various academic activities to foster interdisciplinary research in causality.