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2013年5月2日，美国韦恩州立大学陈学文教授到访模式识别与机器智能实验室，向自动化学院的师生做了题为“Graphical Modeling: Representation and Learning”的学术报告。

Title: Graphical Modeling: Representation and Learning

Abstract: Graphical models are probabilistic models with a graph representing the conditional dependence between random variables. Examples include Bayesian networks, Markov networks, hidden Markov models etc. They have been widely applied to different areas, such as computer vision, computational systems biology, and social networks. In this talk, I will briefly introduce our recent work in graphical modeling, including a Gaussian graphical model learning method and a new graphical representation for latent topic-semantic indexing. I will also discuss some big data challenges in graphical modeling.

陈学文教授是机器学习与模式识别领域的国际著名专家，在大规模数据挖掘、人工智能、机器学习等方面取得创新性的贡献，其研究成果具有重要的应用价值。陈学文教授目前是美国韦恩州立大学计算机科学系的正教授、系主任。另外还担任International Journal of Data Mining and Bioinformatics杂志主编IEEE Computer Society Technical Committee on Computational Life Sciences (TCCLS)主席和多本杂志和书籍的评撰稿人。其最新研究成果发表在BMC Systems Biology、International Journal of Data Mining an Bioinformatics、Bioinformatics等数据挖掘和模式识别领域内的顶尖学术期刊上，累计发表学术论文103篇。相关理论和技术研究共获5项美国国家科学基金项目、3项美国NIH基金项目、2项美国国防部项目、1项美国国家航空航天局基金等多项基金支持，并获美国发明专利1项。

陈学文教授个人主页：http://engineering.wayne.edu/profile/xuewen.chen/