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20210719 孟澄 Dimension Reduction Techniques in Optimal Transport Problems

发布时间:2021-07-15 15:15    浏览次数:    来源:

【学术报告】Dimension Reduction Techniques in Optimal Transport Problems


报告专家:孟澄 助理教

工作单位:中国人民大学统计与大数据研究院

报告时间:202171915:30-16:30

报告地点:数学学院203

邀请人:陈亮


报告摘要:

Recently, optimal transport methods have drawn great attention in Statistics, machine learning, and computer science due to their close relationship to deep generative neural networks. Despite its broad applications, the estimation of high-dimensional Wasserstein distances is a well-known challenging problem owing to the curse-of-dimensionality. In this talk, we will introduce some cutting-edge dimension reduction techniques that tackle high-dimensional optimal transport problems. We will also cover some recent studies, which indicate OT methods themselves, surprisingly, can be utilized to construct dimension reduction tools. Open challenges will be discussed at the end of the talk.


专家简介:

孟澄2015年毕业于清华大学数学系,2020年毕业于美国佐治亚大学统计系,师从马平教授与钟文瑄教授,获统计学博士学位。目前的主要研究方向有,大数据里的抽样和降维问题,最优传输中的计算问题和理论性质分析,光滑样条,医学影像数据以及三维点云数据处理与分析,以及传统统计和深度学习间的联系等。

详见个人主页:https://chengzijunaixiaoli.github.io/


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