应数学系付永强教授邀请，俄罗斯科学院通讯院士Andrey Vesnin教授和大连理工大学数学科学学院副经理雷逢春教授于10月3日至6日来公司访问并讲学，欢迎感兴趣的师生参加！

报 告 人：Andrey Vesnin 教授(俄罗斯科学院通讯院士,Sobolev Institute of Mathematics, Novosibirsk, Russia)

报告题目：On complexity of 3-manifolds

报告时间：2017年10月5日下午3:00-4:00

报告地点：格物楼503

报告摘要：The complexity theory of 3-manifolds, also known as the Matveev complexity theory,  is the base for the powerful approach to the problem of  classification of 3-manifolds. Tables of nundred of thousands of manifolds of small complexity were constructed by computer based methods. In the  talk we will give explicit values of complexity for few infinite families of hyperbolic 3-manifolds. We will discuss closed manifolds, manifolds with cusps, and manifolds with totally geodesic boundary.

报告人简介: Prof. Andrey Vesnin is head of the Laboratory of Applied Analysis, Sobolev Institute of Mathematics, Siberian Branch of Russian Academy of Sciences and a professor of Geometry and Topology, Novosibirsk State University. He received a Candidate of Sciences in physics and mathematics is 1991 from Sobolev Institute of Mathematics for the thesis ”Discrete groups of reflections and three-dimensional manifolds”, and a Doctor of Sciences in physics in mathematics in 2005 for the thesis ”Volumes and isometries of three-dimensional hyperbolic manifolds and orbifolds”. He was a visiting professor in Seoul National University in 2002 – 2004. 

    Prof. Vesnin's reseach interests include low-dimensional topology, knot theory, hyperbolic geometry, combinatorial group theory, graph theory and applications.

    In 2008 Prof. Vesnin was elected to corresponding member of the Russian Academy of Sciences. 

    Prof. Vesnin is the editor-in-chief of Siberian Electronic Mathematics Reports and a member of editorial boards of Siberian Mathematical Journal and Scientiae Mathematicae Japonicae.

报 告 人：雷逢春 教授(大连理工大学数学科学学院)

报告题目：拓扑数据分析简介

报告时间：2017年10月5日下午4:00-5:00

报告地点：格物楼503

报告摘要：在复杂的大数据内部也存在着在连续变换（或拓扑变换）下保持不变的结构性质，我们可以形象地称之为数据的形状特征（或拓扑特征）。挖掘数据的拓扑特征的方法就是拓扑数据分析（Topological Data Analysis，简称TDA），是最近十余年发展起来的新兴交叉学科。相比于主成分分析、聚类分析这些常用的数据分析方法，TDA不仅可以有效地捕捉高维数据空间的拓扑信息，而且擅长发现一些用传统方法无法发现的小分类。TDA作为一种强大的数据分析工具，已经开始被广泛的应用与大数据相关的各个领域。本报告将概要介绍拓扑数据分析的基本理论、方法和一些应用实例。