报告时间：6月9日上午10:30-12:00
报告地点：红足1世77814红足1世77814101会议室
报告题目：Bridging Topological Data Analysis and Machine Learning
报告摘要：Topological data analysis (TDA) extracts the topological features that are complementary to statistical quantities, which has been found in many applications in computer vision. In TDA, Persistence diagram (PD) has been considered as a compact descriptor for topological data analysis (TDA). Unfortunately, PD cannot be directly used in machine learning methods since it is a multiset of points. Recent efforts have been devoted to transforming PDs into vectors to accommodate machine learning methods. However, existing methods share one common shortcoming: the mapping of PDs to a feature representation depends on a pre-defined polynomial. This presentation will introduce two recent advances: polynomial representation and Hilbert representation for PD, with the aim of extracting the discriminative topological features. Finally, potential applications in the field of computer vision, and biomedical science will be discussed.
报告人： Gang Li，澳大利亚迪肯大学信息技术学院教授（相当于北美首席教授、讲座教授），目前担任网络安全研究中心（CREST）人工智能主任。历任澳大利亚迪肯大学理工学科博士训练主管、Data to Intelligence数据智能研究中心主任、信息技术学院学术训练主管（Academic Director, Research Training）。主要从事隐私保护、数据发掘、商业智能、机器学习等方面的研究。已在包括CVPR, IJCAI, KDD、ICDM、SDM、VLDB, MLJ、TIFS、TKDE等在内的国际重要会议和刊物上发表论文近 200篇，八次作为指导教师荣获最佳论文奖，包括2018年KSEM最佳论文、2017年度IFITT年度最佳期刊论文、2016年IEEE Trustcom最佳学生论文、2015年IFITT年度最佳期刊论文、2014 PAKDD最佳学生论文、2012 ACM/IEEE ASONAM最佳论文、Springer的2007 Nightingale奖等。其研究受到澳洲ARC-LP、印度Spark，香港GRF项目资助。
