报告题目: Learning from Data Heterogeneity: Algorithms and Applications

时间：2017年6月28日 9:30-10:30

地点：理科大楼1002B

报告人：何京芮 博士

主持人: 赵静

摘要: Data heterogeneity is common across many high-impact real applications, ranging from security to manufacturing, from healthcare to traffic analytics, and it is closely related to the 'Variety' aspect of big data. Such heterogeneity can be presented in a variety of forms, including task heterogeneity, where multiple related tasks may form a hierarchical structure; view heterogeneity, where information is being collected from various sources; instance heterogeneity, where the label of a single example can be decomposed into a set of heterogeneous labels associated with composing instances; etc. In this talk, I will introduce how we model data heterogeneity from a holistic perspective. In particular, I will hinge on multiple applications, discuss the major challenges being shared by these applications that are related to data heterogeneity, and introduce our proposed techniques for addressing these challenges.

报告人简介: Jingrui He is an assistant professor in the School of Computing, Informatics and Decision Systems Engineering at Arizona State University. She received her PhD in Computer Science from Carnegie Mellon University. She joined ASU in 2014 and directs the Statistical Learning Lab (STAR Lab). Her research focuses on heterogeneous machine learning, rare category analysis, active learning and semi-supervised learning, with applications in social network analysis, healthcare, and manufacturing. She is the recipient of the 2016 NSF CAREER Award, and 2 times recipient of the IBM Faculty Award in 2015 and 2014 respectively. Dr. He is selected as IJCAI 2017 Early Career Spotlight. She has published more than 60 refereed articles, and is the author of the book on Analysis of Rare Categories (Springer-Verlag, 2011). Her papers have been selected as Bests of the Conference by ICDM 2016, ICDM 2010, and SDM 2010.