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Jinguri He: "Taming Data Heterogeneity: Towards a Unified Framework"

Event Type
Department of Computer Science
2405 Thomas M. Siebel Center for Computer Science
Apr 18, 2018   10:00 am  
Dr. Jingrui He, Assistant Professor, Arizona State University
Lisa Yanello
Originating Calendar
Computer Science Speakers Calendar

Abstract: Data heterogeneity is common across many high-impact application domains, ranging from national security to air traffic modeling, manufacturing, and healthcare. It is at the core of the 'Variety' aspect of big data. Such heterogeneity can be present in a variety of forms, including (1) task heterogeneity, where multiple related learning tasks can be jointly learned to improve the overall performance; (2) view heterogeneity, where complementary information is available from various sources; (3) oracle heterogeneity, where multiple oracles may have different opinions regarding the true label of an example; and (4) instance heterogeneity, where a single example can be decomposed into a set of instances with heterogeneous labels.

In this talk, I will present our recent work towards building a unified framework for learning from such data heterogeneity, focusing on the co-existence of multiple types of data heterogeneity such as task and oracle dual heterogeneity. The goal is to enable the learning model to enjoy the best of all possible worlds. In particular, I will hinge on the application of accident phase classification in air traffic modeling, discuss the problem-specific data heterogeneity, and our proposed tensor-based model for learning from such data heterogeneity, together with empirical results on various data sets. Finally, I will conclude the talk by sharing my vision for heterogeneous learning in the future.

Bio: Dr. Jingrui He is an assistant professor in School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. She received her Ph.D in Computer Science from Carnegie Mellon University in 2010, and joined ASU in 2014. Her research focuses on heterogeneous machine learning, rare category analysis, active learning and semi-supervised learning, with applications in social network analysis, healthcare, financial fraud detection, and manufacturing processes. Dr. He is the recipient of the 2016 NSF CAREER Award, two times recipient of the IBM Faculty Award in 2015 and 2014 respectively, and was selected as IJCAI 2017 Early Career Spotlight. She has published more than 70 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. Dr. He has served on the senior or general program committees of many international conferences, including KDD, IJCAI, AAAI, SDM, and ICML. 

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