SMP 2018 数据挖掘论坛


论坛概况

论坛时间:2018年8月4日 10:20-12:20

论坛简介:数据挖掘是计算机学科相关研究中的热点,其具体研究涵盖理论、关键技术以及计算机核心应用等各个方面。随着大数据时代的到来,人们需要日益强大的挖掘与分析能力,应用于安全和商务领域,根据用户通过电子商务、互联网、电话通信、电子邮箱所留下的更多的生活痕迹,为期提供更全面、更精准、更安全的服务;同时,数据挖掘技术亦需要被应用于气象学、天文学等领域,分析各类设备所传回的愈发难以驾驭的数据流所包含的关键信息。 本次论坛邀请了来自UIC、UCLA、清华大学、中国科学院的四位专家讲者,分享最前沿的数据挖掘研究工作。

论坛主席:浙江大学计算机学院 杨洋 助理教授

主席简介: 杨洋,博士,现任浙江大学计算机科学与技术学院助理教授,2016年获清华大学计算机科学与技术专业博士学位,获中国电子学会优秀博士毕业论文、清华大学优秀博士毕业论文、北京市优秀博士毕业生等荣誉,博士期间访问美国康奈尔大学John Hopcroft教授、比利时鲁汶大学Sien Moens教授。主要研究方向为社交网络挖掘、网络表示学习、异常检测、计算社会学等,在KDD、WWW、AAAI、TOIS、TKDD等国际顶级学术会议及期刊上发表论文20余篇,曾担任KDD’18、WWW'17、WSDM'17‘16、ICWSM’17、CIKM'17’16、WSDM’16、ASONAM’15程序委员会委员。


论坛嘉宾

University of Illinois at Chicago  Philip S. Yu  Distinguished Professor


报告主题:Broad Learning via Fusion of Heterogeneous Information
报告摘要:In the era of big data, there are abundant of data available across many different data sources in various formats. “Broad Learning” is a new type of learning task, which focuses on fusing multiple large-scale information sources of diverse varieties together and carrying out synergistic data mining tasks across these fused sources in one unified analytic. Great challenges exist on “Broad Learning” for the effective fusion of relevant knowledge across different data sources, which depend upon not only the relatedness of these data sources, but also the target application problem. In this talk we examine how to fuse heterogeneous information to improve mining effectiveness over various applications.
嘉宾简介:Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,100 referred conference and journal papers cited more than 98,000 times with an H-index of 142. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).


University of California, Los Angeles  Wei Wang  the Leonard Kleinrock Chair Professor


报告主题:Analyzing and understanding human online behaviors
报告摘要:Online social media platforms and online services are prevalent nowadays, which generate rich data for analyzing and understanding human behaviors. However accurate modeling of human behaviors facing a number of challenges. For example, some users may carry out multiple conversations simultaneously on a chat platform which therefore results in interleaved conversations in the log, making it difficult to distill the topics of these conversations. Customers subscribed to online services may choose to share an account to save individual subscription costs, posing challenges to customer profiling and online recommendation. In this talk, I will present our recent research on representation learning that can help to disentangle the online conversations and online service requests.
嘉宾简介:Wei Wang is the Leonard Kleinrock Chair Professor of Computer Science at University of California, Los Angeles and the founding director of the Scalable Analytics Institute (ScAi). She is a co-director of the NIH BD2K Centers-Coordination Center. She received her PhD degree in Computer Science from the University of California, Los Angeles in 1999. She was a professor in Computer Science at the University of North Carolina at Chapel Hill from 2002 to 2012, and was a research staff member at the IBM T. J. Watson Research Center between 1999 and 2002. Dr. Wang's research interests include big data analytics, data mining, bioinformatics and computational biology, and databases. She has filed seven patents, and has published one monograph and more than one hundred seventy research papers in international journals and major peer-reviewed conference proceedings and multiple best paper awards. Dr. Wang received the IBM Invention Achievement Awards in 2000 and 2001. She was the recipient of an NSF Faculty Early Career Development (CAREER) Award in 2005. She was named a Microsoft Research New Faculty Fellow in 2005. She was honored with the 2007 Phillip and Ruth Hettleman Prize for Artistic and Scholarly Achievement at UNC. She was recognized with an IEEE ICDM Outstanding Service Award in 2012, an Okawa Foundation Research Award in 2013, and an ACM SIGKDD Service Award in 2016. Dr. Wang has been an associate editor of the IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Big Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, ACM Transactions on Knowledge Discovery in Data, Journal of Computational Biology, Journal of Knowledge and Information Systems, Data Mining and Knowledge Discovery, and International Journal of Knowledge Discovery in Bioinformatics. She serves on the organization and program committees of international conferences including ACM SIGMOD, ACM SIGKDD, ACM BCB, VLDB, ICDE, EDBT, ACM CIKM, IEEE ICDM, SIAM DM, SSDBM, RECOMB, BIBM. She was elected to the Board of Directors of the ACM Special Interest Group on Bioinformatics, Computational Biology, and Biomedical Informatics (SIGBio) in 2015.


清华大学  崔鹏  副教授


报告主题:Dynamic Network Embedding
报告摘要:Network embedding, aiming to embed a network into a low dimensional vector space while preserving the inherent structural properties of the network, has attracted considerable attention. However, most existing embedding methods focus on the static network while neglecting the evolving characteristic of real-world networks. In this talk, I will introduce some of our recent works on dynamic network embedding, including the DepthLGP for inferring embeddings for out-of-sample nodes, DHPE for preserving high-order proximities in dynamic network embedding, TIMERS for avoiding aggregated error in incrementally updating embeddings, as well as D-SGD for scalable optimization on highly dynamic and recency-sensitive data.
嘉宾简介:Peng Cui is an Associate Professor with tenure in Tsinghua University. He got his PhD degree from Tsinghua University in 2010. His research interests include network representation learning, human behavioral modeling, and social-sensed multimedia computing. He has published more than 60 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the SIGKDD 2016 Best Paper Finalist, ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is the Area Chair of ICDM 2016, ACM MM 2014-2015, IEEE ICME 2014-2015, ICASSP 2013, Associate Editors of IEEE TKDE, IEEE TBD and ACM TOMM, etc. He was the recipient of ACM China Rising Star Award in 2015.


中国科学院计算技术研究所  沈华伟  研究员


报告主题:Graph Convolutional Neural Network: An Overview
报告摘要:卷积神经网络在处理图像、自然语言等具有较好空间结构的数据时已经展现出了很好的优势。然而,卷积神经网络不能直接应用于图(Graph)这类空间结构不规则的数据上。近年来,研究人员开始研究如何将卷积神经网络迁移到图数据上,涌现出ChevNet、MoNet、GCN、GAT等一系列方法,在基于图的半监督分类和图表示学习等任务中表现出很好的性能。报告将梳理和回顾该方向的主要研究进展,并结合社会媒体计算探讨未来可能的发展方向。
嘉宾简介:沈华伟,博士,中国科学院计算技术研究所研究员,中国中文信息学会社会媒体处理专委会副主任。研究方向为网络数据挖掘和社交媒体计算。先后获得过CCF优博、中科院优博、首届UCAS-Springer优博、中科院院长特别奖、入选首届中科院青年创新促进会、中科院计算所“学术百星”。2013年在美国东北大学进行学术访问。2015年被评为中国科学院优秀青年促进会会员(中科院优青)。获得国家科技进步二等奖、北京市科学技术二等奖、中国电子学会科学技术一等奖、中国中文信息学会钱伟长中文信息处理科学技术一等奖。在Science、PNAS等期刊和WWW、SIGIR、CIKM、WSDM、AAAI、IJCAI等会议上发表论文80余篇。担任PNAS、IEEE TKDE、ACM TKDD等10余个学术期刊审稿人和KDD、WWW、SIGIR、AAAI、IJCAI等20余个学术会议的程序委员会委员。