会议时间：2021年12月13日 下午 2：30
会议地点：B8报告厅
会议主题：数据智能学术研讨
主持人：蔡毅
报告题目：
Recent progresses on Learned Indexes and Adversarial Machine Learning
报告内容简介：
In this talk, we will introduce our recent work in two areas: Learned index and Adversarial Machine Learning. In the first part of the talk, we will demonstrate the basic idea of the learned indexes, and then illustrate our work in learned indexes for high-dimensional data and graph data. In the second part of the talk, we will introduce our recent work in attacking deep image retrieval systems, in both whitebox and blackbox settings.
报告人简介：
Dr. Wei Wang is a currently a Professor in the Data Science and Analytics Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), China. Before that, he is a Professor in the School of Computer Science and Engineering, The University of New South Wales, Australia. His current research interests include Similarity Query Processing, Artificial Intelligence, Knowledge Graphs, and Security for AI Models. He has published over a hundred research papers, with most of them in premier journals (TODS, VLDB J, TKDE, TPAMI) and conferences (SIGMOD, VLDB, ICDE, WWW, IJCAI, AAAI, ACL, SIGIR, NeurIPS). He is currently an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE), and program committee members in various first-tier conferences (SIGMOD, VLDB, ICDE, SIGIR, SIGKDD, WSDM, etc.). More details can be found on his homepage at: http://www.cse.ust.hk/~weiwcs
报告题目：
Target-aware Holistic Influence Maximization Computing over Large Social Networks
报告内容简介：
Social media has become an emerging platform for organizations to broadcast their policies, for companies to advertise their products, and for people to propagate their opinions. In the social media data analytics, one of the most significant problems is the influence maximization problem. Given a social network and a campaign budget, the goal of influence maximization problem is to identify a set of influential users that are most likely to influence the maximum number of users in the social network. Meanwhile, the selected user set size is limited to the specified campaign budget. Thus, the small set of selected users can help campaign organizers to improve their marketing, branding, and product adoption in a profitable way.
At this seminar, Jianxin will first introduce some background knowledge about social influence and the traditional influence maximization problem, and then briefly overview his recent research on the problem from different perspectives – topic-aware, location-aware, community-aware and target-aware, published in TKDE’16, WWW’17, TKDE’17, ICDE’17, and ICDE’18 and PAKDD’18. After that, he will mainly present his latest research – target-aware holistic influence maximization work that was published in ICDE’18. At this part, Jianxin will explain the motivation of the novel problem, its new insights, the procedure of defining the problem in an easy way, the optimization techniques in solving the problem, and the experimental evaluations.
The main goal in presenting this seminar is to help audiences to know and understand the different applications of social influence in need, how the influence models are devised, the existing research challenges and one state-of-the-art work in this topic. This presentation is suitable to a broad audience who have interest in data science.
报告人简介：
Dr Jianxin Li is an A/Professor in the School of IT, Deakin University. His research interests include social computing, query processing and optimization, and big data analytics. He has published 70 high quality research papers in top international conferences and journals, including PVLDB, IEEE ICDE, ACM WWW, IEEE ICDM, EDBT, ACM CIKM, IEEE TKDE, and ACM WWW. His professional service can be identified by different roles in academic committees, e.g., the technical program committee members in ACM SIGMOD, PVLDB, AAAI, PAKDD, IEEE ICDM, and ACM CIKM; the journal reviewer in IEEE TKDE, ACM TKDD, WWW Journal and VLDB Journal; the proceeding chairs in DASFAA 2018, ADMA 2016 and ADC 2015; and the program committee chair in the International Workshop on Social Computing 2017 and 2018; the tutorial chair in the 26th International Conference on WWW 2017; and the guest editors in international journals, such as Computational Intelligence, IET Intelligent Transport Systems, Complexity, Data Science and Engineering.