Ph.D., D.Sc., IEEE Fellow
|Title:||To be ready soon.|
|Abstract:||To be ready soon.|
• Chairman of the Provosts’ Council, Department IV of Engineering Sciences, Polish Academy of Sciences:
Supervising and evaluating 13 research institutes and 21 national scientific committees in the area of engineering sciences Member, Presidium of the Polish Academy of Sciences
• Member, Central Testimonial Commission of the Republic of Poland
• Head of Laboratory of Intelligent Systems, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Memberships of Academies:
• Full member, Polish Academy of Sciences, since 2010 (Member correspondent since 2002).
• Foreign member, Spanish Royal Academy of Economic and Financial Sciences (RACEF), since 2007.
• Foreign member, Bulgarian Academy of Sciences, since 2013.
• Fellow, IEEE (Institute of Electrical and Electronics Engineers), since 2006.
• Fellow, IFSA (International Fuzzy Systems Association), since 1997.
Ph.D., IEEE Fellow
|Title:||Fuzzy Discrete Event Systems Theory and Its Application in HIV/AIDS Treatment.|
|Abstract:||To be ready soon.|
|Biography:||Professor Ying has published one single-author research monograph/advanced textbook entitled Fuzzy Control and Modeling: Analytical Foundations and Applications (IEEE Press, 2000, 342 pages; foreword by Professor Lotfi A. Zadeh), which contains solely his own research results. He has coauthored another book titled Introduction to Type-2 Fuzzy Logic Control: Theory and Applications (IEEE Press and John Wiley & Sons, Inc., 2014). In addition, he has published 110 peer-reviewed journal papers and 160 peer-reviewed conference papers. Prof. Ying's work has been widely cited - his Google Scholar h-index is 43. The 43 publications included in his index have by themselves generated more than 4,000 citations whereas the total number of citations for all his publications is almost 7,000. He holds two U.S. patents. He is serving as an Associate Editor or a Member of Editorial Board for nine international journals, including the IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Systems, Man, and Cybernetics: Systems. He serves as a member of the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society and is a member of the Fellow Evaluation Committee of the IEEE Systems, Man, and Cybernetics Society. He was elected to serve as a board member of the North American Fuzzy Information Processing Society (NAFIPS) for two terms (2005-2008 and 2008-2011). He served as Program Chair for the 2005 NAFIPS Conference and Program Co-Chair for the 2010 NAFIPS Conference as well as for the International Joint Conference of NAFIPS Conference, Industrial Fuzzy Control and Intelligent System Conference, and NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic held in 1994. He served as the Publication Chair for the 2000 IEEE International Conference on Fuzzy Systems and the Competition Chair for this annual conference in 2009 and 2011. He also served as a Program/Technical Committee Member for 90 international conferences.|
|Title:||Machine Learning in Social Network Analysis and Mining|
A network is a set of items (vertices or nodes) with connections (edges) between them. Systems taking the form of networks abound in the real world such as online social networks, information network (e.g. citation network, preference network), biological network like gene-disease network or protein interaction network, and many others. The above networks are usually called complex networks.
Networks have first been studied extensively in the social sciences, thus generating the research field of Social Network Analysis and Mining (SNAM in short). In SNAM, the properties of network such as clustering coefficient and degree distributions are analyzed. There are also mining tasks including node ranking, link prediction, community detection, network evolution and so on. The research results of SNAM are also applicable to other complex networks.
Machine learning is a powerful tool for data analysis and mining. It has been successful applied in text mining and web mining. This tutorial will give a detailed introduction on SNAM and reviews the recent work of machine learning methods used in SNAM, especially in the task of link prediction and community detection. Finally, future research topics in SNAM and their machine learning solutions will be discussed.
|Biography:||Kejia Chen is an associate professor in Nanjing University of Posts and Telecommunications. She received her PhD in Université de Technologie de Compiègne in France and her master's degree in LAMDA group, Nanjing University. She joined Jiangsu Key Laboratory of Big Data Security & Intelligent Processing in 2017. Her current research focuses on data mining and machine learning with applications in social network analysis. She once published papers in TOIS, ASONAM, ICDMW, ECML, ICTAI and got the best paper award in CCML. She also serves as a reviewer for the international journals like Information Systems and Neural Networks.|