GERIS '16 March 7, 8, and 9, 2016 Binghamton University, Binghamton, NY
Abstract: Data Mining for Event Discovery
Typically before a planned cyber attack, there may be signs or traces or any other abnormal patterns occurring either in the cyber space or in the physical space or both that may or may not be directly noticeable by humans but can be clearly discovered by intelligent algorithms. This is the research of data mining for event discovery. In this talk, I will report relevant research on this topic through several case studies in different application scenarios of event discovery.
Mark Zhang is a professor at the Computer Science Department, State University of New York (SUNY) at Binghamton, USA, and the director of the Multimedia Research Laboratory in the Department. He received a PhD in Computer Science from the University of Massachusetts at Amherst, USA. He was on the faculty of Computer Science and Engineering Department, and a research scientist at the Center of Excellence for Document Analysis and Recognition, both at SUNY Buffalo, before he joined the faculty of computer science at SUNY Binghamton. He is the author of the very first monograph on multimedia data mining and the very first monograph on relational data clustering . His research has been funded by a number of US federal government agencies including NSF, AFOSR, and AFRL, industry research labs including Kodak Research and Microsoft Research, as well as a number of private foundations and funding agencies from other countries such as French CNRS, Japanese JSPS, and Chinese Ministry of Science and Technology. He holds more than twenty inventions, has served as reviewers/PC members for many conferences and journals, has held the positions as regular grant review panelists for US federal government funding agencies (NSF and NASA), New York State government funding agencies, and private funding agencies, and is currently in the editorial boards for several journals. He has also served as technical consultants for a number of industrial and governmental organizations.