The Good, the Bad and the Ugly: Uncovering Novel Opportunities of Data Science
Seminar title: The Good, the Bad and the Ugly: Uncovering Novel Opportunities of Data Science
Speaker: Professor Huan Liu, Data Mining and Machine Learning Lab, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University
Seminar room: CB11.12.113
Date and time: 13:30 – 14:30, Tuesday 19 April, 2016
Seminar chairman: Dr Guandong Xu
Big data is ubiquitous and becomes bigger, and challenges traditional data mining and machine learning methods. Social media is a new source of data that is significantly different from conventional ones. Social media data is mostly user generated, and is big, linked, and heterogeneous. We present the good, the bad and the ugly associated with the multi-faceted social media data, exemplify the importance of data reduction and inferring invisible information with real-world examples, and illuminate new opportunities of developing novel algorithms and tools for data mining and machine learning. In our endeavor of taming the bad and the ugly with the help of the good, we deepen our understanding of ever growing and evolving data and generate innovative solutions with interdisciplinary, collaborative research.
Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California. He was recognized for excellence in teaching and research in Computer Science and Engineering at Arizona State University. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating problems that arise in real-world applications with high-dimensional data of disparate forms. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He serves on journal editorial/advisory boards and numerous conference program committees. He is a Fellow of IEEE and a member of several professional societies.