- Brief Biography
Understanding Climate Change: Opportunities and Challenges for Data Driven Research
Climate change is the defining environmental challenge facing our planet, yet there is considerable uncertainty regarding the social and environmental impact due to the limited capabilities of existing physics-based models of the Earth system. This talk will present an overview of research being done in a large interdisciplinary project on the development of novel data driven approaches that take advantage of the wealth of climate and ecosystem data now available from satellite and ground-based sensors, the observational record for atmospheric, oceanic, and terrestrial processes, and physics-based climate model simulations. These information-rich datasets offer huge potential for monitoring, understanding, and predicting the behavior of the Earth's ecosystem and for advancing the science of climate change.This talk will discuss some of the challenges in analyzing such data sets and our early research results.
Vipin Kumar is currently William Norris Professor and Head of Computer Science and Engineering at the University of Minnesota. His research interests include High Performance computing and data mining, and he is currently leading an NSF Expedition project on understanding climate change using data driven approaches. He has authored over 300 research articles, and co-edited or coauthored 10 books including the widely used text book ``Introduction to Parallel Computing", and "Introduction to Data Mining" both published by Addison-Wesley. Kumar co-founded SIAM International Conference on Data Mining and served as a founding co-editor-in-chief of Journal of Statistical Analysis and Data Mining (an official journal of the American Statistical Association). Kumar is a Fellow of the AAAS, ACM and IEEE. He received the Distinguished Alumnus Award from the Indian Institute of Technology (IIT) Roorkee (2013), the Distinguished Alumnus Award from the Computer Science Department, University of Maryland College Park (2009), and IEEE Computer Society's Technical Achievement Award (2005). Kumar's foundational research in data mining and its applications to scientific data was honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD).
- Research Center of Information Technology Innovation (CITI), Academia Sinica
Professor Ming-Syan Chen
- Brief Biography
On Information Extraction for Social Networks
Recently due to the fast increasing activities of social networks, it has become very desirable to conduct various analyses for applications on social networks. However, as the scale of a social network has become prohibitively large, it is infeasible to scrutinize the entire social network. As a result, a significant amount of research effort has been elaborated upon extracting the essential application-dependent information from a social network. In this talk, we shall examine some recent studies on information extraction for social networks with an attempt to classify them in view of their purposes. Explicitly, we shall explore the methods for three levels of information extraction in a social network, namely, parameter extraction, information extraction, and structure extraction, and use one example work to describe each of them.
Ming-Syan Chen received the M.S. and Ph.D. degrees in Computer, Information and Control Engineering from The University of Michigan, Ann Arbor, MI, USA. He is now a Distinguished Research Fellow and the Director of Research Center of Information Technology Innovation (CITI) in the Academia Sinica, Taiwan, and is also a Distinguished Professor jointly appointed by EE Department at National Taiwan University. He was a research staff member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, the Director of Graduate Inst. of Comm. Eng. at NTU, and also the President/CEO of Institute for Information Industry (III), which is one of the largest organizations for information technology in Taiwan. His research interests include databases, data mining, social networks, and mobile data management, and he has published more than 300 papers in his research areas. Dr. Chen holds, or has applied for, eighteen U.S. patents and seven ROC patents in his research areas. He is a recipient of the Academic Award of the Ministry of Education, the NSC (National Science Council) Distinguished Research Award, Pan Wen Yuan Distinguished Research Award, Teco Award, Honorary Medal of Information, and K.-T. Li Research Breakthrough Award for his research work, and also the Outstanding Innovation Award from IBM Corporate for his contribution to a major database product. Dr. Chen is a Fellow of ACM and a Fellow of IEEE.
- Brief Biography
Being a Happy Dwarf in the Age of Big Data
Big data has posted grand challenges, and at the same time, has presented grand opportunities for data management and analytics research, development, and applications. In this talk, I will examine some of the exciting and novel research challenges on big data analytics, such as context-aware on-demand data mining, egocentric analytics, and automatic information organization and integration, and how they are different from many traditional data management issues. This talk commits itself to an open-minded dialog. Instead of leading to any deterministic conclusions, the goal is to incite ourselves to rebellion against simple scaling up or out of traditional data management methods.
Jian Pei is a professor at the School of Computing Science at Simon Fraser University, Canada. He received a Ph.D. degree in Computing Science from the same school in 2002, under Dr. Jiawei Han’s supervision. His research interests can be summarized as developing effective and efficient data analysis techniques for novel data intensive applications. Particularly, he is currently interested in various techniques of data mining, information retrieval, data warehousing, online analytical processing, and database systems, as well as their applications in social networks, network security informatics, healthcare informatics, business intelligence, and web search. His research outcome has been adopted by industry production systems. He has published prolifically in premier academic venues. His publications have been cited more than 30,000 times. His research has been supported in part by many government agencies and many industry partners. Currently, his priority in research is on developing industry relations and collaboration, and transferring his technologies to industry applications. He is also actively serving the professional communities. He is current the editor-in-chief of IEEE Transactions of Knowledge and Data Engineering, and an associate editor or editorial board member of several premier journals in his areas. He has played key roles in many top academic conferences. He is a director of ACM SIGKDD and an ACM Distinguished Speaker. He received several prestigious awards. He is a fellow of IEEE and a senior member of ACM.