Sanguthevar Rajasekaran received his M.E. degree in Automation from the Indian Institute of Science (Bangalore) in 1983, and his Ph.D. degree in Computer Science from Harvard University in 1988. Currently he is the Board of Trustees Distinguished Professor, UTC Chair Professor of Computer Science and Engineering, and the Director of Booth Engineering Center for Advanced Technologies (BECAT) at the University of Connecticut. Before joining UConn, he has served as a faculty member in the CISE Department of the University of Florida and in the CIS Department of the University of Pennsylvania. During 2000-2002 he was the Chief Scientist for Arcot Systems. His research interests include Big Data, Bioinformatics, Algorithms, Data Mining, Randomized Computing, and HPC. He has published over 350 research articles in journals and conferences. He has co-authored two texts on algorithms and co-edited six books on algorithms and related topics. He has been awarded numerous research grants from such agencies as NSF, NIH, DARPA, and DHS (totaling more than $20M). He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the American Association for the Advancement of Science (AAAS). He is also an elected member of the Connecticut Academy of Science and Engineering.
Algorithms for Big Data Analytics
We live in an era when voluminous datasets are generated and have to be processed in every area of science and engineering. Efficient techniques are needed to process these data. In particular, we need tools to extract useful information from massive data sets. Society at large can benefit immensely from advances in this arena. For example, information extracted from biological data can result in gene identification, diagnosis for diseases, drug design, etc. Market-data information can be used for custom-designed catalogues for customers, supermarket shelving, and so on. Weather prediction and protecting the environment from pollution are possible with the analysis of atmospheric data.
In this talk we present some challenges existing in processing big data in various disciplines. We also provide an overview of some basic techniques. In particular, we will summarize various data processing and reduction techniques.