Big Data Research: Methods, Systems, and Applications

The Chinese University of Hong Kong
Department of Computer Science and Engineering

Seminar

ABSTRACT:

We are in the era of "Big Data", which is loosely defined as the application of data driven approaches to solve problems arising in a wide-range of domains in science, engineering, government, and business. Big Data holds the promise of allowing us to tackle problems at a scale, complexity, and fidelity that was previously impossible, enables us to achieve a deep understanding about the world around us, and revolutionize every aspect of our daily life.

In this talk, I present an overview of some recent "Big Data" work in my laboratory that spans various aspects of "Big Data" related research including development of new data analysis algorithms, runtime systems for efficient processing of large datasets, and applications of data analysis methods to emerging "Big Data" areas. On the algorithms side, the talk will focus on methods for building predictive model for collaborative-filtering based recommender systems, on methods for analyzing dynamic relational networks towards finding patterns of relational co-evolution, and on methods for analyzing multivariate time series in order to understand how users' behaviors changes over time. On the systems side, the talk will focus on our work for developing runtime systems to allow the automated out-of-core execution of distributed memory message-passing programs that provides a framework for processing very large dataset on moderate size clusters and still achieve high-levels of computational performance. Finally, on the application side, the talk will present our work on employing "Big Data" approaches to analyze datasets obtained from spontaneous adverse drug event reporting systems and course/learning management systems.

BIOGRAPHY:

George Karypis is a professor at the Department of Computer Science & Engineering at the University of Minnesota, Twin Cities. His research interests spans the areas of data mining, high performance computing, information retrieval, collaborative filtering, bioinformatics, cheminformatics ,and scientific computing. His research has resulted in the development of software libraries for serial and parallel graph partitioning (METIS and ParMETIS), hypergraph partitioning (hMETIS), for parallel Cholesky factorization (PSPASES), for collaborative filtering-based recommendation algorithms (SUGGEST), clustering high dimensional datasets (CLUTO), finding frequent patterns in diverse datasets (PAFI), and for protein secondary structure prediction (YASSPP). He has coauthored over 250 papers on these topics and two books ("Introduction to Protein Structure Prediction: Methods and Algorithms" (Wiley, 2010) and "Introduction to Parallel Computing" (Publ. Addison Wesley, 2003, 2nd edition)). In addition, he is serving on the program committees of many conferences and workshops on these topics, and on the editorial boards of the IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery, Social Network Analysis and Data Mining Journal, International Journal of Data Mining and Bioinformatics, the journal on Current Proteomics, Advances in Bioinformatics, and Biomedicine and Biotechnology.

Enquiries: Miss Evelyn Lee at tel 3943 8444

For more information, please refer to http://www.cse.cuhk.edu.hk/seminar.

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