学术讲座【The Burrows-Wheeler Transform and Applications】

浏览次数: 178 发布时间: 2016-12-08

讲座名称:The Burrows-Wheeler Transform and Applications

时间:20161212日下午14:30

地点:旗山校区软件学院507报告厅

主讲:Professor Don Adjeroh, Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia, USA.

主办:福建师范大学软件学院

专家简介:

Dr. Don Adjeroh received the Ph.D. degree in computer science from the Chinese University of Hong Kong, Hong Kong. He joined the Lane Department of Computer Science and Electrical Engineering, West Virginia University (WVU), Morgantown, WV, USA in 2000, where he is currently a Full Professor. He is the Coordinator of the graduate program in computer science, and also an Associate Chair in the Lane Department. Before joining WVU, he was a faculty member with the Department of Computer Science, University of Canterbury, Christchurch, New Zealand. His general research interests are in search data structures, computational biology, biomedical informatics, data analytics, image/video processing, and biometrics. He has published over 120 papers in these areas.

Dr. Adjeroh is a member of the IEEE, and the IEEE Computer Society. His work has been supported by grants from the US National Science Foundation, the National Aeronautics and Space Administration, Department of Homeland Security, Department of Defense/Office of Naval Research, Department of Justice/National Institute of Justice, and WV EPSCoR. He was recognized with the WVU Statler Outstanding Researcher award in 2009, and 2012.He also received the US Department of Energy CAREER Award in 2002. Dr. Adjeroh is a co-author of the BWT book: The Burrows-Wheeler Transform: Data Compression, Suffix Arrays, and Pattern Matching published by Springer in 2008.

报告摘要:

The Burrows-Wheeler Transform (BWT) performs a permutation of the symbols in a sequence such that symbols in lexically similar contexts will be near to each other. Remarkably, given the permuted string and a single integer, the original sequence can be recovered without error. This permutation is the key to the popularity of the BWT as a compression scheme, and in its widespread use in other applications, such in scalable and rapid pattern matching, for both compressed and uncompressed text. In this talk, we first describe the basic principles underlying the Burrows-Wheeler Transform. Then, we present some recent applications of the BWT in selected problems in computational biology and bioinformatics, and in computer vision.