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威廉希尔亚洲公司员工王铠博士学术报告

发布时间:2015-06-04 编辑: 来源:威廉希尔

 

报告人Kai Wang(王铠)

时间201564日(周四)下午1500—1700

地点:新教师办公楼(医务室旁边)三楼会议室

主持人:朱大铭 教授

 

报告题目Explorations in Cloud Computing for Large Bio Dataset Processing and Sequence Analysis

 

Bio:  Dr. Wang is interested in the area of high performance computing/cloud computing and big data. He has published several papers on world-wide famous journals and conferences, such as Bioinformatics, Journal of Applied Mathematics and Computing, International Conference on Scientific Computing, Computational and Mathematical Methods in Medicine etc. Moreover, he has achieved a large amount of foundation supports from China and USA since 2006. Now he holds a China National Science Foundation which has $457,000.

 

Kai Wang got his B.S. on Computational Mathematics from Shandong University in September 1994. Later he received his M.S. on Computer Science from Shandong University in September 1997. He achieved his PhD on Computer Science from University of Kentucky in December 2003.

 

Dr. Wang has rich work experiences which are as follows.

Director: Complete Genomics, BGI Research. Lead the software and algorithm group in research and development of the big data issue in gene data computation.

Senior Architect/Staff Scientist: Lawrence Berkeley National Lab. October  2011– January 2015. Lead the cloud computing efforts, design high performance computing algorithms for big data processing.

Staff Scientist: High Performance Computing Office, National Institute of Health, Bethesda, MD. June 2010 -­‐ September 2011. Design high performance computing algorithms for sequence data processing and analyzing.

Assistant Professor: Computer Science Department, University of South Dakota. January 2006 – May 2010. Teach and advise undergraduate, graduate, and PhD students. Research and development in high performance computing architectures, algorithms, and applications.

Scientific Programmer: Parallel Programming Lab, University of Illinois at Urbana-­‐Champaign. 2003 – 2005. Parallel programming model. Simulation on high performance large systems.

 

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