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Meng, Xiannong
Bucknell University
Lewisburg, Pennsylvania
 
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Xiannong Meng is a professor of computer science (CS) at Bucknell University. He received his PhD in CS from Worcester Polytechnic Institute in 1990, and taught at the University of Texas–Pan American (now the University of Texas Rio Grande Valley) from 1994 to 2001. He joined Bucknell in 2001.

His research and teaching interests include information retrieval, distributed computing, intelligent web search, operating systems, computer networks, and CS education. His PhD research focused on performance measurement in computer networks with multiple classes of traffic, now known as “multimedia networks.” The work involved investigating the performance of network architectures and protocols that support multimedia, using measurement, simulation, and queueing models as tools.

Later on, in the late 1990s, Xiannong and colleagues worked on intelligent web search when they built some small-scale search engines that employ relevance feedback technologies, which allow users to search the web interactively. More specifically, the user enters a search query and the search engine returns an initial set of results based on the query. The user can mark the top results as relevant or irrelevant before sending the feedback to the search engine. The search engine, based on this feedback, refines the search and generates a new set of results. This process can continue at the user’s preference.

Xiannong is also interested in how to effectively teach the subject of information retrieval at the undergraduate level. He successfully offered the first web information retrieval course at Bucknell in early 2000. The course combined computer network components and information retrieval, and students were asked to build a search engine using a high-level programming language and term frequency–inverse document frequency as the basic search strategy. He continues to research text search that can be used in many different application areas.

Xiannong and colleagues recently investigated the general topic of undergraduate CS curricula in both the US and China. They published some initial results from the comparison in the 2019 ACM Conference on Global Computing Education.

Xiannong has been a reviewer for Computing Reviews since 2009.

 
 
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- 4 of 4 reviews

   
  Algorithms for data science
Steele B., Chandler J., Reddy S., Springer International Publishing, New York, NY, 2017. 430 pp.  Type: Book (978-3-319457-95-6)

This 430-page book contains an excellent collection of information on the subject of practical algorithms used in data science. The authors present the algorithms in the context of applications. The discussion of each algorithm starts ...

Sep 11 2017  
   Discovering computer science: interdisciplinary problems, principles, and Python programming
Havill J., Chapman & Hall/CRC, Boca Raton, FL, 2015. 750 pp.  Type: Book (978-1-482254-14-3)

Havill’s book introduces computer science in a very unique and effective way. The book discusses fundamental computer science concepts such as abstraction, repetition, condition, and recursion through real-world problems such...

Feb 3 2016  
  Information retrieval architecture and algorithms
Kowalski G., Springer-Verlag New York, Inc., New York, NY, 2010. 308 pp.  Type: Book (978-1-441977-15-1)

Kowalski’s textbook is for advanced undergraduate and first-year graduate courses on information retrieval (IR) systems....

Jun 20 2011  
  A combination approach to Web user profiling
Tang J., Yao L., Zhang D., Zhang J. ACM Transactions on Knowledge Discovery from Data 5(1): 1-44, 2010.  Type: Article

The Web contains rich sets of information about researchers in all areas. It would be extremely useful to be able to retrieve an accurate and complete profile of a researcher by simply supplying a name to a search engine. In this paper...

Apr 6 2011  
 
 
   
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