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Hu, Chenyi
Univ. of Central Arkansas
Conway, Arkansas
 
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Chenyi Hu is a professor of computer science at the University of Central Arkansas, where he also served as the department chairperson for 11 years (2002 to 2013). From 1990 to 2002, he served as a faculty member in the computer and mathematical sciences at the University of Houston-Downtown; in 1999, he received the university’s Faculty Scholarly/Creativity Award.

Chenyi’s main research interest is in scientific computing and applications, especially with interval methods. He has published about 100 related articles and book chapters. He is an editor and main contributor of the book Knowledge Processing with Interval and Soft Computing, published by Springer in 2008. His research work has been supported by the US National Science Foundation through grant awards. He has taught various courses, ranging from programming introduction to advanced algorithms. He has also been actively involved in various professional services. For example, as a program evaluator for the ABET Computing Accreditation Commission, he visited and assessed multiple computer science undergraduate degree programs at universities in the US for their ABET accreditation.

Chenyi received his PhD from the Department of Mathematics at the University of Louisiana, Lafayette in 1990; an MS degree in mathematics from Southern Illinois University, Edwardsville in 1987; and an undergraduate diploma in applied mathematics from Anhui University, China in 1976.

He has been a reviewer for Computing Reviews since 2003.

 
 
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   Engineering resilient collective adaptive systems by self-stabilisation
Viroli M., Audrito G., Beal J., Damiani F., Pianini D. ACM Transactions on Modeling and Computer Simulation 28(2): 1-28, 2018.  Type: Article

Smart cities, together with the Internet of Things (IoT), are becoming reality at an accelerated speed, supported by the fifth generation of mobile technology (5G) and other advances in technology. The supporting networked computationa...

Jul 8 2020  
   Fast and scalable approximate spectral matching for higher order graph matching
Park S., Park S., Hebert M. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(3): 479-492, 2014.  Type: Article

Matching visual objects computationally has many practical applications. Observational instruments placed in various positions related to the same physical object usually produce very different images; thus, accurately matching these i...

Apr 14 2014  
 
 
   
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