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Chenyi Hu
Univ. of Central Arkansas
Conway, Arkansas
 

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.


     

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 images...

 

Limit theorems for simulation-based optimization via random search
Chia Y., Glynn P.  ACM Transactions on Modeling and Computer Simulation 23(3): 1-18, 2013. Type: Article

It is important to know if a random search for an optimal solution converges or not. Furthermore, if it does converge, then it is important to be able to determine the rate of convergence and the distribution....

 

Cross-application data provenance and policy enforcement
Demsky B.  ACM Transactions on Information and System Security 14(1): 1-22, 2011. Type: Article

In today’s society, people increasingly rely on computer networks to exchange a variety of information. With the complexity of our networked world, ensuring information security has become critical, not only to protecting personal privacy,...

 

Efficient algorithms for large-scale local triangle counting
Becchetti L., Boldi P., Castillo C., Gionis A.  ACM Transactions on Knowledge Discovery from Data (TKDD) 4(3): 1-28, 2010. Type: Article

Becchetti et al. present efficient approximation algorithms that count the number of local triangles in large graphs (both undirected and directed). They suggest possible applications of the counting algorithms for Web spam detection and local...

 

Approximation algorithms for multi-criteria traveling salesman problems
Manthey B., Shankar Ram L.  Algorithmica 53(1): 69-88, 2009. Type: Article

Even in the single criterion case, the classic traveling salesman problem (TSP) is known to be nondeterministic polynomial time (NP) hard. For efficiency in solving TSP, one seeks good enough approximations rather than the optimum. In contrast to ...

 
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