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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.


     

A new block matching algorithm based on stochastic fractal search
Betka A., Terki N., Toumi A., Hamiane M., Ourchani A.  Applied Intelligence 49(3): 1146-1160, 2019. Type: Article

Block matching is an important technique for applications involving motion estimation, such as in video surveillance, TV broadcasting, video games, and so on. To improve the efficiency and effectiveness of block matching algorithms, the authors of...

 

randUTV: a blocked randomized algorithm for computing a rank-revealing UTV factorization
Martinsson P., Quintana-Ortí G., Heavner N.  ACM Transactions on Mathematical Software 45(1): 1-26, 2019. Type: Article

Matrix singular value decomposition (SVD) has broad applications in both computational linear algebra and data analysis. Robust SVD algorithms and sophisticated software implementations have been available in the literature for decades. However, e...

 

Rehumanized crowdsourcing: a labeling framework addressing bias and ethics in machine learning
Barbosa N., Chen M.  CHI 2019 (Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK,  May 4-9, 2019) 1-12, 2019. Type: Proceedings

Crowdsourcing is the practice of obtaining information or input into a task from a large number of people, either paid or unpaid, typically via the Internet. With its fast growth, crowdsourcing has produced large volumes of data manually labeled v...

 

Data science data governance
Kroll J.  IEEE Security and Privacy 16(6): 61-70, 2018. Type: Article

Recent advances in machine learning, data analytics, and artificial intelligence (AI) have empowered human beings to automatically make decisions by processing vast amounts of data much more efficiently than ever before. Along with great advantage...

 

The secret formula for choosing the right next role
Matsudaira K.  Communications of the ACM 61(10): 44-46, 2018. Type: Article

Like it or not, tech professionals still very much enjoy great career opportunities. Searching and changing positions, with various motivations, is just a part of life for some tech professionals. Reported in a recent survey, the tech sector has t...

 
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