Dr. Li ChenBased in Washington, DC, United States
Dr. Li Chen is currently a professor of computer science at the University of the District of Columbia. He received his BS, MS, and PhD in CS from Wuhan University (1982), Utah State University (1995), and the University of Bedfordshire (2001), respectively. Chen has worked in both academia and industry. He was a lecturer at South East University and Wuhan University in China before serving as a visiting assistant professor at the University of North Dakota. He was also a visiting associate professor at the University of Maryland, and adjunct professor at Virginia Tech. In industry, he worked for companies as a senior software engineer.
Chen has been an active member of ACM since 2008. He has been the Education Column coordinator of ACM SIGACT Newsletters for several years.
Chen has published more than 68 researcher papers in journals and conference proceedings including Discrete Mathematics; Theoretical Computer Science; Topology and its Applications; IEEE Systems, Man, and Cybernetics; Information Science; the Chinese Science Bulletin; and the Chinese Journal of Computers. He also holds a United States patent.
In addition to his research, Chen has published a total of five books including the recently published “Mathematical problems in data science" (Springer, 2016), “Digital and discrete geometry” (Springer, 2014), “Digital functions and data reconstruction" (Springer, 2012). Chen also writes articles in CS and applied math education.
Chen has given professional talks on various topics in many universities and colleges including the University of Toronto, University of Maryland, George Mason University, Rutgers University, NIH, and Georgetown University. He was a visitor of DIMACS (Rutgers-Princeton) and a Scientific Researcher in the Fields Institute at the University of Toronto.
Chen has received several awards including the SEAS Outstanding Research Award (UDC, 2015), Outstanding Teacher at Wuhan University (1990), and the Award Research Fund of Chinese Academy of Science for Young Scientists (1987).
Chen's research interests are broad in computer science and applied mathematics and include applied algorithm design, digital and discrete geometry, image processing, and applications to data science. He has made contributions to several research areas of computer science and its applications including: (1) the construction of gradually varied surfaces, (2) lambda-connected image segmentation methods, (3) the digital form of the Gauss-Bonnet theorem, (4) the polynomial time algorithm for finite Abelian group decomposition, (5) the definition of digital manifolds and classification of 3D digital surfaces, and (6) the optimum algorithm for the check matrix of the optimal SEC-DED code (optimal Hamming code).
In 2014, Chen chaired the Satellite Conference on Data Science of International Congress of Mathematicians (ICM14). He is recently editing a handbook of data science and working on a Block-Chain project.
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Data Science: Recent Developments and Future Trends
Data contains science. How data is handled today is much different than the classical mathematical approach of using models to fit the data. Today, people are supposed to find rules and...
- Digital Geometry and its Applications: Geometrical and Topological Data ProcessingDigital geometry focuses on digital objects, which are usually represented by a finite number of integer points or vectors. However, in a much larger sense, digital objects could be digital...
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- Digital Geometry and its Applications: Geometrical and Topological Data Processing