Chen Guoliang, born in 1938, a native of Yingshang County, Anhui Province, currently works as a professor and doctoral supervisor at the University of Science and Technology of China (USTC). Prof. Chen also serves as Dean of the School of Software Engineering of USTC, and Director of the National High Performance Computing Center at Hefei. Chen graduated from the Department of Radio and Electronics, Xi'an Jiaotong University in 1961. He joined the faculty of USTC in 1973. From 1981 to 1983, Prof. Chen undertook research and study at the Purdue University in the United States as a visiting scholar. In 2003, Prof. Chen was elected academician of the Chinese Academy of Sciences.
Over the past two decades, Prof. Chen Guoliang has carried out systematic investigations into the theory, design and application of parallel algorithms and put forward a series of new ideas and new methods in this regard. As a result of his pioneering efforts, the integrated research system of “parallel algorithms - parallel computer - parallel programming” has been established. Prof. Chen has also made significant achievements and contributions in the field of non-numerical parallel algorithms, high performance computing and its application.
In the early 1980s, Prof. Chen was the first in China to carry out research on non-numerical parallel algorithms. Prof. Chen’s research achievements were in the vanguard position among his peers during the same period. His representative achievements lie in research on packet switching network, routing algorithms for Benes network, VLSI planar embedding algorithm and the network maximum flow algorithm. He had made improvements to the research of some Turing Awardees, including Yao’s bounds on selection networks and parallel algorithms for string matching, and revised Karp’s random string matching algorithm into determinate string matching algorithm. He was the first in the world to have carried our analyses of the average performance of uniform distribution on (0, d] and packing algorithm as well as of the algorithm for two-dimensional packing. Prof. Chen’s research results have been spoken highly of by his peers both at home and abroad.
In the early 1990s, Prof. Chen carried out early researches in China on the optimization of neural network and genetic algorithm, which has promoted cross-disciplinary research and development. He also developed the world-class parallel neural information processing systems, which has provided sound tools and environment for the teaching, research and application of neural networks in China.
In the mid-1990s, Prof. Chen was engaged in the research on high performance computing and its application, and was the first to set up China’s first National High Performance Computing Center, which has promoted China’s development in this field. He also developed the copyrighted commercial software of “user development environment” for the Dawn Parallel Processing System, yielding meritorious contributions to the promotion of parallel processing systems made in China. He also developed the Dawn 1000-based Decision-making System for Disaster Prevention and Reduction in Anhui Province, which has played an important role in disaster forecast and optimal operation of reservoirs in the Huaihe River Basin in Anhui Province, resulting in significant social and economic benefits.
Prof. Chen has 15 national-level research projects, of which 11 have been completed. Besides, he has authored or co-authored more than 170 research papers and eight monographs, with the total number of citations exceeding over 540. Prof. Chen has won numerous awards or prizes at state, provincial and ministerial levels, including the second prize of the National Science and Technology Progress Award of China, the second prize of the National Teaching Achievement Award, and 12 science and technology progress awards at provincial or ministerial levels. Up to now, Prof. Chen has graduated a plethora of competent talents engaged in research on parallel algorithm including 26 doctorates. Prof. Chen is well-known for his rigorous style of learning and is venerated as a paragon of virtue for others.