Dr. Yasuo Matsuyama graduated from the Department of Electrical Engineering in the School of Science and Engineering at Waseda University in 1969 and studied for a doctorate in Electrical Engineering at the same university, receiving a doctorate in 1974 for his research on stochastic pulse density modulation in the nervous system. The same year, he was selected for the Japan-U.S. Personnel Exchange Program, a joint program of the Japan Society for the Promotion of Science, the U.S. Institute of International Education, and the Fulbright Program. He was sent to Stanford University to study at the Graduate School, where he received his Ph. D. (EE) degree in 1978 for his research on distortion measure theory of stochastic processes and its application to signal processing.

He served as an assistant professor at Stanford University's Information Systems Laboratory, a professor at Ibaraki University, and the director of the doctoral program, and in 1996, he became a professor at Waseda University's School of Science and Engineering (after reorganization, professor at the Faculty of Science and Engineering), and since 2017 he has been a professor emeritus at the university, where he remains to the present. During this period, he has held several important positions outside the university, including a Visiting Scholar at the University of Alabama, a Member of the National Personnel Authority's Comprehensive Examination for Senior Executives, and within the university, including Director of the Media Network Center.

He has made pioneering achievements by "constructing a machine learning theory for structuring incomplete data (real data) and applying it to data science." After establishing this technology, including the grant of a basic patent in Japan for vector quantization, he developed the design method into generalized competitive learning, which enables multi-objective and multi-optimization. Based on this, he has enabled the application of this technique to various data processing applications, including audio, still and video image generation and similarity search, and large-scale traveling salesman problems. These results are based on the work he independently completed in the two doctoral programs mentioned above: the analysis of stochastic processes in the nervous system and vector quantization for highly efficient information compression in information theory.

When considering generalized competitive learning from the perspective of probability and statistics, this machine learning algorithm implicitly assumes that the data is uniformly distributed. However, as the target data becomes diverse and large, a non-uniform distributional bias emerges in the big data. He considered the usable aspect of this bias and created the "Alpha Expectation Maximization Algorithm" as a theoretical superstructure of the conventional method. On this basis, he established a vertical hierarchy with vector quantization and k-means as a substructure and succeeded in systematizing related technologies by providing high-speed methods for hidden Markov algorithms and independent component analysis as horizontal generalizations. Furthermore, based on this systematization, he has brought about various applications such as audio and image information processing, personal authentication using brain signals, and blockchain for the further evaluation of data evaluators.

These achievements have been recognized with the IEICE Best Paper Award and Distinguished Achievement and Contributions Award, IEEE Neural Networks Best Paper Award, IEEE-ACM Dote Memorial Best Paper Award, and other awards, as well as the titles of IEICE Fellow, IEEE Fellow and Life Fellow, and IPSJ Fellow. He has also served as a councilor of the Tokyo Section of the IEICE. He has held many important positions in Japan, including as a member of the National Personnel Authority's Senior Comprehensive Examination Committee.

As mentioned above, he has made outstanding contributions to the development of science and technology based on electronics, information, and communication engineering, and we recommend him as a suitable Honorary Member of the IEICE.