Dr. Naonori Ueda graduated from the Department of Communications Engineering in the Faculty of Engineering at Osaka University in 1982, received his Master's degree in Communications Engineering from Osaka University in 1984, and joined the Yokosuka Telecommunications Research Laboratories of Nippon Telegraph and Telephone Public Corporation (currently NTT) in the same year. In 1991, he was a senior researcher at NTT Communication Science Laboratories. He received a Ph.D. in engineering from Osaka University in 1992 and in 1993, he was a visiting researcher at Purdue University in the U.S. He was also an invited researcher at the University of Toronto in Canada in 1997, and an invited researcher at the University of London in the U.K. in 1999. From 2010 to 2013, he served as Director of NTT Communication Science Laboratories, and from 2013 to 2016, he was head of the NTT Machine Learning and Data Science Center. From 2016 to 2023, he was head of the Ueda Research Laboratory (NTT Fellow) at NTT Communication Science Laboratories. He is currently Deputy Director of the RIKEN Center for Advanced Intelligence Project and a visiting fellow at NTT Communication Science Laboratories.

Since the dawn of machine learning research, which is now regarded to be a fundamental technology for artificial intelligence, he has made pioneering contributions to statistical learning theory and its application to pattern recognition. He has also made significant contributions to the academic development of the field. Specifically, he devised a theory and an algorithm for local optimality of the EM algorithm, which is a widely used learning method in statistical machine learning. He significantly improved the quality of the parameter estimation problem, which is a challenge in mathematical modeling for a wide variety of actual data. Moreover, by formulating the multi-classification problem when a single document consists of multiple classes (topics), such as online text, he devised the world's first multi-topic text model (parametric mixture model) and established technology to simultaneously extract multiple characteristic topics hidden in large-scale data with high accuracy. Furthermore, he established the world's first automatic model acquisition learning method for hidden Markov models, which were widely used in speech recognition, and laid the foundation for the optimal model determination method based on Bayesian learning, which has become mainstream in recent years. He also devised a people-flow prediction technology for spatio-temporal data analysis based on machine learning, which is now used in actual services.

In the Cabinet Office's FIRST program, he developed a technology for the automatic recognition of nursing activities using acceleration sensors, thereby significantly enhancing the recognition performance of conventional technologies. Furthermore, he was the first to successfully extract significant statistical information for operational analysis, such as in the relationship between nurse action types, their time required, and patient severity, from approximately 9 million actual nursing action histories accumulated in hospital wards, paving the way for health and medical big data analysis using information and communication technology (ICT). In addition, he received the highest evaluation for his work on automatic supernova detection utilizing a learning method from rare data in a JST CREST-funded study and optimal people-flow guidance based on spatio-temporal statistical analysis in a NICT-funded study. Since being appointed Deputy Director of the RIKEN Center for Integrated Research on Innovative Intelligence in September 2016, he has achieved notable outcomes in the application of machine learning to natural and social sciences, including medicine and disaster prevention/mitigation. He has also been appointed as a research director of the JST CREST Mathematical Information Utilization Platform since FY2019, with the objective of fostering the integration of mathematics and information research.

For these achievements, he has received the Best Paper Award, Fellow, Achievement Award, and Contribution Award of the IEICE, as well as the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology (Research Category) and the Telecommunications Advancement Foundation Award, among others.

As mentioned above, his academic contributions to electronics and telecommunications have been outstanding, and we recommend him as the IEICE's Honorary Member.