Naonori Ueda respectively received B.S., M.S., and
PhD degrees in Communication Engineering from Osaka University, Osaka, Japan in
1982, 1984, and 1992. In 1984, he joined the Electrical Communication
Laboratories of NTT in Japan, where he researched image processing, pattern
recognition, and computer vision. In 1991, he joined the NTT Communication
Science Laboratories. From 1993 to 1994, he was a visiting scholar at Purdue
University, West Lafayette, USA. He was the director of the NTT Communication
Science Laboratories from April 2010 to March 2013. He is currently the head of
the Ueda Research Laboratory (NTT Fellow) and the director of the Machine
Learning and Data Science Center. He also serves as the deputy director of the
RIKEN Center for the Advanced Intelligence Project, which was established in
April, 2016. His current research interests include statistical machine
learning and its applications to such natural sciences as medical, seismology,
and meteorology. He is a visiting professor at the following three institutions:
the Graduate School of Informatics, Kyoto University, Kobe University, and
Meiji University. In 2019, he also serves as the research supervisor of
Mathematical Information Platform, Japan Science and Technology Agency (JST).
He is a member of the Information Processing Society of Japan (IPSJ), a fellow
of the Institute of Electronics, Information, and Communication Engineers in
Japan (IEICE), and a senior member of IEEE.
Summary of Research
Achievements
In the early 1990s, as multi-layer neural
networks were falling out of fashion, research had just started on statistical
machine learning based on mathematical statistics, which can be described as
the progenitor of modern machine learning. This is when he first started doing
basic research in the field of machine learning. He also worked on a
number of crucial cutting-edge projects, including Bayesian learning theory and
its application to speech recognition. He has made significant technical
contributions in the field of statistical machine learning, such as
Nonparametric Bayes theory and its applications to relational data analysis. He
worked as a sub-leader at the Funding Program for World-Leading Innovative
R&D on Science and Technology (FIRST), at which he is a central research
representative, while simultaneously designing an automatic technique for
recognizing actual nursing activities from acceleration sensor data. Using this
technique, he became the first scientist to analyze some nine million actions,
an achievement that would not have been possible with conventional technology.
Next, he launched a project called the gspatio-temporal multidimensional
collective data analysis researchh as a representative of the NTT Machine
Learning Data Science Center (NTT MLC) and developed a spatio-temporal
prediction method applicable to unstationary data and real-time proactive
people flow navigation technology. As the director of the Goal-oriented
Technology Research Group at RIKEN AIP, he has been managing science research
and social issues such as disaster prevention in Japan,@such as disaster prevention and
mitigation, through machine learning technologies.
The following are his primary research
achievements.
(1) Basic machine learning methods:
Naonori Ueda improved the quality of the solutions obtained by vector
quantization, which is a critical technique used in the distortion-tolerant
compression of such media as speech and images. He also discovered novel
conditions necessary for obtaining optimal solutions and devised an online
learning algorithm that implements these design principles by approximation.
This research used new principles and algorithms to solve the issue of optimal
quantizer design, resulting in a major step forward in the optimization of such
quantizer design technology. For this work, he received an award from the
Telecommunications Advancement Foundation in 1997. He is currently involved in
a pioneering study of current online clustering.
Ueda also devised a novel method called
Deterministic Annealing Expectation Maximization (DAEM) by applying the
concepts of statistical mechanics to the poor local optimal problem associated
the EM algorithm and devised a method called Split-Merge Expectation
Maximization (SMEM) for solving local optimality problems in mixed models. Both
techniques greatly improved the quality of parameter estimation solutions over
a wide range of applications and gained international acclaim, including an
introduction in an eminent mathematical statistics textbook.
Ueda rapidly became one of Japan's authorities on
advanced statistical machine learning, such as variational Bayesian estimation
as an approximate calculation method for large-scale statistical models. He
also collaborated with researchers in speech recognition to develop the world's
first model structure automatic learning method using a hidden Markov model,
for which his research team received several research awards. His research
group developed novel algorithms for relational data analysis based on
nonparametric Bayes theory for handling infinite dimensional data
partitioning. Uedafs team also developed methods for spatio-temporal
statistical data analysis, including spatio-temporal point process using
general intensity functions with deep neural networks.
(2) Applications based on machine learning
approach:
In conventional pattern classification, a single pattern is assumed to be
attributable to a unique class. When a single document consists of multiple
classes like the text data on the web, conventional pattern classification
techniques are inapplicable. To address this multiple classification issue,
Ueda devised the first multiplex topic text model (PMM: Parametric Mixture
Model) and demonstrated its usefulness in tests where it performed multiplex
topic classification of tens of thousands of actual web pages. This technique
was put into practical use as the topic classification engine of the news
article classification service at the NTT Group portal site (Goo). This
multiplex classification problem was later presented at an international
workshop, where he joined the administration committee as a pioneering
researcher.
Although techniques for behavior recognition
from diverse sensors have recently been studied both in Japan and overseas,
Uedafs work on the automatic behavior recognition of 41 different nursing
actions from four acceleration sensors constitutes a unique contribution in
this field. In this problem where conventional technology achieved a
recognition rate of no more than 30%, he proposed a meta-learning method that
remarkably boosted the success rate to over 60%. In collaboration with medical
researchers at FIRST, this technique was used to analyze about nine million
actions by nurses in the cardiac ward of Saiseikai Kumamoto Hospital. This
collaboration is the first time useful statistics have been gathered on the
relationships among nursing actions, the time taken to perform them, and the
severity of the patients' conditions. This research introduced an entirely new
avenue of big data analysis in ICT applications to healthcare.
As the director of NTT MLC, he launched
spatio-temporal multidimensional collective data analysis research and
developed a novel real-time, proactive navigation approach based on
spatio-temporal predictions and gwhat-ifh simulations. An NTT operating company
used this software in its actual operations. This series of spatio-temporal
multidimensional collective data analysis studies was also commended by the
Asia Pacific Signal Processing Society.
As the team leader of the Disaster Resilience Science
Team at RIKEN AIP, Ueda has been managed novel machine learning approaches for
several important research themes in the field of natural hazards and
seismology. Specific topics include earthquake damage estimation, earthquake
occurrence forecasting, and landslide susceptibility mapping. In 2019, our
research results received the SC2017 Best Poster Award and the Best Paper Award
from the International Society for Photogrammetry and Remote Sensing.
Association Memberships
The Institute of Electronics, Information and Communication
Engineers(IEICE)
Japanese Neural Network Society(JNNS) `2007
Information Processing Society of Japan(IPSJ)
IEEED
Japanese Association for Medical Artificial Intelligence