The Japan Statistical Society
July 11, 2022
The Institute of Statistical Mathematics (ISM)
The Japan Statistical Society (JSS)

Announcement of the Awardee ofthe Fourth Akaike Memorial
Lecture AwardAwardee/Speaker: Professor Aapo Hyvärinen(University of Helsinki, Finland)
Lecture Title: “Identifiability of latent-variable andstructural-equation models: from linear to nonlinear"
Time and Date: 16:00–18:00 Monday, September 5, 2022 (*)


Dr. Hyvärinen’s lecture will be presented at the plenary session of the Japanese
Joint Statistical Meeting 2022.
Dr. Hyvärinen will speak online from Finland.
The Japanese Joint Statistical Meeting 2022 will be held in a hybrid format with
online and in-person participation options.

Venue: Seikei University(https://www.seikei.ac.jp/university/eng/aboutus/access_campus.html)

Professor Aapo Hyvärinen

Born in 1970, Professor Hyvärinen is currently a professor at the University of Helsinki, Finland.
He has contributed many outstanding achievements in machine learning and computational
neuroscience, including the development of well recognized statistical techniques for signal
processing and image analysis. His research style, characterized by conceptualizing and refining
powerful methodologies with a particular focus on their practical application, relates to that of
the late Dr. Akaike. Professor Hyvärinen has co-authored peer-reviewed scientific articles withmore than 20 Japanese researchers, thereby extensively contributing to the Japanese statisticalcommunity. Among the works in those articles, causal discovery using non-Gaussianity, blindseparation of independent nonstationary signals, and simplified estimation for non-Gaussianstatistical models are cornerstones of recent advancements in a field of statistics.

Dr. Hirotugu Akaike (https://www.ism.ac.jp/akaikememorial/index-e.html)
The late Dr. Akaike was a statistician who proposed the Akaike Information Criterion(AIC). He established a novel paradigm of statistical modeling, characterized by apredictive point of view, that was completely distinct from traditional statistical theory.
Dr. Akaike’s research has greatly influenced a wide range of research areas. To recognize
his achievements, the Akaike Memorial Lecture Award was established in May 2016.

(*) Detailed information will be uploaded on the websites of ISM(http://www.ism.ac.jp/index_e.html) and related organizations.

Press Contact: URA Station, ISM
E-mail: ask-ura@ism.ac.jp, TEL: +81-50-5533-8580

The Japan Statistical Society
July 11, 2022

Announcement of the Awardee ofthe Fourth Akaike Memorial Lecture Award SummaryIn May 2016, the Institute of Statistical Mathematics (ISM) and the Japan Statistics Society (JSS) jointly
launched the Akaike Memorial Lecture Award program. The purpose of this award is to commemorate theachievements of the late Dr. Akaike, who established a novel paradigm to evaluate the predictive accuracyof statistical modeling. He proposed a metric for model selection, the Akaike Information Criterion (AIC),
based on an approach completely different from the statistical theories common at the time. Dr. Akaike’s
ideas have influenced a wide range of theoretical and applied research areas.
The Fourth Award goes to Professor Aapo Hyvärinen of the University of Helsinki, Finland. Professor
Hyvärinen has contributed numerous outstanding achievements in machine learning and computational
neuroscience, including the development of well recognized statistical techniques in signal processing andimage analysis. Among his accomplishments, his contributions to the development of independent
component analysis (ICA) are well recognized and valued in the statistics and machine learning communities.
His academic interests and research style are closely related to those of the late Dr. Akaike. ProfessorHyvärinen is a renowned international leader in pure and applied statistical science and deserves the Akaike
Memorial Lecture Award.
Because of the COVID-19 pandemic and related overseas travel restrictions, Professor Hyvärinen will
present his lecture online at the plenary session of the Japanese Joint Statistical Meeting (JJSM) 2022 which
will be held on September 5 at Seikei University, Tokyo.

Reasons for the Award
Professor Hyvärinen has contributed many outstanding achievements in machine learning andcomputational neuroscience, including the development of well recognized statistical techniques for signalprocessing and image analysis. He has also made great contributions in causal discovery, whereby observed
data are analyzed to identify their possible cause-and-effect relationships. Moreover, he has advanced thedevelopment of estimation methods for complex statistical models that usually require data normalization.
Among his achievements, Professor Hyvärinen’s contributions to the development of the ICA are wellrecognized and valued in the statistics and machine learning communities. His original articles have been
cited more than 20,000 times. His research style, characterized by his ability to conceptualize and organize
powerful ideas with a particular focus on practical applications, is similar to that of the late Dr. Akaike. He
has developed a new type of information criterion based on the AIC, thereby expanding the paradigm
pioneered by Dr. Akaike.
Notably, Professor Hyvärinen has conducted joint research with many Japanese junior and senior statisticalscientists, co-authoring peer-reviewed journal articles with more than 20 of them. His works on causal
discovery using non-Gaussianity, blind separation of independent nonstationary signals, and simplified
estimation for non-Gaussian statistical models have been cornerstones of recent advancements in a field ofstatistics. His numerous contributions to statistical science and the Japanese statistical community

1/5

The Japan Statistical Society
demonstrate that Professor Hyvärinen deserves the award.
On the basis of these considerations, the nomination committee has selected Professor Hyvärinen, arenowned global leader in theoretical and applied statistical science, as the fourth awardee of the Akaike
Memorial Lecture Award.

 The Fourth Awardee: Professor Aapo Hyvärinen
Academic Achievements
Professor Hyvärinen has made many outstanding contributions to the fields of signal processing, image
analysis, machine learning, computational neuroscience and mathematical statistics. Among hisachievements, Professor Hyvärinen’s contributions to the development of the ICA are well recognized andvalued in the statistics and machine learning communities. His original articles have been cited more than
20,000 times. The ICA represents a method for separating independent signals from their mixtures. TheICA has had far-reaching influences on a wide range of scientific topics, such as identifying major soundsources from noise-corrupted speech signals and extracting relevant neural signals from brain-wave data.
The unique approach of the ICA, which is based on a minimum set of assumptions, is distinctly different
from many conventional statistical methodologies. Its initial publication was reportedly met withastonishment and praise from the statistical community.
Professor Hyvärinen improved and generalized his ICA algorithms to apply them to a wide range ofscientific areas. With minor modifications, they have also been applied to causal discovery research. As
one of the many applications of his idea, the development of the noise contrastive estimation (NCE) method
is described here. The NCE method enables powerful parameter estimation for complex, unnormalized
statistical models that represent conditions where analytical normalization is almost impossible. The
development of the NCE method showcases the creative talents of Professor Hyvärinen. Because thepeculiarities of the NCE method prevent the use of the AIC for model selection, a group of researchers
including Professor Hyvärinen has recently developed an information criterion applicable to the NCE
method. Because the new criterion was derived from the AIC, it is considered an NCE version of the AIC.
The group’s work has expanded the paradigm proposed by the late Dr. Akaike.

TitleAuthur
Independent component analysis
AHyvärinen, J Karhunen, E Oja
John Wiley & Sons
Fast and robust fixed-point algorithms for independent component analysis
AHyvarinen
IEEE Transactions on Neural Networks 10 (3), 626-634
Afast fixed-point algorithm for independent component analysis
AHyvärinen, E Oja
Neural Computation
Noise-contrastive estimation:Anew estimation principle for unnormalized statisticalmodels
M Gutmann,AHyvärinen
Proceedings of the Thirteenth International Conference onArtificial Intelligence andStatistics

2/5

Number of Citations
as of May 30, 2022

Year21265

2001

7846

1999

4578

1997

1326

2010

The Japan Statistical Society
Validating the independent components of neuroimaging time series via clusteringand visualization
J Himberg,AHyvärinen, F Esposito
Neuroimage 22 (3), 1214-1222
Alinear non-Gaussian acyclic model for causal discoveryS Shimizu, PO Hoyer,AHyvärinen,AKerminen, M Jordan
Journal of Machine Learning Research 7 (10)Natural Image Statistics:Aprobabilistic approach to early computational vision
AHyvärinen, J Hurri, PO Hoyer
Springer-Verlag, New York
Estimation of non-normalized statistical models by score matching.
AHyvärinen
Journal of Machine Learning Research 6 (4)Information criteria for non-normalized models
T Matsuda, M Uehara,AHyvärinen
Journal of Machine Learning Research
Unsupervised feature extraction by time-contrastive learning and nonlinear ica
AHyvarinen, H Morioka
Advances in Neural Information Processing Systems, 3765-3773

1130

2004

1116

2006

815

2009

677

2005
2021
2016

By Google ScholarCV
Name: Aapo Johannes Hyvärinen

Current Position: Professor, Department of Computer Science, University of Helsinki, Finland
Born: 1970
Education: Ph.D. degree in Information Science at the Helsinki University of Technology, Finland in 1997
(Doctoral Thesis: “Independent component analysis: A neural network approach”)
Professional Summary:
2003-

University of Helsinki

2008-

Professor, Department of Computer Science, University of Helsinki

2016-2019 Professor, Gatsby Computational Neuroscience Unit, University College London, UK

Details of the Fourth Akaike Memorial Lecture
Under the sponsorship of JSS, the Fourth Akaike Memorial Lecture will be jointly hosted by ISM and theOrganizing Committee for JJSM 2022. The lecture will be delivered online at the plenary session of JJSM
2022.

3/5

The Japan Statistical Society

Session Title:

The plenary session of JJSM 2022: the Akaike Memorial Lecture

Speaker:

Professor Aapo Hyvärinen, University of Helsinki, Finland

Lecture Title:

Identifiability of latent-variable and structural-equation models:from linear to nonlinear

Moderator:

Professor Tomoyuki Higuchi (Director General, the Japanese Federation ofStatistical Science Associations / President, JSS)

Chair:

Professor Hiroe Tsubaki (Director-General, ISM)

Panelists:

Dr. Takeru Matsuda (Unit Leader, Statistical Mathematics Unit, RIKEN
Center for Brain Science)
Dr. Hiroshi Morioka (Postdoctoral Researcher, RIKEN Center for AdvancedIntelligence Project)*The entire session will be presented in English.

Time and Date:

16:00 p.m. to 18:00 p.m., Monday, September 5, 2022

Venue:

Seikei University
3-3-1 Kichijoji-Kitamachi, Musashino-shi, Tokyo, 180-8633, Japan(https://www.seikei.ac.jp/university/eng/aboutus/access_campus.html)*The lecture will be held in a hybrid format with online and in-person
participation options.

For more details of the lecture, please visit the official web sites of the following organizations: ISM (https://www.ism.ac.jp/index_e.html),
 JJSM 2022 (https://confit.atlas.jp/guide/event/jfssa2022/top), and JSS (https://www.jss.gr.jp/en/).

The Purpose of the Akaike Memorial Lecture Award
In 2014, ISM and JSS started discussions to launch the Akaike Memorial Lecture Award as their joint
program to commemorate the late Dr. Hirotugu Akaike’s achievements and impact in statistical science.
(*)
The Awardee of this award will deliver the lecture (hereinafter referred to as the "Akaike MemorialLecture"). This award aims to provide Japanese and other statistical scientists with opportunities to network
and interact with each other, nurture junior researchers, and advance statistical science.
Once every two years, one awardee will be selected from among international researchers who havemade far-reaching and pioneering achievements similar to those of the late Dr. Akaike. The scope ofnomination covers pure and applied research in mathematical science, mathematical engineering, and otherstatistical sciences (including, for example, system control and optimization algorithms). The awardee will
receive an honorarium of 100,000 yen, a commemorative award plaque, and travel expenses.
As a means for researcher development, several discussants selected from among promising graduate
students and junior researchers will have an opportunity to participate in a question-and-answer session

4/5

The Japan Statistical Society
with the awardee. Each lecture presentation and a summary of subsequent discussions will be published as
an invited article in the Annals of the Institute of Statistical Mathematics.

(*) About the Late Dr. Hirotugu AkaikeDr. Akaike was born in Shizuoka Prefecture, Japan, on November 5, 1927. After graduating from the Naval
Academy of Japan, the First High School, and the Department of Mathematics, School of Science, The
University of Tokyo, he joined ISM in 1952.
In the 1960s, he made pioneering contributions in the field of time-series analysis. He developed newmethodologies for spectral analysis, multivariate time-series analysis, and statistical system control,contributing to the development of the Time Series Analysis and Control (TIMSAC) software package. Inthe 1970s, he launched the Akaike Information Criterion (AIC), which led to a new paradigm of statisticalmodeling that enables estimations of the goodness of predictive fits. This approach was a radical departurefrom the statistical approaches common at the time and profoundly influenced a wide array of research fields.
In the 1980s, he developed methods that facilitate the practical application of Bayesian modeling. His works
paved a path to novel information processing analytics in the "big data" era. For his widely acknowledged
and long-lasting contributions to the statistical sciences, he received the Purple Ribbon Medal of Honor, theGold and Silver Star (Second Class) of the Sacred Treasure, the Kyoto Prize, and many other prestigious
awards.
Serving as the Director-General of ISM from 1986, he was also engaged in postgraduate education when
the Graduate University for Advanced Studies known as SOKENDAI (Kanagawa, Japan) launched theDepartment of Statistical Science. After his term expired in 1994, he became Professor Emeritus both at ISM
and at the Graduate University for Advanced Studies. After Dr. Akaike retired from full-time posts, hispassion for statistical research remained insatiable and he continued to work on Bayesian and other statistical
approaches. He published several journal articles analyzing the relationship between golf-swing motion
parameters and driving distance as an illustrative example of mathematical modeling. From January 1989 toDecember 1990, he also served as the 19th President of JSS.
On August 4, 2009, he passed away at the age of 81 in Ibaraki Prefecture. On November 5, 2017, acommemorative doodle was posted on the Google search screen in 16 countries and regions to celebrate"Hirotugu Akaike’s 90th Birthday"(source: https://www.google.com/doodles/hirotugu-akaikes-90th-birthday).
Hirotugu Akaike Memorial Website:
https://www.ism.ac.jp/akaikememorial/index_e.html-------------------------------------------URAStation, ISM
E-mail: ask-ura@ism.ac.jp
Phone: +81-50-5533-8580 (direct)

5/5