Message from
the center director
Unprecedented-scale Data
Analytics Center, Tohoku University
Director Mitsuyuki Nakao
The importance of promoting open science and data-driven research and education has been stated in the Sixth Science, Technology and Innovation Basic Plan of the Cabinet Office. In addition, the AI Strategy 2021 states that the need to urgently develop an environment that fosters expert personnel who have competence in AI, mathematics and data science (AIMD) and can work on creating innovation. In addition to the Tohoku Medical Megabank Project and large-scale measurement equipment such as a cryo-electron microscopy, Tohoku University is the first national university to have a next-generation synchrotron radiation facility on-campus, which will be fully operational from FY2023. Researchers from all over the world will come and go in search of this environment to carry out cutting-edge research, and a large number of companies are expected to gather under the Coalition Concept at the adjacent Science Park-type R&D base, one of the largest at a national university, to conduct "unprecedented scale" research that far surpasses the conventional scale in a wide range of fields. An environment is being developed in which the challenge of value creation from 'data' can be taken on.
As described above, Tohoku University is truly becoming a unique node of data-driven science. To contribute to this from the standpoint of data analytics, the Unprecedented Scale Data Analytics Center was established on 1 January 2022 as the third center of the Organization for Innovations in Data Synergy. The center aims to create new value and innovation by developing advanced data analysis and analysis methods for unprecedented scale data generated by the university's various research activities and the above-mentioned large-scale facilities, and by applying these methods across fields and demonstrating their effectiveness. The term 'unprecedented scale' here does not simply refer to the size of the data. It refers to data that far surpasses conventional scales in terms of the speed at which data is brought to us, the resolution of data measurement and generation, and the diversity of data modalities. While the nature of such data has the potential to bring ‘unprecedented’ insights by revealing the extremes of the subject, it is also expected to demand completely new analytics to extract value from it. The Center will boldly take up the challenge of developing such analytics. In addition, it will collaborate with research and education activities inside and outside the university under an open innovation ecosystem, and through this, foster young researchers and corporate engineers in the field of AIMD (AI, mathematics and data science) and support entrepreneurship. Currently, the AIMD education system at Tohoku University has been developed vertically and horizontally beyond the fields of specialization, from university-wide undergraduate to postgraduate levels. The center intends to provide an even more advanced and cutting-edge AIMD human resource development environment by making use of the achievements of unprecedented data analytics.
The mission of the Unprecedented Scale Data Analytics Center has no end in sight. We will continue to walk in close cooperation with the Cyberscience Center and the Center for Data-driven Science and Artificial Intelligence. We look forward to your continued guidance and support.
Department introduction
Data Analytics Research Division
Develop novel analytics for unprecedented scale data and share it extensively.
- Research and development of methods for handling
and analyzing unprecedented scale data. - Creation of new social value through cross-disciplinary research.
- Research and development of advanced analysis of research
and educational data using cutting-edge AI and data science methods. - Commonization and sharing of data analytics across diverse research fields.
- Consultation regarding processing and analytics of research
and educational data. - Development of new data-driven educational improvement methods,
their practical validation, and their commercialization.
Member
Professor, Motoki Shiga
President-Appointed Professor, Mitsuyuki Nakao
Associate Researcher, Koki Kitai
Professor, Center for Data-driven Science and Artificial Intelligence, Jun Suzuki
Professor, Graduate School of Science, Masahiro Terada
Professor, Graduate School of Engineering, Akinori Ito
Professor, Graduate School of Information Sciences, Masayuki Ohzeki
Professor, Institute for Excellence for Higher Education, Kazuhiro Sugimoto
Professor, Center for Data-driven Science and Artificial Intelligence, Yoshinori Hayakawa
Associate Professor, Graduate School of Information Sciences, Akira Suzuki
Associate Professor, Center for Data-driven Science and Artificial Intelligence, Takashi Mitsuishi
Associate Researcher, Graduate School of Information Sciences, Samy Baladram Mohammad
Data Management Division
Management that promotes efficient and secure use of unprecedented scale data as well as data provided by university researchers
- Research digital data preservation services and archiving.
- Data-related legal compliance, rule-making, and contractual services.
- Centralization of functions and tasks related to research data management.
- Support for building a data platform based on ultra-large scale storage (data lake),
supercomputers, high-speed networks, etc. in collaboration with the Cyber Science Center, and collection, storage, management, and shared use of unprecedented scale data based on this platform.
Member
Appointed Professor, Masahiro Hiji
Professor, Cyberscience Center, Takuo Suganuma
Professor, Center for Data-driven Science and Artificial Intelligence, Hiroki Shizuya
Associate Professor, Center for Data-driven Science and Artificial Intelligence, Masao Sakai
Associate Professor, Graduate School of Information Sciences, Takeshi Obayashi
Associate Professor, Center for Data-driven Science and Artificial Intelligence, Emi Yuda
Associate Researcher, Organization for Innovations in Data Synergy, Masakazu Motoki
Social Integration Research Division
Accept students, researchers, and corporate engineers who challenge unprecedented scale data, and support value creation and entrepreneurship under the open ecosystems.
- Promote social implementation through collaboration with faculties, graduate schools, research institutes, and centers within the university, as well as through joint research with overseas research and educational institutions, companies, governments, and municipalities.
- Planning special programs for working professionals in collaboration with the Center for Data-driven Science and Artificial Intelligence.
- Promote participation of graduate students, young researchers, and corporate engineers from Japan and abroad in an open ecosystem, and support for value creation initiatives and entrepreneurship.
Member
Professor, Kazunori Yamada
Associate Researcher, Shun Kodate
Associate Professor, Graduate School of Economics and Management, Takuya Ishihara
Associate Professor, Graduate School of Economics and Management, Tsukasa Ishigaki
Associate Professor, Cyberscience Center, Tohru Abe
Appointed Associate Professor, Graduate School of Information Sciences, Atsushi Koike
Edge Data Processing Division
Edge processing of unprecedented-scale data enhancing their accessibility and promoting efficient and reliable measurement and analysis
- Adaptive compression and flow control for sensor data generated by large-scale measurement facilities, such as next-generation synchrotron facilities (NanoTerasu) and cryo-electron microscopes
- Edge processing systems for measurement/experiment-adaptive and intelligent control in large-scale measurement facilities
- Edge processing systems functioning for high-efficiency, ultra-high-speed, and large-capacity networking/storage infrastructure
- Development of edge processing systems for unprecedented-scale data from device to architecture levels by integrating advanced knowledge of circuit-system hardware, CS theory, data science, and AI
- Human resource development and promotion of societal implementation based on research achievement of edge processing