Call for Proposal
Basic information of the Research Area
[Prediction Mathematical Foundation]Creation of mathematical foundation for prediction and control
Research Supervisor
Motoko KotaniPrincipal Investigator / Professor, AIMR, Tohoku University;
Executive Vice President for Research, Tohoku University
Strategic Objective
Scientific prediction and control as the foundation of a new society and industry
Overview
In global and social issues becoming more manifest and severe due to complex intertwined factors, in order to respond to the threats and uncertain situations we face, we must accurately identify and predict important signs and points of change, and furthermore, avoid transitions to irreversible conditions. In addition, by intervening in events based on such predictions, it is necessary to build a new social infrastructure that can control events, which means to ultimately lead to a more desirable state (or maintain a favorable state).
Achieving this necessitates not only the integration and application of all relevant information and data pertaining to natural and social phenomena across various fields but also leveraging the strength of mathematical sciences in abstracting and visualizing complex phenomena. We consider it crucial to aim at deepening the understanding and elucidation of these phenomena, and based on that, generate new theories and innovative technologies related to prediction and control.
In this research area, we aim to develop advanced mathematical analysis and evaluation methods through the integration of mathematics/mathematical sciences and other research fields, targeting real-time data and big data related to social issues. These methods are designed to generate new fundamental theories for prediction and control based on analysis and evaluation results, and to establish foundational technologies for applying these theories to real-world problems.
Specifically, we abstract social issues involving multiple phenomena through mathematical models and mathematical descriptions, deriving causal relationships and key parameters. By utilizing expertise from various research fields and technologies such as artificial intelligence and machine learning, we engage in verifying the plausibility of these abstractions, identifying early signs of change and the characteristics of post-change states, and attempting interventions on the phenomena. Additionally, for the application to real-world problems, we work on developing predictive programs and proposing new intervention and control methods, taking into account expert knowledge from various fields and the needs of society and industry.
This research area participates in the Ministry of Education, Culture, Sports, Science and Technology (MEXT)’s Advanced Integrated Intelligence Platform Project on Artificial Intelligence/Big Data/IoT/Cybersecurity (AIP Project).
Research Area Advisors
| Hirokazu Anai | Principal Research Director, Fujitsu Research, FUJITSU LIMITED |
|---|---|
| Jun-ichi Imura | Professor/Executive Vice-President, School of Engineering, Tokyo Institute of Technology |
| Yasuyuki Kawahigashi | Profesor, Graduate School of Mathematical Sciences, The University of Tokyo |
| Tomoyuki Shirai | Deputy Director/Professor, Institute of Mathematics for Industry, Kyushu University |
| Tetsuo Hatsuda | Program Director, Interdisciplinary Theoretical and Mathematical Sciences Program, RIKEN |
| Kenji Fukumizu | Professor, Department of Advanced Data Science, The Institute of Statistical Mathematics |
| Kenji Yamanishi | Profesor, Graduate School of Inforamation Science and Technology, The University of Tokyo |
The list of Research Area Advisor will be updated later.
Schedule of Selection Process
| Deadline for application | 2024/06/04 at 12:00 noon, Japan time |
|---|---|
| Document-based review | Late June – Middle of July |
| JST will contact to the interviewees no later than | Early July - Late July |
| Interview-based review（ONLINE） ※Interview date and time will be assigned by JST. |
Middle of July – Early August |
Research Supervisor's Policy
Research Supervisor's Policy of this Research Area can be downloaded from below.