We study information-theoretic frameworks and its applications to unravel the valuable information from the data collected and generated from complex and diverse natural phenomena, social phenomena and human activities, especially cases that are transient and difficult to process routinely.  
 

・Three-dimensional measurement of animal flocking behavior and modeling of collective dynamics
・Study of pattern formation in interfaces growing under nonequilibrium conditions

・Structural analysis for solution space in combinatorial reconfiguration problems
・Quantitative analysis and computational aspects of solution diversity and robustness

・Development of organizational learning analysis methodology focused on transition of learning process
・Evidence-based advancement of teaching and learning and the design of IT-enhanced educational environment
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・Construction of a Visualization and Behavioral Analysis Framework for Supporting Self-Regulated Learning (SRL)
・Construction of a Decentralized Privacy-Preserving Processing and Analysis Framework for Self-Sovereign Data

・Developments of a regression analysis method using sparse estimation and its estimation algorithm
・Modeling spatio-temporal data using regression analysis methods