I am an Assistant Professor at Ritsumeikan University working on graph and network modeling, with a focus on understanding complex relational systems through a combination of discrete mathematics, network science, and machine learning.
My research explores how real-world systems can be represented as graphs and how learning algorithms can be applied to analyze their structure, dynamics, and evolution. I am particularly interested in relationship discovery, network prediction, and information diffusion.
An important aspect of my work involves constructing graphs from unstructured data. In this context, I study natural language processing methods, including large language models, to extract structured knowledge and design ontologies for automatic knowledge graph construction.
My current research focuses on modeling and predicting business-to-business interactions using graph-based approaches.
Many real-world systems—such as economic networks, social interactions, and information ecosystems— can be naturally represented as graphs. However, these systems are often only partially observable and embedded in large volumes of unstructured data.
My research aims to develop a unified framework for modeling, constructing, and analyzing such networks by combining tools from discrete mathematics, network science, and graph-based machine learning. A key challenge is bridging the gap between unstructured data and structured representations.
By integrating advances in natural language processing with graph learning techniques, I aim to enable predictive and interpretable analysis of complex systems.
Email: [lastname]@fc.ritsumei.ac.jp
Office: Ritsumeikan University, Osaka
Last updated: April 2026