Please tell us about your research.

My main research topic is “management and analysis of graph data.” Graph data is a data structure that models relationships between entities and is widely applicable to real-world services. For example, it's used in Google search, Amazon's product recommendation system, friend recommendation in social network sevices, and conversational AI. The analysis of relationships is an important area of research that contributes to a deeper understanding of individuals and society.

My work focuses on developing database management systems that enable high-speed graph data search, as well as deep learning methods optimized for graph data.

These research outcomes are actively being applied in fields such as chemistry, materials science, medicine, and urban engineering. One of the most exciting aspects of data and computer science is its potential to enable collaborative research across a wide range of disciplines. Through such collaborations, I have been able to broaden my knowledge and experience the joy and value of interdisciplinary learning firsthand. I find that this approach aligns very well with my academic interests.

What is “fairness”?

Recently, I have been working toward incorporating trustworthy, in particular, fairness into graph data analysis. By embedding fairness—whether for individuals or groups—into AI models, the AI models can be built based on those fairness values. Since fairness itself is complex even in human society, it is a challenging issue that cannot be easily solved by computer science alone.

I am also deeply interested in the social scientific aspects of questions such as: How is the notion of "fairness" formed in different groups? Between which groups do fairness-related gaps emerge? What kinds of "fairness" actually exist in the world? And what kinds of emotions are involved when people "trust" AI? To explore these questions, I have been collaborating with researchers from Japan, the Netherlands, and Denmark, as well as scholars in philosophy, psychology, and sociology. Together, we pose a wide range of questions to people from diverse cultural and social backgrounds across multiple countries

To investigate trustworthy and fairness, we are not only developing state-of-the-art technologies but also collecting real-world cases through surveys. We analyze what people perceive as fair and trust and conduct international comparisons based on the results.

Project website: https://yuya-s.github.io/musubi/

We named this research project "Musubi" (meaning “connection” in Japanese). We chose the swallow, a migratory bird, as our symbol because it’s considered a harbinger of good luck and I like swallows. The project’s logo was created using traditional Japanese woodblock printing. This design reflects our desire to honor not only the present but also the temporal connections that shape the future.