Launching My Personal Website

Why I Built This Website

As I began my career as a doctoral researcher, I found myself repeatedly asking a simple question: who can truly understand my research?

In my previous research lab in Japan, I worked in a research area that was largely new to the laboratory. My work focused on applying artificial intelligence to manufacturing systems within a robotics research environment. Because it sat at the intersection of multiple disciplines, communicating the key ideas was often challenging. Researchers from different backgrounds naturally approached problems with different assumptions, terminology, and perspectives.

In many discussions and presentations, I found myself spending considerable time explaining fundamental concepts before reaching the core contributions of the research. While these conversations were valuable, they often left little room to discuss the deeper questions that motivated my work.

To understand research that spans multiple fields, a substantial amount of background knowledge is often required. Academic papers are written primarily for specialists and can be difficult to follow for readers outside the field. As a result, there is often a gap between researchers who are interested in a topic and those who can fully access its ideas.

I hope this website will help bridge that gap. Here, I aim to explain my research, share the motivations behind it, and discuss ideas that may not fit naturally into academic publications. My goal is to make my work more accessible to researchers, students, and practitioners from diverse backgrounds.

My research journey

My research journey began during my final year at the University of Tokyo. In Japan, undergraduate students are typically assigned to a laboratory and conduct a year-long research project as part of their degree. I joined a mobile robotics laboratory, where I was introduced not only to robotics but also to knowledge management in manufacturing systems.

I became particularly interested in the relationship between knowledge and data. This led me to begin research on diagnostic systems for manufacturing environments. Manufacturing in Japan relies heavily on the expertise of skilled engineers and technicians. However, demographic changes, including an aging population and a shrinking workforce, have made the preservation and transfer of expert knowledge increasingly important.

Rather than relying solely on large amounts of data, I believed that incorporating expert knowledge into computational models could provide a more efficient and reliable approach. Expert knowledge captures decades of experience and often contains valuable information that is difficult to learn directly from limited or noisy datasets.

At the University of Tokyo, I worked on modelling expert knowledge for fault diagnosis and anomaly detection. The goal was to develop systems that could emulate the reasoning processes of experienced engineers and support decision-making in complex manufacturing environments.

Today, my research focuses on asset management for railway infrastructure. While the application domain has changed, the central research question remains the same: how can we combine knowledge and data to better understand complex engineering systems? In this work, I develop models that quantify infrastructure risk and support the prioritisation of maintenance activities.

At first glance, my research path may appear diverse, spanning manufacturing systems, diagnostics, artificial intelligence, and infrastructure management. However, a common theme connects all of these topics. I am interested in uncovering the underlying mechanisms that govern engineering systems and developing methods that combine human knowledge with data-driven learning.

This website serves as a record of that journey and a place to explore ideas on the future of AI for engineering.