Sho Okazaki
Researcher in Infrastructure Asset Management
I am a researcher specializing in infrastructure asset management, with a focus on leveraging data science and AI techniques to enhance the resilience and sustainability of critical infrastructure systems. My work encompasses a range of topics including predictive maintenance, risk assessment, and the application of AI in engineering contexts. I am passionate about advancing the field through innovative research and practical applications that address real-world challenges in complex engineering environments.
News
- 2025-10-01
Awarded Nakajima Foundation Scholarship and Chadwick Scholarship.
- 2025-10-01
Joined UCL as a Doctoral Researcher.
- 2025-03-24
Master's thesis was awarded the Excellence Award by Department of Precision Engineering at the University of Tokyo.
- 2024-11-01
Joined the Committee of Young Researcher Association for Knowledge Graph in Japan.
- 2023-10-16
Joined University of Cambridge as a Visiting Researcher.
Featured Publications
View allA spatio-temporal anomaly detection system to support understanding of abnormal phenomena in automated manufacturing lines
Okazaki, S., Kaminishi, K., Wang, Y., Fujiu, T., Nakata, Y., Hamamoto, S., ... & Ota, J.
A concise prototype entry describing how small teams can structure writing, data, and publication artifacts so the work remains easy to maintain over time.
Machine Learning Approach to Redefining Risk in Railway Drainage Systems
Okazaki, S., Herrera, M., Sasidharan, M., McNaughton, J., Raja, J., & Parlikad, A. K.
An implementation-oriented example of using local files as the primary source of truth, with backend integration deferred until it is genuinely needed.
FBS model-based maintenance record accumulation for failure-cause inference in manufacturing systems
Fujiu, T., Okazaki, S., Kaminishi, K., Nakata, Y., Hamamoto, S., Yokose, K., ... & Ota, J.
An implementation-oriented example of using local files as the primary source of truth, with backend integration deferred until it is genuinely needed.
Current Projects
View allWhole-System Approach to Railway Asset Management
A research project focused on developing a holistic framework for managing railway assets, integrating data-driven risk assessment, maintenance optimization, and resilience strategies to enhance the safety and reliability of railway infrastructure.
Knowledge-Driven Anomaly Detection and Diagnosis in Manufacturing Systems
A research project that developed a knowledge-driven anomaly detection and diagnosis system for manufacturing systems, leveraging machine learning techniques and domain knowledge to identify and infer the causes of faults across manufacturing lines.