Jialun Chen

Jialun Chen serves as an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow within the Department of Engineering Science at the University of Oxford, and is a member of Reuben College. His research focuses on leveraging machine learning for precise wave forecasting at large temporal and spatial scales to enhance coastal dynamics modeling and offshore operational safety.

His educational background includes:

• PhD in Ocean Engineering (specialization: Machine Learning) from the University of Western Australia, with doctoral research on phase-resolved wave and motion forecasting for offshore infrastructure.

Chen's research centers on machine learning applications in ocean engineering, specifically:

• Machine learning-based wave modeling and forecasting for coastal/offshore environments
• Short-term motion prediction of floating infrastructure to enable safe ship-to-ship transfers and offshore operations
• Integration of physical understanding with data-driven models for improved accuracy

Analysis of his 2022-2024 publications reveals a progression from autoregressive models to sophisticated phase-resolved predictions incorporating physical constraints, with consistent emphasis on practical applications for offshore wind energy operations and marine safety.

No scientific awards or honors are documented in the available information.

Chen is supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship and contributes to the TetraSpar Offshore Wind Turbine Project, where he develops machine learning solutions for safe floater-to-floater transfers. No student advisement roles are indicated.

He operates within Oxford's Marine Renewable Energy research group under Professor Thomas Adcock, focusing on advancing offshore wind energy technologies through AI-driven operational safety solutions for floating infrastructure.