Mingyang Zhang

Mingyang Zhang is a Postdoctoral Researcher at the Department of Energy and Mechanical Engineering, Aalto University, Finland. His research focuses on the intersection of Maritime Engineering and Artificial Intelligence, particularly in Digital Twin Technology, Ship Motion Prediction, and Risk Analysis for Arctic and inland waterway operations.

• Awarded Best Paper at G-NAOE 2024
• Honored with 5 Highly Cited Paper Awards (2021-2024)
• Active in Marine and Arctic Technology research group

His machine learning work addresses ship fuel consumption prediction, collision avoidance, and grounding risk assessment, with applications in autonomous shipping and offshore wind farm monitoring. Key publications span Reliability Engineering, Ocean Engineering, and Marine Hydrodynamics journals.

Recent article trends include:

• Digital Twin applications for shipping decarbonization (2024)
• Deep learning models for 6-DoF ship motions and focused wave prediction (2023)
• Big Data Analytics for Arctic navigation risks and inland waterways (2022-2021)
• Integration of HFACS and fault tree analysis in risk modeling (2019)

  Scientific achievements include:

  • 2024: Best Paper, G-NAOE Conference
  • 2023: 2 Highly Cited Paper Awards
  • 2022: 2 Highly Cited Paper Awards
  • 2021: Highly Cited Paper Award
  • 2019: Best Paper, 5th International Conference on Transportation Information and Safety

  Current research at the Marine and Arctic Technology group emphasizes AI-driven maritime safety, real-time risk monitoring, and data-intensive shipping decarbonization projects. Collaborations include Spyros Hirdaris, Pentti Kujala, and Jakub Montewka.