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  1. Ansatte

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Paul Lee

Last ned pressefoto
Last ned pressefoto
Foto:

Paul Lee

Postdoktor
Institutt for marin teknikk

paul.lee@ntnu.no
+4796982767 +306944046683 1.247 Professor Mørchs hus Tyholt, Trondheim
Google Scholar
Om Forskning Publikasjoner Undervisning Formidling

Om

CV

Bio

I am a Postdoctoral Fellow specializing in risk-informed decision making for autonomous ships during safety-critical operations. My expertise lies in applying advanced artificial intelligence techniques, such as deep reinforcement learning, for robust, sophisticated, and safe decision-making. My research interests include but are not limited to online risk modeling; explainable, controllable, and human-in-the-loop AI; decentralized robots; cyber-security; high-fidelity digital twins; and verification \& validation of autonomous systems.

Kompetanseord

  • Artificial intelligence
  • Autonomous control
  • Collision avoidance
  • Deep reinforcement learning
  • Navigation
  • Risk
  • Risk assessment
  • Risk management
  • Risk-informed decision-making
  • Safety
  • Safety-critical operation

Forskning

Postdoctoral Research | August 2024 - current

  • Work package: Safety & Assurance
  • Project: SFI AutoShip
  • Research group: The Risk Group
  • Supervisor: Ingrid Bouwer Utne, Ekaterina Kim

 

Doctor of Philosophy Research | February 2020 - August 2024

  • Project: AUTOSHIP
  • Research group: Maritime Safety Research Group (MSRC)
  • Supervisors: Gerasimos Theotokatos, Evangelos Boulougouris

 

Research Groups

  • The Risk Group
  • MSRC

SFI Autoship

SFI Autoship is an 8-year research-based innovation centre that will contribute to Norwegian players taking a leading role in the development of autonomous ships for safe and sustainable operations.

AUTOSHIP

AUTOSHIP – Autonomous Shipping Initiative for European Waters – aims at speeding-up the transition towards a next generation of autonomous ships in EU.

Publikasjoner

Robust decision-making for the reactive collision avoidance of autonomous ships against various perception sensor noise levels

This study aims to develop a methodology to enhance the robustness of decision-making for the reactive collision avoidance of autonomous ships against various perception sensor noise levels.

Risk-informed collision avoidance system design for maritime autonomous surface ships

This study aims to develop a methodology to conduct the risk-informed design for the Collision Avoidance System (CAS) of MASSs.

Summary of Published Work

  • Robust decision making: paper 2
  • Risk-informed design of autonomous systems: paper 1

 

List of Published Work

  • Kronologisk
  • Etter kategori
  • Alle publikasjoner i Nasjonalt vitenarkiv (NVA)

2025

  • Lee, Paul; Kim, Ekaterina. (2025) Towards robust deep reinforcement learning agent for the path following of autonomous ships amid perception sensor noise. Proceedings of the 35th European Safety and Reliability & the 33rd Society for Risk Analysis Europe Conference
    Vitenskapelig artikkel
  • Grimstad, Joachim; Correa-Jullian, Camila; Dugan, Spencer August; Lee, Paul; Ramos, Marilia Abilio; Thieme, Christoph Alexander. (2025) Proceedings of the 6th International Workshop on Autonomous Systems Safety.
    Annet

Tidsskriftspublikasjoner

  • Lee, Paul; Kim, Ekaterina. (2025) Towards robust deep reinforcement learning agent for the path following of autonomous ships amid perception sensor noise. Proceedings of the 35th European Safety and Reliability & the 33rd Society for Risk Analysis Europe Conference
    Vitenskapelig artikkel

Del av bok/rapport

  • Grimstad, Joachim; Correa-Jullian, Camila; Dugan, Spencer August; Lee, Paul; Ramos, Marilia Abilio; Thieme, Christoph Alexander. (2025) Proceedings of the 6th International Workshop on Autonomous Systems Safety.
    Annet

Undervisning

Marine Engineering with Specialisation in Autonomous Marine Vehicles | 2021

  • Topic: Autonomous Marine Vehicles & Digital Twin \ Guidance, Navigation, and Control \ Trajectory tracking and manoeuvring control
  • Course level: Master of Sciences (MSc)
  • Naval Architecture, Ocean, and Marine Engineering (NAOME), University of Strathclyde

Formidling

Korean-Scandinavian Scientists and Engineers Association (KSSEA)

Korean-Scandinavian Scientists and Engineers Association (KSSEA)

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