Daniel Menges
About
Daniel Menges is a Postdoctoral Researcher in the Department of Clinical and Molecular Medicine at the Norwegian University of Science and Technology (NTNU) and a member of the research group Artificial Intelligence and Digital Pathology in Cancer (AICAN). He develops machine learning-based methods for medical artificial intelligence (AI) aimed at detection and outcome prediction in breast and lung cancers. His work centers on computationally demanding whole-slide histopathology images (WSIs) and integrates these images with genomic profiles and cancer-subtype information to improve prognosis and support treatment planning.
He completed a Ph.D. in Engineering Cybernetics at the NTNU, contributing to the Centre for Research-based Innovation (SFI) AutoShip on situational awareness and optimal control for autonomous surface vessels, and participating in the PERSEUS doctoral program focused on digital twins. He holds a Master’s degree in Mechanical Engineering from Karlsruhe Institute of Technology (Germany), specializing in Automation and Robotics with an emphasis on control theory and machine learning.
Research
Objectives:
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Develop machine-learning models for detection and outcome prediction in breast and lung cancer using whole-slide histopathology images (WSIs).
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Integrate WSIs with genomic profiles and cancer-subtype information to improve prognostic accuracy and support treatment planning.
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Build memory- and compute-efficient pipelines for gigapixel WSIs (e.g., tiling, multiple-instance learning, self-supervised pretraining).
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Deliver interpretable, clinician-facing tools (heatmaps and case-level explanations) and reproducible software suitable for clinical research workflows.
Publications
2025
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Rasheed, Adil;
Brekke, Edmund Førland;
Lekkas, Anastasios M.;
Menges, Daniel.
(2025)
Digital Twin for Situational Awareness and Optimal Control of Autonomous Surface Vessels.
Norges teknisk-naturvitenskapelige universitet
Norges teknisk-naturvitenskapelige universitet
Doctoral dissertation
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Berg, Henrik Stokland;
Menges, Daniel;
Tengesdal, Trym;
Rasheed, Adil.
(2025)
Digital twin syncing for autonomous surface vessels using reinforcement learning and nonlinear model predictive control.
Scientific Reports
Academic article
2024
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Menges, Daniel;
Brandis, Andreas von;
Rasheed, Adil.
(2024)
Digital Twin of Autonomous Surface Vessels for Safe Maritime Navigation Enabled Through Predictive Modeling and Reinforcement Learning.
Academic chapter/article/Conference paper
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Vaaler, Aksel;
Husa, Svein Jostein;
Menges, Daniel;
Larsen, Thomas Nakken;
Rasheed, Adil.
(2024)
Modular control architecture for safe marine navigation: Reinforcement learning with predictive safety filters.
Artificial Intelligence
Academic article
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Menges, Daniel;
Tengesdal, Trym;
Rasheed, Adil.
(2024)
Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous Surface Vessels.
IFAC-PapersOnLine
Academic article
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Menges, Daniel;
Rasheed, Adil;
Martens, Harald;
Pedersen, Torbjørn.
(2024)
Real-Time Predictive Condition Monitoring Using Multivariate Data.
IEEE Transactions on Image Processing
Academic article
-
Menges, Daniel;
Rasheed, Adil.
(2024)
Digital Twin for Autonomous Surface Vessels: Enabler for Safe Maritime Navigation.
arXiv.org
Academic literature review
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Brandis, Andreas von;
Menges, Daniel;
Rasheed, Adil.
(2024)
Multi-Target Tracking for Autonomous Surface Vessels Using LiDAR and AIS Data Integration.
Applied Ocean Research
Academic article
-
Menges, Daniel;
Stadtmann, Florian;
Jordheim, Henrik;
Rasheed, Adil.
(2024)
Predictive Digital Twin for Condition Monitoring Using Thermal Imaging.
arXiv
Article in business/trade/industry journal
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Menges, Daniel;
Rasheed, Adil.
(2024)
Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data.
Academic chapter/article/Conference paper
2023
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Menges, Daniel;
Rasheed, Adil.
(2023)
An environmental disturbance observer framework for autonomous surface vessels.
Ocean Engineering
Academic article
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Menges, Daniel;
Sætre, Simon Mork;
Rasheed, Adil.
(2023)
Digital Twin for Autonomous Surface Vessels to Generate Situational Awareness.
Academic chapter/article/Conference paper
Journal publications
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Berg, Henrik Stokland;
Menges, Daniel;
Tengesdal, Trym;
Rasheed, Adil.
(2025)
Digital twin syncing for autonomous surface vessels using reinforcement learning and nonlinear model predictive control.
Scientific Reports
Academic article
-
Vaaler, Aksel;
Husa, Svein Jostein;
Menges, Daniel;
Larsen, Thomas Nakken;
Rasheed, Adil.
(2024)
Modular control architecture for safe marine navigation: Reinforcement learning with predictive safety filters.
Artificial Intelligence
Academic article
-
Menges, Daniel;
Tengesdal, Trym;
Rasheed, Adil.
(2024)
Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous Surface Vessels.
IFAC-PapersOnLine
Academic article
-
Menges, Daniel;
Rasheed, Adil;
Martens, Harald;
Pedersen, Torbjørn.
(2024)
Real-Time Predictive Condition Monitoring Using Multivariate Data.
IEEE Transactions on Image Processing
Academic article
-
Menges, Daniel;
Rasheed, Adil.
(2024)
Digital Twin for Autonomous Surface Vessels: Enabler for Safe Maritime Navigation.
arXiv.org
Academic literature review
-
Brandis, Andreas von;
Menges, Daniel;
Rasheed, Adil.
(2024)
Multi-Target Tracking for Autonomous Surface Vessels Using LiDAR and AIS Data Integration.
Applied Ocean Research
Academic article
-
Menges, Daniel;
Stadtmann, Florian;
Jordheim, Henrik;
Rasheed, Adil.
(2024)
Predictive Digital Twin for Condition Monitoring Using Thermal Imaging.
arXiv
Article in business/trade/industry journal
-
Menges, Daniel;
Rasheed, Adil.
(2023)
An environmental disturbance observer framework for autonomous surface vessels.
Ocean Engineering
Academic article
Part of book/report
-
Menges, Daniel;
Brandis, Andreas von;
Rasheed, Adil.
(2024)
Digital Twin of Autonomous Surface Vessels for Safe Maritime Navigation Enabled Through Predictive Modeling and Reinforcement Learning.
Academic chapter/article/Conference paper
-
Menges, Daniel;
Rasheed, Adil.
(2024)
Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data.
Academic chapter/article/Conference paper
-
Menges, Daniel;
Sætre, Simon Mork;
Rasheed, Adil.
(2023)
Digital Twin for Autonomous Surface Vessels to Generate Situational Awareness.
Academic chapter/article/Conference paper
Report
-
Rasheed, Adil;
Brekke, Edmund Førland;
Lekkas, Anastasios M.;
Menges, Daniel.
(2025)
Digital Twin for Situational Awareness and Optimal Control of Autonomous Surface Vessels.
Norges teknisk-naturvitenskapelige universitet
Norges teknisk-naturvitenskapelige universitet
Doctoral dissertation
Outreach
2025
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Academic lectureMenges, Daniel. (2025) The Potential of Intelligent Algorithms for Cancer Diagnosis and Therapy. ScanPath - the Scandinavian Symposium on Translational Pathology , Bergen 29.10.2025 - 30.10.2025
2024
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PosterMenges, Daniel; Rasheed, Adil. (2024) Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous Surface Vessels. 8th IFAC Conference on Nonlinear Model Predictive Control (NMPC) , Kyoto (Japan) 21.08.2024 - 24.08.2024
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PosterMenges, Daniel; Rasheed, Adil. (2024) Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data. IEEE Conference on Artificial Intelligence , Singapore 25.06.2024 - 27.06.2024
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Academic lectureMenges, Daniel; Brandis, Andreas Von; Rasheed, Adil. (2024) Digital Twin of Autonomous Surface Vessels for Safe Maritime Navigation Enabled through Predictive Modeling and Reinforcement Learning. 43rd International Conference on Ocean, Offshore & Arctic Engineering 09.06.2024 - 14.06.2024
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Academic lectureMenges, Daniel. (2024) SFI AutoShip webinar: Safe Optimal Control and Multi-Target Tracking Demonstrated with a Digital Twin. SFI AutoShip webinar 03.10.2024 - 03.10.2024
2023
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LectureMenges, Daniel; Håland, Joar; Brekke, Edmund Førland. (2023) SFI Autoship Webinar on Condition Monitoring. SFI Autoship webinar 06.10.2023 - 06.10.2023
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Academic lectureMenges, Daniel; Rasheed, Adil. (2023) Digital Twin for Autonomous Surface Vessels to Generate Situational Awareness. OMAE 2023 42nd International Conference on Ocean, Offshore & Arctic Engineering , Melbourne, Australia 11.06.2023 - 16.06.2023