- Eirik Runde Barlaug and Jørgen Lind Fløystad, Best Masters thesis in Norsk Forening for Elektro og Automatisering, 2024
- Eirik Runde Barlaug and Jørgen Lind Fløystad, Best Masters thesis award, OpenAI Lab 2023
- Aksel Vaaler and Svein Jostein Husa, Best Masters thesis in Norsk Forening for Elektro og Automatisering, 2023
- Aksel Vaaler and Svein Jostein Husa, Best Masters thesis award, OpenAI Lab 2023
- Anne Eiken, Best Master Thesis award from Bane NOR, 2022
- Elias Elfarri, Second best project idea from Tekna, 2022
- Sindre Stene Blakseth, Best Masters thesis award, OpenAI Lab 2021
- Eivind Meyer, Best Masters thesis award, Open AI Lab 2020
- Haakon Robinson, Best Masters thesis award, Open AI Lab, 2019
- Duy Tan Tran, Best Poster Award, Deep Wind 2019
Masters stuents
M1 Oscar Raavik, 2025
M2 Olav Lill Østerholt, 2025
M3 Oussama Elbettal
M4 Tsvetelin Angelov Petrov
M5 Olea Linnea Andersson, 2025
M6 Andreas Von Brandis, Multi-Target Tracking for Autonomous Surface Vessels – Fusing LiDAR and AIS Data in a Digital Twin Framework, Journal article in Applied Ocean Research, 2024
M7 Eirik Runde Barlaug, Reactive Quadrotor Guidance System Using Deep Reinforcement Learning, Autoencoders and Nonlinear Control, Journal article under review and OMAE paper, NFEA Award, OpenAI Award, 2024
M8 Henrik Stokland Berg, Deep Reinforcement Learning and Nonlinear Model Predictive Control in Marine Navigation, 2024, Journal article under review
M9 Jørgen Lind Fløystad, Reactive Quadrotor Guidance System Using Deep Reinforcement Learning, Autoencoders and Nonlinear Control, Journal article under review and OMAE paper, NFEA Award, OpenAI Award, 2024
M10 Tobias Rotnes Aasen, Optimizing Nonlinear Dynamic Models with Deep Active Learning and Min- imal System Interference, 2024
M11 Elias Buø, Reachability Analysis of Deep Active Learning for Nonlinear System Identification2024
M12 Magnus Selle, The Digital Twin-Ready Aquarium: A Step Towards Industry 4.0 in Aquaculture, 2024
M13 Magnus Haaker, 2024
M14 Shayan Tafrishi, The Future of Correctional Workforce Management: AI and Societal Cybernetics to the Rescue, 2024
M15 Pernille Sofie Pedersen, Statistical and Mathematical Modeling of Supermarket Cabinet Temperature for Demand Response Applications, 2024
M16 Kristoffer Arlind, 2024
M17 Håvard Einarssønn Høymork, Drone-Assisted Temperature Monitoring for Sustainable Control of Residential Climates, 2024
M18 Marte Eggen, Explainable AI Approaches for Large Generative Transformer-Based Language Mod- els, 2024
M19 Hedda Nielsen Dale, 2024
M20 Albert Johannessen, Physics informed neural network, 2022
M21 Jacob Wulff Wold, GAN Based Super-Resolution of Near-Surface 3D Atmospheric Wind Flow with Physics Informed Loss Function, 2023, Journal article in Engineering Applications of Arti- ficial Intelligence
M22 Henrik Albin Larsson Hestnes, Machine Learning-Enabled Predictive Modeling of Building Perfor- mance for Electricity Optimization, 2023
M23 Aksel Vaaler, Safe Reinforcement Learning in Marine Navigation and Control: Using a Predictive Safety Filter for Safety Verification on Autonomous Surface Vessels, 2023, Journal article in Artificial Intelligence and OMAE paper, NFEA Award, OpenAI Award
M24 Kristoffer Skare, Unleashing the Potential of AI-Driven Digital Twins A Framework for Research using a Sensor-Enhanced Greenhouse, 2023, Conference paper
M25 Endre Bruaset, Unleashing the Potential of AI-Driven Digital Twins A Framework for Research using a Sensor-Enhanced Greenhouse, 2023, Conference paper
M26 Kristian William Macdonald Gulaker, Object detection in EM2040 point clouds
M27 Eivind Dogger, Multi-agent reinforcement learning with graph neural networks for optimizing an industrial sorting system
M28 Emil Johannesen Haugstvedt, On the Potential of Utilizing Laboratory-Scale Experimental Setup as Proxy For Real-Life Applications: Time Series Analysis and Prediction for Hole Cleaning, 2023, Journal article published in IEEE Access
M29 Eirik Rugaard Furevik, Physics-guided neural networks for aerodynamic characterization of wind turbines
M30 Henrik Andreas Gusdal Wassertheurer, Developing a predictive digital twin of a wind farm , 2023
M31 Ørjan Carlsen, Merging Classical Control and Deep Reinforcement Learning for Dynamic Collision Avoidance for a Quadcopter, 2023
M32 Svein Jostein Husa, Safe Reinforcement Learning in Marine Navigation and Control: Using a Pre- dictive Safety Filter for Safety Verification on Autonomous Surface Vessels, 2023, Journal article in Artificial Intelligence
M33 Jannani JohanRaj, Improving Credit Management Practices: A Transdisciplinary Approach to Op- timizing Risk and Profitability, 2023
M34 Marthe Aaberg, Improving Credit Management Practices: A Transdisciplinary Approach to Opti- mizing Risk and Profitability, 2023
M35 Kristian Brudeli, Path-following and Collision Avoidance using World Models, 2023
M36 Sondre Sorbø, Corrective Source Term Approach for improving Erroneous Physics-Based Models, 2022, Journal article in Applied Soft Computing
M37 Simon Mork Sætre, Laying The Foundation For an Artificial Intelligence-Powered Extendable Digital Twin Framework For Autonomous Sea Vessels, 2022, OMAE Conference paper
M38 Marcus Skagemo, Stacking classifiers for improved order execution
M39 Ludvig Løken Sundøen, Path Following for Quadcopters using Deep Reinforcement Learning
M40 Lars Gjardar Musæus, Fractal Analysis and Its Application on Time-Series Data – An Innovative Method for Condition Monitoring of Hole Cleaning Operations, 2022
M41 Elias Mohammed Elfarri, Digital Twin of a Building Powered by Artificial Intelligence and Demon- strated in Virtual Reality, Tekna Award for project idea
M42 Annfrid Hopland Myklebust, Building a digital twin of the thermodynamic behaviour of a building using hybrid modeling, 2022
M43 Anne Willkommen Eiken, Position Alignment and Geographical Location Determination of Railway Track Condition Monitoring Data, Best Master’s thesis award from BaneNor
M44 Katarina Charlotte Guderud, Predicting feeding patterns in aquaculture
M45 Viljer Ness, Simulating Ordinary Differential Equations using the Physics-Guided Machine Learning Framework, 2021
M46 Vebjørn Malmin, Reinforcement Learning and Predictive Safety Filtering for Floating Offshore Wind Turbine Control, 2021
M47 Andrine Elsetrønning, Generalized workflow with uncertainty quantification for detecting abnormal- ities in lung sounds, 2021
M48 Julia Marie Graham, Geometric change detection in the context of Digital Twin, leveraging Dynamic Mode Decomposition, Object Detection and innovations in 3D technology, Journal article in Digital
M49 Marie Skatvedt, Sea bottom detection using Doppler Velocity Logger, 2021
M50 Torkel Laache, Physics Guided Machine Learning: Injecting neural networks with simplified theories, 2021, Journal article in Frontiers in Robotics and AI
M51 Halvor Ødegard Teigen, Reinforcement Learning and Predictive Safety Filtering for Floating Off- shore Wind Turbine Control, 2021, Journal article in Frontiers in Robotics and AI
M52 Ole-Jørgen Hannestad, Securing trust in AI systems through increased explainability: linking Nor- wegian organizations’ challenges in regards to eXplainable Artificial Intelligence (XAI) with a 2021 view on relevant techniques and methods, 2021
M53 Bendik Austnes, Increasing Validity and Uncovering Utility in Machine Learning Studies – An Il- lustrative Approach to Essential Concepts and Procedures in Model Development and Assessment, 2021
M54 Olav Landmark Pedersen, A proof-of-concept Digital Twin implementation for monitoring patients through the Clinical Pathway for Prostate Cancer in the Norwegian Health and Care Service, 2021
M55 Fredrik Pettersen, Making a digital twin of a heterogeneous rod under transient heat transfer
M56 Sindre Stenen Blakseth, Introducing CoSTA: A Deep Neural Network Enabled Approach to Improv- ing Physics-Based Numerical Simulations, 2021, Best Masters thesis award from the Norwe- gian Open AI Lab, 2 Journal articles in Neural Networks and Applied Soft Computing
M57 Tiril Sundby, Towards Geometric Change Detection in Digital Twins using Dynamic Mode Decom- position, Object Detection and 3D Machine Learning, 2020, Journal article in Digital
M58 Daniel Nakken, A strategy controller for concave obstacle avoidance, 2020
M59 Thomas Nakken, On the applicability of a perceptually driven generative-adversarial framework for super-resolution of wind fields in complex terrain, 2020
M60 Eivind Meyer, On Course Towards Model-Free Guidance: A Self-Learning Approach To Dynamic Collision Avoidance for Autonomous Surface Vehicles, 2020, Best Masters thesis award from the Norwegian Open AI Lab, 2 Journal Articles in IEEE Access
M61 Eirik E. Vesterkjær, Combining grid-based uncertainty propagation and neural networks with un- certainty estimation, 2020
M62 Herman Stavelin, Biomass estimation using sonar and machine learning, 2020, Journal Article in Ecological Informatics
M63 Duy Tan Tran, Convolutional Neural Network and Generative Adversarial Networks Enabled Res- olution Enhancement of Numerical Simulations, 2020, Best poster award at the Deep Wind Conference
M64 Simen Theie Havenstrom, From Beginner to Expert: Deep Reinforcement Learning Controller for 3D Path Following and Collision Avoidance by Autonomous Underwater Vehicles, 2019-2020, Journal Article in Frontiers in Robotics and AI
M65 Amalie Heiberg, Risk-based reinforcement learning for path following and collision avoidance, 2019- 2020, Journal Article in Neural Networks
M66 Haakon Robinson, On the Piecewise Affine Representation of Neural Networks, 2019, Runner-up best Masters thesis award from the Norwegian Open AI Lab
Specialization project students
SP1 Kristine Stabell, Trajectorial risk assessment of autonomous surface vessels using AIS data, Bayesian networks, and machine learning, 2023
SP2 Andreas Von Brandis, Introducing predictive capabilities for an Autonomous Surface Vessel in a Digital Twin framework, 2023
SP3 Eirik Runde Barlaug, Low-Dimensional Latent Encodings for Enhanced Reinforcement Learning- Based Collision Avoidance, 2023
SP4 Henrik Stokland Berg, CNN-based situational awareness in marine applications; neural network search, 2023
SP5 Jørgen Lind Fløystad, Bi-Rotor Drone Doing Path Following and Collision Avoidance in the Vertical Plane Using Deep Reinforcement Learning, 2023
SP6 Vegard Bergum Hovland, Autonomous data sampling with a quadrotor drone using a digital twin of a smart house, 2023
SP7 Tobias Rotnes Aasen, General Deep Active Learning Framework for Nonlinear System Identification, 2023
SP8 Elias Buø, Nonlinear System Identification of Maneuvering Model using Deep Active Learning, 2023
SP9 Magnus Selle, Digital Twin-ready Aquarium, 2023
SP10 Magnus Haaker, Unveiling Trends and Challenges in Modern Logistics, 2023
SP11 Olea Linnea Andersson, Samfunnskybernetikk: Measuring the organizational health in companies from different industries using cybernetics analytics, neuroscience, psychology, etc.
SP12 Shayan Tafrishi, Samfunnskybernetikk: Measuring the organizational health in companies from different industries using cybernetics analytics, neuroscience, psychology, etc.
SP13 Pernille Sofie Pedersen, Ambient Temperature-Based Predictive Modeling of Energy Consumption for Standard Operations of a CO2 Cooling System in Porsgrunn, Norway, 2023
SP14 Kristoffer Arlind, The Use of Market Paradigm Adaptive Machine Learning Models for Short Term Stock Return Prediction
SP15 Gjermund Bae, Digital Twins of Listed Companies’ to Accelerate the Net Zero Transition
SP16 Håvard Einarssønn Høymork, Autonomous Temperature Monitoring in a Dense Environment using a Micro Aerial Vehicle, 2023
SP17 Marte Eggen, Explainable AI on transformer models (HUNT dataset)
SP18 Hedda Nielsen Dale, Identifying Pain Points in the Industrial Value Chain: A Mixed-Methods Analysis
SP19 Albert Johannessen, Physics informed neural network, 2022
SP20 Eivind Dogger, Reinforcement learning for efficient control of parcels in an automated logistics system, 2022
SP21 Aksel Vaaler, Safe learning of small passenger ship, 2022
SP22 Emil Johannesen, Corrective Source Term with Sparse Neural Networks, 2022
SP23 Endre Bruaset, Experimental setup for discovering a dynamical model of plant growth, 2022
SP24 Erik Rugaard Furevik, Developing a wind forecast system for predictive digital twins, 2022
SP25 Hannah Hansen, CNN-based situational awareness and risk estimation using LiDAR perception in marine applications, 2022
SP26 Henrik Albin Larsson Hestnes, Digital Twin for Built Environment, 2022
SP27 Henrik Andreas Gusdal Wassertheurer, Developing a wind forecast system for predictive digital twins, 2022
SP28 Jannani Johanraj, Digital twin for business processes, 2022
SP29 Kristoffer Skare, Numerical setup for discovering a dynamical model of plant growth, 2022
SP30 Marte Aaberge, Digital twin for business processes. 2022
SP31 Ørjan Carlsen, Adversarial Reinforcement Learning (“Trial-by-Fire”), 2022
SP32 Svein Jostein Husa, Approximate MPC control of neural network dynamics, 2022
SP33 Sondre Sorbø, Physics Guided Neural Network-assisted Corrective Source Term Approach to Hybrid Analysis and Modeling, 2021
SP34 Simon Mork Sætre, Machine Learning in Unity, 2021
SP35 Marcus Skagemo, Improved market entry of long-term time horizon trading signals using short-term residual reversal, 2021
SP36 Ludvig Løken Sundøen, Path Following for Quadcopters using Deep Reinforcement Learning, 2021
SP37 Lars Gjardar Musæus, Railway Track Condition Monitoring and Data-Driven Predictive Main- tainance, 2021
SP38 Elias Mohammed Elfarri, Digital Twin of Smart Housing: An Initial Setup of a Digital Twin Using The Capability Levels Framework, 2021
SP39 Annfrid Hopland Myklebust, Building a digital twin of the thermodynamic behaviour of a building using hybrid modeling, 2021
SP40 Anne Willkommen Eiken, Analysis of alignment methods for railway track geometry measurements, 2021
SP41 Katarina Charlotte Guderud, Predicting feeding patterns in aquaculture
SP42 Daniel Vennestrøm, Industry 4.0 digital twin for mobile robots operating in energy industry facilities, 2021
SP43 Hanna Backer Malm, Digital Twin for Enterprises, 2020
SP44 Viljer Ness, Digital Twin for Enterprises, 2020
SP45 Vebjørn Malmin, Model Predictive Control using Deep Neural Network, 2020
SP46 Andrine Elsetrønning, Machine Learning based anomaly detection in lung sound data, 2020
SP47 Julia Marie Graham, Combining Dynamic Mode Decomposition and Compressed Sensing for intru- sion detection, 2020
SP48 Marie Skatvedt, Sea bottom detection using Doppler Velocity Logger, 2020
SP49 Torkel Laache, Reinforcement learning for path following and collision avoidance under the influence of wind and current, 2020
SP50 Halvor Ødegard Teigen, Reinforcement learning for path following and collision avoidance in 3-D, 2020
SP51 Ole-Jørgen Hannestad, Explainable artificial inelligence for bussinesses, 2020
SP52 Raja Iqran Iftikar, Bead classification in developing COVID-19 kit, 2020
SP53 Bendik Austnes, Analysis of hyperspectral images for detecting skin disease, 2020
SP54 Olav Landmark Pedersen, 2020
SP55 Fredrik Pettersen, Making a digital twin of a heterogenous rod under transient heat transfer, 2020
SP56 Sindre Stenen Blakseth, Hybrid Analysis and Modeling, 2020
SP57 Kari Moe, GANS assisted design of prosthetic arms for third world amputees, 2020
SP58 Eivind Meyer, Path Following and Collision Avoidance for Surface Vessel using Reinforcement Learn- ing, 2019
SP59 Eirik E. Vesterkjær, Uncertainty Propagation and Applications to Neural Networks, 2019
SP60 Herman Stavelin, Object Detection Applied to Marine Data for Species Classification and Biomass Estimation, 2019
SP61 Duy Tan Tran, Generative Adversarial Networks assisted super-resolution simulation of atmospheric flows in complex terrain, 2019
SP62 Simen Theie Havenstrom, 3D Path Following and Motion Control for Autonomous Underwater Vehicles Using Deep Reinforcement Learning, 2019
SP63 Haakon Robinson, Reinforcement Learning based controllers for autonomous ships (path following with collision avoidance), 2018
SP64 Camilla Sterud, Reinforcement Learning based controllers for underwater vehicles (path following with collision avoidance), 2018
SP65 Daniel Nakken, Machine Learning Controllers for Robotic Manipulator, 2018