Norwegian University of Science and TechnologyNorway
Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.
Per-Arne Andersen is an Associate Professor at the Department of Information and Communication Technology within the University of Agder . His research focuses on artificial intelligence , reinforcement learning , Tsetlin machines , and deep learning , with applications in real-time strategy games , industrial environments , and IoT systems . Projects: RESTORE Research Groups: CAIR - Center for Artificial Intelligence Research, CIEM - Center for Integrated Crisis Management, Intelligent Mechatronics (iTron) His work explores safe and sustainable reinforcement learning , interpretable AI , and generative environment modeling . He has developed frameworks like CaiRL and CostNet for high-performance RL environments and goal-directed learning. Recent publications include advancements in Tsetlin automaton analysis , GNSS jamming classification , and road quality detection . Articles from 2025-2016 span machine learning , computer vision , and environmental modeling . He contributes to IEEE , Springer , and LNCS publications, with a focus on interdisciplinary AI applications in crisis management , cybersecurity , and industrial optimization .
Professor Ole-Christoffer Granmo is a distinguished academic at the University of Agder, Norway, where he serves as Professor in the Department of Information and Communication Technology. He is the Founding Director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, leading cutting-edge research in artificial intelligence and machine learning. Dr. Granmo obtained his master's degree in 1999 and his PhD in 2004, both from the University of Oslo. His academic journey has been marked by significant contributions to the field of AI, most notably the creation of the Tsetlin machine in 2018, for which he received the AI research paper of the decade award from the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Professor Granmo's research primarily focuses on logical and causal world modeling across multiple modalities including images, sound, and natural language. His work spans logical auto-encoding, convolution, regression, transformer architectures, and reinforcement learning, all with the overarching goal of creating ultra-low-power artificial general intelligence through transparent logical learning and reasoning. His publications reveal a strong emphasis on interpretable AI systems, hardware implementations, and applications across diverse domains including cybersecurity, healthcare, social media analysis, and bioinformatics. AI Research Paper of the Decade (2022) - Norwegian Artificial Intelligence Consortium (NORA) Eight paper awards in machine learning Professor Granmo has coordinated over seven research projects and mentored 55+ master's students and nine PhD students. His leadership extends to co-founding the Norwegian Artificial Intelligence Consortium (NORA) and establishing two companies: Anzyz Technologies AS and Tsense Intelligent Healthcare AS. As an advisor at Literal Labs, he actively bridges academic research with practical industry applications, demonstrating his commitment to translating theoretical innovations into real-world solutions that address complex challenges across multiple sectors.
Norwegian University of Science and TechnologyNorway
Professor Adil Rasheed is affiliated with the Department of Engineering Cybernetics at the Faculty of Information Technology and Electrical Engineering , Norwegian University of Science and Technology (NTNU). His work focuses on integrating data-driven methods with physics-based modeling to create reliable hybrid systems for high-stakes applications. Research Interests : Bigdata Cybernetics, Hybrid Analytics / Modeling, Artificial Intelligence, Reduced Order Modeling, Computational Fluid Dynamics, Wind Energy, Autonomous Vessels, and Safe Reinforcement Learning. Digital Twin Applications : Professor Rasheed leads projects in Digital Twin technology for wind energy and smart greenhouses. His work includes autonomous marine navigation, federated learning for Industrial IoT, and predictive maintenance in offshore wind turbines using integrated data-driven models. Collaborative Efforts : He collaborates with industry partners on digital twin syncing for autonomous vessels, thermal zoning algorithms for building control, and anomaly detection in multivariate time series. His publications highlight the use of transformers, federated transfer learning, and corrective source terms in hybrid modeling.
Norwegian University of Science And TechnologyNorway
Roger Skjetne is a Professor in Marine Control Engineering at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Marine Technology under the Faculty of Engineering. His research focuses on autonomous ships, dynamic positioning systems, energy and power management for hybrid-electric vessels, Arctic stationkeeping, and ice management systems. He leads the SFI Autoship research center, which aims to advance autonomous ship technology for safe and sustainable operations in Arctic and maritime environments. Key projects include DigitalSeaIce (multiscale sea ice observation), ENDURE (safety solutions for autonomous ships), and ZEVS (zero-emission passenger vessel systems). His work integrates control systems, robotics, and environmental monitoring to address challenges in marine autonomy and energy efficiency. Skjetne’s research has produced over 150 publications since 2020, with a focus on hybrid control barrier functions, energy optimization, and autonomous navigation. He collaborates with industry partners to translate academic findings into practical maritime solutions.
Norwegian University of Science And TechnologyNorway
Adil Rasheed is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. His research focuses on digital twin technology, artificial intelligence (AI), machine learning (ML), physics-based modeling, and hybrid analysis methodologies. He leads projects such as the work package on Digital Twin and Asset Management, collaborating with major industry partners to advance digital twin capabilities. His interdisciplinary work bridges AI-driven approaches with traditional physics-based models for applications in wind energy, autonomous vessels, smart greenhouses, and aquaculture. Notable contributions include developing a VR-enabled smart greenhouse digital twin and the PoroTwin framework for porous media flow analysis. His recent publications span topics like safe marine navigation using reinforcement learning, federated learning for industrial IoT anomaly detection, and predictive maintenance in offshore wind turbines. Rasheed actively engages in academic outreach and serves as an advisor to research initiatives in hybrid modeling and autonomous systems. Research Interests Creation and application of digital twins for physical systems optimization Integration of AI/ML with physics-based models for hybrid systems Autonomous systems navigation and safety (vessels, drones) Wind energy systems and offshore renewable energy Condition monitoring and predictive maintenance Data-driven solutions for aquaculture and urban mobility challenges Key Projects Smart Greenhouse: AI-driven digital twin for autonomous plant growth monitoring via VR PoroTwin: Digital twin for porous media flow analysis in oil and gas Hybrid Analysis and Modeling (HAM) framework combining knowledge-based and data-driven methods NorthWind Project: Digital twin advancements for wind energy systems Publications Trends Rasheed's recent work emphasizes digital twin applications in energy systems (wind turbines), autonomous maritime navigation, and industrial IoT security. He explores federated learning techniques for decentralized anomaly detection and integrates reinforcement learning for safety-critical control systems. Collaborations with industry partners highlight practical implementations in offshore infrastructure, aquaculture, and urban mobility. Grants & Partnerships Active collaborations with industry leaders in renewable energy, maritime robotics, and smart agriculture ensure his research addresses real-world challenges. His work is supported by interdisciplinary projects combining academia-industry expertise. Labs & Teams Leads the Digital Twin and Asset Management team at NTNU, coordinating with research groups in computational engineering, autonomous systems, and renewable energy technology.
Norwegian University of Science And TechnologyNorway
Martin Pierre Francois Jacquet is a Research Fellow at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His work focuses on advanced robotics, specifically in collision avoidance systems and autonomous navigation using deep learning and control theory. He has contributed to conferences such as IEEE ICRA and IROS, presenting research on neural control barrier functions and model predictive control (MPC) frameworks. Research interests include integrating deep neural networks with traditional control methods to enhance safety and resilience in autonomous systems. His publications emphasize real-world applications of robotics, particularly in environments requiring dynamic obstacle avoidance. Recent work explores modular deep learning architectures for aerial robot navigation. No scientific awards are explicitly mentioned in the provided text. His professional activities include academic lectures and poster presentations at major robotics conferences, such as the European Robotics Forum and IEEE/RSJ IROS. No advisory or grant information is available in the current data.
Dan Sui is a Professor in the Department of Energy and Petroleum Engineering at the University of Stavanger, within the Faculty of Science and Technology. His research is centered on drilling automation, digitalization, artificial intelligence, machine learning, data analytics, modeling, optimization, and control systems in petroleum and geothermal energy contexts. His research interests span drilling automation, AI, machine learning, data processing, modeling, optimization, simulation, control system design (including model predictive control, PID, Kalman filters), advanced drilling technologies, drilling event detection, geothermal drilling, and digital twin development. He actively contributes to the development of smart drilling systems and data-driven models for real-time decision support. The recent publications (2020–2025) highlight a strong trend in applying reinforcement learning, deep learning, and data-driven modeling to drilling optimization, ROP prediction, well path design, and subsea control. These works are published in high-impact journals such as SPE Journal , Journal of Petroleum Science and Engineering , and Applied Sciences , as well as in proceedings from ASME and IADC/SPE conferences, indicating a strong presence in both petroleum and mechanical engineering domains. Automatic calibration of directional drilling control Multi-agent reinforcement learning for waterflooding PI controller tuning using Deep Q-Learning Safe operating envelope for directional drilling Neural network optimization for ROP prediction Real-time ROP trend analysis Well path optimization with Bezier curves Anti-collision trajectory design Automated drilling algorithms on lab rigs Subsea shuttle tanker depth control While no specific scientific awards are listed, his extensive publication record and involvement in AI and digital twin projects reflect significant recognition in the field. He advises students and collaborates on research involving laboratory-scale drilling automation systems, hybrid test environments, and smart drilling robots, contributing to both theoretical and applied advancements. His work includes development of algorithms for autonomous drilling agents, feature selection for kick detection, and experimental studies on drillstring dynamics. Dan Sui is a key contributor to the OpenLab project, a modern drilling digitalization infrastructure, and leads research in data quality improvement, downhole data correction, and sensor data reconstruction using recurrent neural networks. His lab-based work includes designing autonomous small-scale drilling rigs and testing machine learning algorithms for incident detection, showcasing a strong integration of experimental and computational research.
Anne Håkansson is a Professor at the Department of Informatics , UiT The Arctic University of Norway . Her research bridges Artificial Intelligence and Cyber-Physical Systems with applications in mHealth and Smart Energy Systems . Current research focuses on proactive health promotion via digital twins and wearables. Explores robust reasoning in autonomous systems and smart nudging for behavioral change. Recent work includes AI for battery digitalization and multi-agent collision avoidance in dynamic environments. She contributes to Springer and Procedia Computer Science publications, with emphasis on context-aware AI and sustainable technologies . Active in the Open Distributed Systems (ODS) and Computational Analytics and Intelligence (CAI) research groups, and involved in projects like Better Balance in Informatics (BBI) and the Nudge Project .
Norwegian University of Science And TechnologyNorway
Prof. Sebastien Nicolas Gros is the Head of the Department of Engineering Cybernetics at NTNU. His research focuses on safe Reinforcement Learning, Data-Driven Model Predictive Control (MPC), and energy systems optimization. He collaborates with major industries like Equinor, DNV, Kongsberg, and CorPower Ocean, alongside academic partners such as SINTEF. He supervises 6 PhD students and co-supervises projects in artificial pancreas systems, multi-rotor wind turbine control, and industrial applications with Volvo. His work emphasizes AI-driven solutions for energy management in buildings, domestic battery systems, and indoor farming. Notable projects include MAIDOM (smart home energy control) and wave energy converter optimization with CorPower Ocean. He actively engages students through MSc projects involving data-driven control, smart home integration, and hydroponics automation. Prof. Gros leads a research group addressing interdisciplinary challenges in energy transition and cybernetic systems. His expertise spans numerical optimization, renewable energy integration, and industrial control systems.
Norwegian University of Science and TechnologyNorway
Professor Houxiang Zhang holds a faculty position at the Norwegian University of Science and Technology (NTNU), serving as a Professor in Mechatronics and Deputy Research Leader (Nestleder forskning) at the Department of Ocean Operations and Civil Engineering within the Faculty of Engineering. He joined NTNU in 2011 after completing a Habilitation in Informatics at the University of Hamburg (2011). His research focuses on biological robotics, modular robotics, virtual prototyping, and maritime mechatronics, with over 300 publications and multiple best-paper awards. Academic memberships include the Academy of the Royal Norwegian Society of Sciences and Letters (DKNVS), Norwegian Academy of Technological Sciences (NTVA), and IEEE Senior Member. He has led significant projects such as the EU Horizon-RIA Project "Robotic Safe Adaptation in Unprecedented Situations" and the NFR Research Infrastructure Program "The Digital Ocean Space-Møre Ocean Lab." Research interests span marine automation, AI applications, and hybrid modeling. Notable achievements include pioneering work in digital twin technology for maritime systems and contributions to offshore mechatronics. His lab, the Intelligent Systems Lab, focuses on integrating advanced technologies for marine operations and automation. Recent awards include the 2024 Best Paper Award from IEEE RAS and multiple finalist recognitions at robotics and automation conferences. Current research emphasizes autonomous ship systems, environmental modeling, and data-driven decision support for maritime safety and efficiency.
Norwegian University of Science and TechnologyNorway
Konstantinos Alexis is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). He leads the Autonomous Robots Lab and serves as Principal Investigator for major international projects including the DARPA Subterranean Challenge. His research focuses on developing resilient autonomous systems capable of operating in extreme environments through resourcefulness, robustness, and redundancy. His research interests span resilient robotic autonomy with emphasis on aerial robotics , underwater robotics , robot control , path planning , robot learning , and Simultaneous Localization and Mapping (SLAM) . He takes a holistic approach across these disciplines to enable autonomous systems to navigate challenging environments including subterranean spaces, underwater operations, and extreme terrestrial conditions. Recent work demonstrates significant advances in collision-tolerant navigation, degradation-resilient state estimation, and semantic-aware inspection planning. Professor Alexis has secured substantial funding from diverse sources including US agencies (DARPA, NSF, DOE, USDA), EU Horizon programs, and the Research Council of Norway. His research portfolio includes field deployments in nuclear environments, aquaculture operations, and planetary exploration scenarios. Principal Investigator for DARPA Subterranean Challenge Major grants from NSF, DOE, USDA, and EU Horizon programs Research Council of Norway funding for multiple projects He actively supervises numerous PhD and Master's students, with recent graduates leading publications in top robotics venues. His lab maintains strong collaborations with international research groups and industry partners for real-world deployment of autonomous systems. Current initiatives include the ResiFarm project for underwater operations in fish farms and advanced exploration systems for Martian lava tube environments.
Norwegian University of Science and TechnologyNorway
Mihir Vinay Kulkarni is a Researcher at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), specializing in autonomous aerial systems for complex environments. His work bridges simulation frameworks and real-world deployment in industrial and hazardous settings. His research focuses on: Developing vision-based navigation for cluttered environments Creating parallel simulation tools (Aerial Gym) for accelerated robotics development Implementing deep reinforcement learning for collision avoidance Deploying aerial robots in ship ballast tanks and subterranean spaces Publications since 2022 demonstrate consistent output in top robotics venues, with recent work emphasizing semantic path planning and neural safety frameworks. His research trajectory shows strong alignment with NTNU's field robotics initiatives and international collaborations, particularly with ETH Zurich. As a recent PhD graduate (2025), Kulkarni represents an emerging researcher with significant potential in aerial robotics. Prospective students should note his focus on practical applications and simulation-to-reality transfer, though supervision capacity may be limited during this early career phase.
