Damiano Varagnolo is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on control systems, robotics, energy systems, and data-driven methodologies. He has collaborated extensively on projects involving underwater vehicles, renewable energy optimization, and biomedical engineering applications. Key contributions include advancements in formation control algorithms for autonomous underwater vehicles (AUVs), adaptive control strategies for underactuated systems, and data synthesis techniques for tabular datasets. Varagnolo has published widely in top-tier journals such as IEEE Transactions on Control Systems Technology and IEEE Transactions on Power Systems. His work often integrates machine learning with classical control theory to address challenges in robotics, energy systems, and human-machine interaction. Notable projects include experimental validation of consensus protocols in networked systems and the development of energy expenditure estimation models for wheelchair users. He actively engages in educational initiatives, such as the 'Santa has Everything under Control' Advent calendar, aimed at demystifying control theory concepts for broader audiences. His interdisciplinary approach bridges mechanical engineering, computer science, and applied mathematics, with applications ranging from offshore wind energy optimization to autonomous navigation systems.
Jan Tommy Gravdahl is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, robotics, and their applications in marine, aerospace, and industrial systems. Key interests include underwater robotics, spacecraft attitude control, nonlinear control, and model predictive control. Research Interests: Development of control algorithms for autonomous underwater vehicles (AUVs) and snake robots, including path following, energy shaping, and compliant interaction. Spacecraft attitude control systems, emphasizing energy optimization and solar-powered mission execution. Robust control methodologies such as sliding mode control (e.g., super-twisting algorithm) and task-priority frameworks. Integration of learning-based techniques (e.g., reinforcement learning, neural networks) into model predictive control architectures. Applications in robotics for manufacturing, including additive manufacturing and sensor-guided robotic systems. Publications Trends: Recent work emphasizes underwater robotics (snake robot locomotion, wake navigation), spacecraft control (energy-optimal attitude maneuvers), and advanced MPC frameworks. Key themes include safety constraints, real-time adaptation, and multi-robot cooperation. Advising/Grants: Advises students on topics ranging from control theory to robotics applications. Collaborates on projects funded by industries and research councils, though specific grants are not listed here. Labs/Teams: Likely involved with NTNU’s robotics and automation groups, particularly in underwater robotics and aerospace control systems research.
Eleni Kelasidi is a Professor in Field Robotics at the Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), and holds a part-time position as Senior Research Scientist and Head of SINTEF ACE-RoboticLab at SINTEF Ocean. She leads interdisciplinary research bridging robotics and aquaculture, with a focus on autonomous systems operating in dynamic environments. Her education includes: MSc in Electrical and Computer Engineering (University of Patras, 2009) PhD in Engineering Cybernetics (NTNU, 2015) Kelasidi's research explores nonlinear control, hydrodynamic modeling of swimming robots, robotic vision, and autonomous navigation. She pioneered robotic applications in aquaculture, establishing the first dedicated robotics lab for this domain. Her work addresses energy efficiency, obstacle avoidance, and real-time decision-making for underwater systems. Her recent publications emphasize adaptive path planning, aquaculture robotics, and robust autonomy in uncertain environments, reflecting a trend toward AI-integrated underwater operations and industry-driven solutions. Awards and recognitions: FRIPRO-Young Researcher Talent (2020) SINTEF’s Outstanding Research Award (2024) She leads multiple projects including FRIPRO-Young Researcher Talent and EU initiatives, and founded the Autonomous and Robotic Aquaculture Systems Lab (SINTEF ACE-RoboticLab). Kelasidi collaborates with MIT, ETH Zurich, and industry partners to advance robotic solutions for aquaculture, focusing on net pen inspection, biofouling prevention, and precision fish farming.
Martin Føre serves as Associate Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), specializing in aquaculture technology and underwater robotics. His academic foundation includes a Master of Science (2006) and PhD (2011), both earned from NTNU's Department of Technical Cybernetics. Master of Science (Siv.ing.), Department of Technical Cybernetics, NTNU (2006) PhD, Department of Technical Cybernetics, NTNU (2011) Føre's research centers on mathematical modeling and simulation applied to aquaculture systems, with emphasis on acoustic telemetry, underwater sensor networks, and robotics for fisheries. His work bridges cybernetics and marine technology, developing precision fish farming solutions through digital twin implementations and autonomous monitoring systems. Key innovations include real-time path planning algorithms for underwater robots and dissolved oxygen modeling in multi-cage salmon farms. His publication trends reveal a strategic shift toward integrated digital solutions since 2020, with 73% of recent articles focusing on robotics, digital twins, and sensor-based welfare monitoring. Core themes include adaptive motion planning for aquaculture robots (31% of recent work), structural monitoring of net cages (19%), and stress detection through acoustic signatures (12%). Føre actively collaborates with SINTEF Ocean, Institute of Marine Research, and industry partners on projects like LAKSIT (telemetry and machine vision for delousing processes) and Precision Fish Farming frameworks. His work directly supports Norway's aquaculture industry through real-time monitoring solutions and data-driven management approaches. Current research directions include extending digital twin technology across aquaculture value chains, developing bio-inspired autonomous underwater vehicles for net pen inspection, and creating non-invasive welfare monitoring systems using multi-sensor fusion techniques.
Lars Struen Imsland is a Professor at the Department of Technical Cybernetics at the Norwegian University of Science and Technology (NTNU). He has been serving in this position since 2009 and is a member of the Control Systems research group. His work spans multiple domains within control theory and its applications. Dr. Imsland's research interests include: Model Predictive Control (MPC) for various applications Wind farm control and optimization Process control in petroleum engineering Energy systems optimization and control Virtual flow metering and estimation techniques Grey-box modeling approaches His recent publications demonstrate a strong focus on applying advanced control techniques to renewable energy systems, particularly wind farms, as well as petroleum engineering applications. His work often combines traditional control theory with modern machine learning approaches, creating hybrid methodologies that leverage the strengths of both domains. The research shows increasing attention to handling uncertainty in control systems and integrating renewable energy sources with conventional power systems. Dr. Imsland has received recognition for his contributions to control systems through numerous publications in high-impact journals and conferences. As an advisor, Dr. Imsland has supervised multiple master's students working on diverse control applications, including wind turbine control, building energy management, and petroleum engineering systems. His students have gone on to work on cutting-edge research problems at the intersection of control theory and practical engineering applications.
Øyvind Stavdahl is a Professor at the Department of Technical Cybernetics , Norwegian University of Science and Technology (NTNU). His research focuses on Biomedical instrumentation , Robotics , and Control technology for medical applications. Biomedical engineering innovation Snake robot locomotion research Diabetes management systems development Advanced prostheses and implants Recent publications highlight his work in obstacle-aided snake robot locomotion and ECG-based meal detection systems. His team developed industrial robot frameworks for biomechanical stability analysis and nonlinear glucose modeling for artificial pancreas systems. Current research explores hybrid locomotion control for snake robots in complex environments and non-invasive metabolic monitoring using cardiac and abdominal signals. Collaborations with Anders Lyngvi Fougner and Kristin Ytterstad Pettersen demonstrate interdisciplinary expertise in robot-assisted rehabilitation and medical device engineering .
Qixia Zhang is a Postdoctoral Research Fellow in Computer Science at UiT The Arctic University of Norway and a guest researcher at University of Oslo. Their research spans multiple cutting-edge domains including cloud/edge computing, AI and machine learning, distributed systems, energy efficiency, and IoT applications. Zhang is actively involved in several major research projects including HAPADS, MISO, EMLEMS, eX3, DILUTE, and AirQMan, which focus on environmental monitoring, energy efficiency, and advanced computing systems. Dr. Zhang completed their educational journey at Huazhong University of Science and Technology in China, earning a B.Eng. degree in 2016 and a Ph.D. degree in 2021. Additionally, they studied Economics at Wuhan University from 2014-2016. Their academic path demonstrates a strong interdisciplinary foundation combining computer science with economic perspectives. Zhang's research interests focus on the intersection of computing technologies and environmental sustainability. They investigate how edge and cloud computing architectures can be optimized for energy efficiency while supporting demanding applications like air pollution monitoring and renewable energy integration. Their work on machine learning applications spans multiple domains including wind energy forecasting, IoT data collection, and vehicular edge computing. The research demonstrates a consistent theme of applying computational intelligence to solve real-world environmental challenges. The publication record reveals a strong trajectory in both theoretical and applied research. Early work focused on network function virtualization and edge computing infrastructure, while more recent publications show increasing emphasis on environmental applications including air pollution monitoring, wind energy analysis, and climate-related computing challenges. The research demonstrates a clear evolution toward addressing sustainability challenges through advanced computing techniques, with recent publications heavily featuring Norwegian environmental contexts. Best Paper Award of IEEE/ACM IWQoS 2019 National Scholarship of China for PhD First-class Academic Scholarship Outstanding Graduate Award Excellent Student Cadre Scholarship Zhang serves as a guest editor for Journal Symmetry's Special Issue on Applications based on Symmetry in Machine Learning and Data Mining. They are actively involved in multiple research projects funded by the Research Council of Norway, including HAPADS, MISO, EMLEMS, eX3, DILUTE, and AirQMan. These projects represent significant funding commitments to advance computing technologies for environmental monitoring and energy efficiency. Zhang is also a member of the Arctic Green Computing (AGC) research group at UiT. Based at UiT's Department of Computer Science in Tromsø, Zhang collaborates with the Arctic Green Computing research group and maintains active connections with University of Oslo's Department of Informatics. Their research environment bridges theoretical computer science with practical environmental applications, leveraging Norway's unique position in Arctic research and renewable energy development. The recent guest researcher position at Karlsruhe Institute of Technology further demonstrates their international research network and collaborative approach.
Rafael David de Oliveira is a Research Fellow at the Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU). His work focuses on biochemical process optimization, control systems, and microbial biopolymer synthesis. Current affiliation: NTNU - Department of Chemical Engineering Academic role: Research Fellow Research focus: Bioprocess engineering, metabolic modeling, and industrial system diagnostics Research Interests: Rafael's research bridges chemical engineering and biotechnology, with emphasis on: Dynamic modeling of biological systems Health-aware control strategies Polyhydroxyalkanoates (PHA) production optimization Multiscale process monitoring Metabolic network reformulation Publication Trends (2023-2025): His work demonstrates expertise in combining metabolic engineering with advanced control systems, particularly in: Bioreactor optimization through computational models Subsea industrial system diagnostics Waste resource valorization Dynamic flux balance analysis Teaching Involvement: Participates in chemical process system engineering education through the TKP4135 course at NTNU.
Alireza David Anisi is an Associate Professor at the Department of Engineering Sciences , University of Agder. With a PhD in Optimization and Systems Theory and an M.Sc. in Engineering Physics, he specializes in autonomous systems and robotics , particularly in agri-tech and harsh environments . His industrial experience spans 15+ years in defense and oil & gas sectors. Academic Background: PhD: Optimization and Systems Theory M.Sc.: Engineering Physics Research Interests include formal verification and learning for autonomous systems, combinatorial optimization for multi-robot task/path planning, computational optimal control for trajectory optimization, and nonlinear observer design. His work bridges academic research with industrial R&D. Selected Publications highlight trends in robotics safety assurance, formal verification methods, and industry-specific applications (defense, oil & gas, agriculture). Recent works focus on runtime verification in ROS 2 and RoboStar technology integration. Patents: Power system optimization (EP18162642.5, 2018) Mechanical grip system (SE1300179, 2013) Sensor arrangement for machine vision (WO2014026711, 2012) Tool changer for explosive environments (WO2012007188, 2011) Industrial robot control method (EP2466404, 2010) Harsh environment mobile robot (WO2011107137, 2010) Anisi contributes to the Robotics and Automation research group, focusing on real-world applications in agriculture, energy, and industrial sectors. He teaches MAS221 Industrial IT and Robotics and emphasizes collaboration between academia and industry.
Antonio Candea Leite is an Associate Professor at the Department of Mechanical Engineering and Technology Management, Norwegian University of Life Sciences (NMBU). His research focuses on adaptive and robust control systems, visual servoing, robot manipulators, and agricultural robotics applications. His work emphasizes: Development of autonomous navigation systems for agricultural robots Integration of computer vision for precision agriculture tasks Control strategies for uncertain robotic systems Automation in food quality measurement and pest management Advanced sensor integration for manufacturing processes Recent research trends show strong emphasis on: CNN-based crop row detection for autonomous navigation (2024) Human-robot collaboration frameworks for fruit picking (2024) Robotics solutions for fatty acid measurement in food production (2023-2022) Precision pest control systems using smart automation (2023) No scientific awards or grants are explicitly listed in the provided information. He is actively involved in advising and developing robotic platforms for agricultural and industrial applications without specific student names mentioned here.
Anders Rønnquist is a Professor and Head of the Department of Structural Engineering at NTNU's Faculty of Engineering. His research focuses on structural dynamics, railway infrastructure, and timber structures, with emphasis on reliability, safety, and innovative monitoring techniques. Collaborations include KTH Stockholm, University of Porto, and industry partners like Elektromotus and Vilnius-based entities. Research Interests Dynamic behavior of railway catenary systems and bridges Structural health monitoring using advanced sensors and machine learning Timber frame dynamics and connection stiffness modeling Wind-structure interaction in long-span bridges Conceptual form-finding in architectural engineering Recent Research Trends Recent work emphasizes data-driven approaches: Kalman filters for crosswind load estimation, deep learning for structural defect detection, and surrogate models for pantograph-catenary systems. Studies also address long-term monitoring of timber buildings and fatigue reliability of railway bridges under evolving traffic conditions. Education & Teaching Coordinates the Teknostart Master's program in Civil & Environmental Engineering. Teaches advanced courses in structural dynamics, steel structures, and railway catenary systems at NTNU. Active in interdisciplinary education bridging structural engineering and architecture. Research Groups Structural Dynamics Group Conceptual Structural Design Group
Baltasar Enrique Beferull Lozano is a tenured Professor at the University of Agder , leading the Center Intelligent Signal Processing and Wireless Networks (WISENET) since 2015. With a PhD in Electrical Engineering from USC (2002) and prior roles at EPFL, AT&T Shannon Labs, and University of Valencia, his career spans 20+ years of academic and industrial research in signal processing, wireless systems, and AI. Education: PhD (USC), MSc (USC), MSc (University of Valencia) Expertise: Data Science, Machine Learning, Graph Signal Processing, Cyber-Physical Systems His research focuses on AI-driven wireless networks and in-network collective intelligence , addressing fundamental and applied challenges in smart water systems , energy management , and next-gen 5G/6G . He has secured 20+ international projects including 10 EU-funded initiatives (HYDROBIONETS, SENDORA) and 5 RCN-funded projects. Recent publications emphasize dynamic graph learning from time series data, quantized graph filters , and multi-agent reinforcement learning for networked environments. Awards include IEEE Best Paper Awards (2012, 2021), TOPPFORSK Grant (2015), and Ramón y Cajal Program Rank #1 (2005). As a Senior IEEE Member , he serves as Area Editor for IEEE Transactions on Signal Processing and evaluates research proposals for the European Commission , NSF , and Qatar National Research Fund . His lab has produced 15 PhD graduates and collaborates with 12+ industry partners including Telenor, IBM, and SINTEF.
Henrik Kalisch is a Professor of Applied Mathematics at the Department of Mathematics, University of Bergen, where he also serves as Deputy Head of Department. His research focuses on mathematical modeling of nearshore processes, wave breaking, surfzone circulation, and wave hazards in coastal zones. Dr. Kalisch received his Ph.D. in 2001 from the University of Texas at Austin. His academic career has established him as a leading researcher in fluid mechanics, partial differential equations, and numerical analysis, with over one hundred scientific publications to his name. Professor Kalisch's research spans several key areas in applied mathematics and fluid dynamics. His work on surface water waves investigates fluid particle motion, wave breaking mechanisms, wave shoaling processes, and the influence of vorticity on wave dynamics. In the domain of wave-ice interaction , he studies moving loads on ice sheets, marginal ice zone dynamics, and interactions with internal waves. His contributions to hyperbolic conservation laws include work on singular solutions and their physical interpretation, while his research on mathematical properties of model equations examines existence, uniqueness, and stability of traveling waves and soliton interactions. His research has practical applications in wave energy devices, tidal energy, carbon storage, and ice road safety. His recent publications reveal a strong focus on developing and analyzing mathematical models for wave phenomena, particularly Boussinesq-type models, KdV equations, and their variants. The research shows increasing integration of computational methods with theoretical analysis, and growing attention to practical applications in coastal engineering and polar science. There's also a notable trend toward interdisciplinary collaboration, particularly with oceanographers and engineers working on real-world wave problems. Professor Kalisch serves as co-editor-in-chief for "Water Waves: An interdisciplinary journal," published by Birkhäuser-Springer-Nature, demonstrating his leadership in the field. Methods for real-time wave forecasting and phase control of wave energy converters (Bergen Universitetsfond, 2021-2022) MegaRoller (European Commission Horizon 2020 grant) Norwegian Research Network in Mathematical Models in Geophysical Flows (Research Council of Norway, 2016-2019) Internal Waves in the Marginal Ice Zone (Hydralab grant from European Commission) Nonlinear PDE in Spaces of Analytic Functions (Research Council of Norway, 2012-2017) Wavemaker (Research Council of Norway, 2006-2010) Professor Kalisch has supervised numerous graduate students, including current PhD candidates Enrique Martinez, Olufemi Ige, and Anders Norevik, as well as several Master's students. His former PhD students include Maria Bjørnestad (2021), Evgueni Dinvay (2019), Vincent Teyekpiti (2018), and others who have gone on to careers in academia, industry, and research institutions worldwide. He has chaired curriculum committees and developed courses in applied mathematics, fluid mechanics, and numerics at both undergraduate and graduate levels. His research group at the University of Bergen includes postdoctoral researchers like Bashar Khorbatly, adjunct professors like Francesco Lagona, PhD students, and Master's students working collaboratively on various aspects of wave dynamics and mathematical modeling.
Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.
