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Ronny SchererView profile

Professor

• Meta-analysis
• Educational Measurement
• Digital Divides
• +6 more

Ronny Scherer is Center Director and Professor at CEMO (Center for Educational Measurement) and Deputy Director at CREATE (Center for Research on Equality in Education) at the University of Oslo's Faculty of Educational Sciences. His work bridges educational measurement, assessment, and evaluation with a focus on research syntheses and complex sampling surveys. Dr. Scherer's research spans two interconnected domains: substantive areas including digital divides, equity and equality in education, and measurement of complex cognitive skills (such as complex problem solving, adaptability, computational thinking, and executive functioning); and methodological areas focusing on advanced meta-analytic techniques, multilevel structural equation modeling, and spatial analysis of complex survey data. His work frequently utilizes international large-scale assessment data from PISA, ICILS, TIMSS, PIRLS, PIAAC, and TALIS. His publication record demonstrates a clear trajectory toward increasingly sophisticated meta-analytic approaches, with recent work focusing on second-order meta-analyses, AI-assisted screening methods, and advanced techniques for handling complex survey data. His research consistently addresses critical educational challenges related to equity, digital literacy, and measurement of 21st century skills. Dr. Scherer has secured significant research funding for projects including ARISE (Academic resilience in mathematics and science among vulnerable students), DiDiRes (Digital inequalities in education), and ADAPT21 (Educational assessments of the 21st century: Measuring and understanding students' adaptability in complex problem solving situations). Co-director of CREATE (Centre for Research on Equality in Education) since 2023 Professor of Educational Assessment and Measurement at CEMO since 2019 Extensive experience with international large-scale assessments including ICILS, TALIS, and PIAAC As an educator, Dr. Scherer teaches advanced courses in measurement models, multilevel models, meta-analysis, and equity in education. He actively supervises graduate students interested in his research areas and has developed numerous workshops on structural equation modeling and meta-analytic methods for international audiences.

Ali Ramezani-KebryaView profile

Associate Professor

• Machine Learning
• Deep Learning
• Federated Learning
• +9 more

Ali Ramezani-Kebrya is an Associate Professor with tenure in the Department of Informatics at the University of Oslo (UiO), where he leads research in machine learning theory. He holds dual Principal Investigator roles at the Norwegian Center for Knowledge-driven Machine Learning (Integreat) and SFI Visual Intelligence, and is an active member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Society. His service includes Area Chair positions for NeurIPS and AISTATS, and Action Editor for Transactions on Machine Learning Research. His research focuses on theoretical foundations of deep learning with emphasis on understanding input data distribution encoding in neural network layers. Key themes include minimizing statistical risk under resource constraints, addressing distribution shifts in distributed settings, and developing practical tools for robust federated learning. Current applications span emotion recognition, marine data analysis, and neuroscience, reflecting his commitment to real-world machine learning challenges as evidenced by his FRIPRO-funded Machine Learning in Real World (MLReal) project. Recent publication trends reveal three dominant threads: (1) label/covariate shift mitigation in distributed systems through entropy regularization and density ratio estimation; (2) communication-efficient optimization via layer-wise quantization and adaptive compression techniques achieving 150% speedups; and (3) robustness guarantees against tailored attacks and distribution shifts. These works consistently bridge theoretical bounds with empirical validation across domains from GAN training to federated settings. Scientific recognition includes: FRIPRO Grant for Early Career Scientists (2025) for MLReal project SFI Visual Intelligence Spotlight Publication award (2023) for federated learning work He actively mentors 11 graduate students across Oslo and Tromsø universities, with recent PhD placements at Apple and NVIDIA. Current grant portfolio features the FRIPRO Early Career award and leadership roles in two major Norwegian research centers. His lab maintains strong industry collaborations through Vector Institute and EPFL, with recent hiring for PhD and postdoc positions in physics-informed machine learning.

• Optimal Control
• Power Electronics
• Wireless Power Transfer
• +3 more

Zeinab Karami Hassan Abadi is a PhD candidate at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), under Professors Jon Are Wold Suul and Marta Molinas. Her research focuses on Control of Wireless Power Transfer Systems for Public Transport Applications, supported by SINTEF Energi's collaboration with Dr. Giuseppe Guidi. Academic Background: 2015-2021: Research Associate at University of Kurdistan, Sanandaj, Iran. 2015-2017: M.Sc. in Power Systems Engineering (University of Kurdistan). 2011-2015: B.Sc. in Electrical Engineering (University of Kurdistan). Research Interests span Optimal Control of Power Converters, Model Predictive and Robust Control, Wireless Power Transfer Systems, and Microgrid Dynamics. She specializes in advanced control strategies for power electronics in renewable energy and EV charging applications. Her work emphasizes stability and efficiency in power systems, with contributions to Model Predictive Control (MPC) for DC microgrids and inductive power transfer. Notable collaborations include projects with SINTEF Energi, focusing on practical implementations of control algorithms in real-world systems. Labs/Teams: Active in NTNU’s Power Electronics and Drives group, collaborating with SINTEF on applied power systems research.

Johannes SkaarView profile

Professor

• Quantum Field Theory
• Quantum Optics
• Electromagnetics
• +3 more

Johannes Skaar is a Professor at the Department of Physics, University of Oslo (UiO). He holds a 100% position there since 2017, previously at NTNU. His research focuses on quantum field theory, quantum optics, electromagnetics, metamaterials, photonics, and quantum information. He teaches advanced courses such as FYS4170 Relativistic Quantum Field Theory and FYS1005 Classical Mechanics. His work spans theoretical physics with notable contributions to single-photon states, metamaterial properties, and quantum cryptography security. Skaar’s research integrates foundational physics with applied technologies like metamaterials and quantum communication systems. His studies on Fresnel equations and magnetic permeability have advanced electromagnetic theory. He frequently publishes in top journals like Physical Review A and Physical Review Letters . Research groups: Theoretical Physics at UiO.

Ann-Cecilie LarsenView profile

Professor

• Nuclear Physics
• Nuclear Astrophysics
• Quantum Mechanics
• +2 more

Ann-Cecilie Larsen is a Professor in Nuclear and Energy Physics at the University of Oslo, leading research in nuclear physics and astrophysics. She works with the Oslo Cyclotron Laboratory (OCL) and collaborates with institutions like Université libre de Bruxelles, Lawrence Livermore National Laboratory, and CERN's ISOLDE facility. Master of Science in Physics, University of Oslo (2002) Cand.scient. in Nuclear Physics, University of Oslo (2004) Ph.D. in Nuclear Physics, University of Oslo (2008) Her research focuses on nuclear properties at extreme temperatures, particularly level density and gamma decay functions. These studies inform astrophysical reaction rates for understanding cosmic element formation. She teaches FYS-MEK1110 - Mechanics and KJM-FYS5920 - Nuclear Measurement Methods . Recent publications analyze nuclear decay patterns in Sn isotopes, neutron interactions on 89Y, and shape coexistence in rare isotopes. Her work connects nuclear structure studies with stellar nucleosynthesis. Prize for Young Outstanding Researchers, Norwegian Research Council (2016) Fulbright Scholarship (2015) ERC Starting Grant (2015-2020) Best Poster, Zakopane Conference (2006) She holds a Research Council of Norway project (2021-2026) and has previously received personal postdoctoral funding (2011-2014). Collaborations include Facility for Rare Isotope Beams, ISOLDE@CERN, IReNA, and ChETEC-INFRA networks.

Mohan Lal KolheView profile

Professor

• Smart Grid
• Renewable Energy Systems
• Electric Vehicles
• +5 more

Professor Mohan Lal Kolhe is a distinguished academic at the University of Agder , serving as a Full Professor in Smart Grid and Renewable Energy within the Faculty of Engineering and Science and the Department of Engineering Sciences . With over three decades of international academic experience, he has held positions at prestigious institutions including University College London, University of Dundee, and Hydrogen Research Institute in Canada. His career spans technical innovation, policy development (e.g., as a member of South Australia’s Renewable Energy Board), and extensive research leadership in sustainable energy systems. Research Leadership : Focus on Smart Grid integration, Electric Vehicles, Hydrogen Energy, Solar/Wind Systems, and Techno-Economic Energy Analysis. Global Recognition : Listed in the top 2% of scientists worldwide (2020-2023) by Stanford University, with 10 publications averaging 200+ citations. Recent publications emphasize advanced optimization techniques for renewable integration, EV charging infrastructure, hydrogen production, and power system stability. His work has secured competitive funding from entities like the Norwegian Research Council and EU programs. Awards and Expert Roles : Top 2% Global Scientist (Stanford, 2020-2023) Highly Cited Researcher (Top 10 publications, 200+ avg. citations) Expert evaluator for European Commission, Royal Society London, EPSRC, and Cyprus Research Foundation He actively contributes to international conferences as keynote speaker and editorial board member, with leadership roles in research groups like Autonomous and Cyber-Physical Systems and Energy Systems .

• Machine Learning
• Information Theory
• Deep Learning
• +6 more

Zhiyuan Wu is a Doctoral Research Fellow at the University of Oslo , affiliated with the Digital Signal Processing and Image Analysis research group under the Faculty of Mathematics and Natural Sciences . Education: Bachelor’s degree in Communication Systems and Information Technology from Lanzhou University, China Master’s degree from the Technical University of Munich, School of CIT Research Focus: Zhiyuan Wu specializes in machine learning, with particular emphasis on probabilistic graphical models, information theory, and tackling real-world challenges such as distributional shifts and privacy concerns. His work explores entropy regularization techniques to address label shift in distributed learning systems, aiming to improve model robustness and data privacy across diverse domains like medical applications. Publications & Research Trends: His recent publication at the International Conference on Learning Representations highlights a novel approach to mitigating label shift through entropy regularization. This aligns with his broader research goals of enhancing the adaptability and interpretability of machine learning models in dynamic, privacy-sensitive environments. Labs & Teams: He is actively involved with the Digital Signal Processing and Image Analysis (DSB) research group, contributing to collaborative projects that bridge theoretical advancements with practical implementations in machine learning.

• Wireless Communications
• Machine Learning
• Digital Twins
• +5 more

Pierluigi Salvo Rossi is a Professor at the Department of Electronic Systems , Norwegian University of Science and Technology ( NTNU ), with additional roles as Deputy Head of Department (since 2021) and Deputy Manager at the Center for Green Shift in the Built Environment (since 2022). He also serves as a part-time Research Scientist at SINTEF Energy's Gas Technology department. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests span Wireless Communications , Digital Twins , Machine Learning , and Statistical Signal Processing , focusing on applications like Industrial IoT , Fault Detection , and Energy Systems . His recent Publications highlight trends in Federated Learning , Graph Signal Processing , and Multi-Sensor Anomaly Detection across domains from Natural Gas Pipelines to Subsea Leakages . Scientific Awards include: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Professional Roles encompass editorial leadership (e.g., IEEE Sensors Journal) and conference organization (e.g., General Chair for IEEE Sensor Array and Multichannel Signal Processing Workshop, 2022). He leads major funded research projects like PREFERENCE (RCN, 2023-2027) and AUTOSHIP (RCN, 2020-2028).

Ferreira Joao CarlosView profile

Professor

• Artificial Intelligence
• AI in Health and Medicine
• AI in Energy and Transportation
• +6 more

Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.

• Power Market Analysis
• Hydropower Scheduling
• Power System Balancing
• +5 more

Hossein Farahmand is a Professor at the Department of Electric Energy, Norwegian University of Science and Technology (NTNU), and leads the Electricity Markets and Energy Systems Planning (EMESP) research group. He holds an Associate Editor role at IEEE Transactions on Energy Markets, Policy and Regulation and contributes to international initiatives like IEA Wind Task 25 and ISGAN Annex 9. Education : Dr.ing. (PhD) from NTNU (2012) His research focuses on power market analysis, hydropower scheduling, power system balancing, and local flexibility markets in smart grids. Recent work explores renewable energy integration, digitalization, and hydrogen systems. Trends include machine learning applications in hydropower scheduling, offshore wind economics, and grid flexibility solutions. Scientific awards include Senior Member of IEEE and representation in ISGAN Annex 9 and IEA Wind Task 25 . He supervises PhD candidates and co-supervised projects in areas like grid tariffs , local energy communities , and electric vehicle integration . Grants include EU Horizon 2020 and Research Council of Norway funding for projects such as IntHydro , HONOR , and Ocean Grid . Labs and teams include the EMESP research group at NTNU, collaborations with Hohai University , Smart Innovation Norway , and industry partners in China and Norway.

• Metal Forming
• Aluminum Alloys
• Product Development
• +3 more

Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.

• Electrical Engineering
• Lighting Technology
• Energy Efficiency
• +6 more

Associate Professor Eilif Hugo Hansen works at the Department of Electrical Energy, Norwegian University of Science and Technology (NTNU). His primary research areas are electrical installations and lighting technology, with a focus on energy efficiency, daylight utilization, and applications in aquaculture. He has contributed to standardization committees like NEK/NK64 and IEC TC64, and co-developed the LYSSTYR lighting control software. Department: Electrical Energy School: Faculty of Information Technology, Mathematics, and Electrical Engineering University: Norwegian University of Science and Technology Education includes a Civil Engineering degree (1985) and Doctorate in Engineering (1990) from NTH (now NTNU). He is a certified electrical contractor with expertise in low-voltage systems. Research Highlights : Over 30 years of publications in lighting technology, electrical safety, and energy systems. Key themes include ground fault detection , non-radial electrical networks , and daylight integration in buildings. His work with Lysforsk center and SINTEF collaborations underscores his industry impact. Teaching involves courses like TET4165 Light and Lighting and TET4170 Electrical Installations . He has supervised over 40 master's theses, including topics on smart lighting , power distribution , and energy-saving technologies . Professional Engagement : Member of NEK/NK64 Low-Voltage Installations committee Contributor to IEC TC64 MT12 standards Former head of the Energy Transformation and Electrical Installations program (1997-2000) Co-developer of the LYSSTYR software for simulating daylight rhythms

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Dr. Danesh Tarapore is an Associate Professor at the University of Southampton specializing in robotics and AI. He focuses on human-robot interaction, swarm intelligence, and autonomous systems. His current research involves developing resilient robotic teams and optimizing learning algorithms for constrained environments. He supervises 6 PhD students in the iPhD MINDS and Computer Science programs. Dr. Tarapore's work bridges theoretical advancements with practical applications in autonomous navigation, multimodal dataset creation, and quality-diversity optimization. His publications span conferences like HRI and journals in robotics and AI. He collaborates with institutions like the University Hospital Southampton and the Boldrewood Innovation Campus. Research Interests: Human-robot collaboration, swarm systems, machine learning, and adaptive control Key Contributions: HRI-SENSE dataset, evolutionary subset selection algorithms, forest navigation frameworks Grants and Funding: Active projects in multi-agent systems and resilient robotics Dr. Tarapore maintains active roles in the robotics community through conference participation and interdisciplinary collaborations.

• Wildlife Ecology
• Large Mammal Conservation
• Human-Wildlife Conflict
• +5 more

Martin Mayer serves as Associate Professor at the University of Inland Norway within the Faculty of Applied Ecology, Agricultural Sciences and Biotechnology and the Department of Forestry and Outback Studies. Based at the Evenstad study location, his research focuses on large mammal ecology in human-dominated landscapes across Scandinavia and Europe. His primary research interests center on Wildlife Ecology and Large Mammal Conservation , with particular emphasis on human-wildlife conflict resolution, road ecology, and carnivore management. Mayer investigates how species like moose, deer, and beavers adapt to anthropogenic landscapes through habitat selection, movement patterns, and population dynamics. His work integrates cutting-edge methodologies including GPS telemetry, drone surveillance, and citizen science data to address conservation challenges in agricultural and urban settings. Analysis of Mayer's recent publications reveals strong trends in using roadkill data for population monitoring, examining spatial constraints on wildlife movement, and developing mitigation strategies for human-wildlife conflicts. His research increasingly incorporates cross-border European perspectives on wildlife management while maintaining a strong foundation in Scandinavian field ecology. Mayer actively contributes to the LARGE research group (ecology of large animals), which conducts field studies on ungulate behavior, predator-prey dynamics, and landscape-scale conservation planning in Norway's boreal ecosystems.

• Safe Reinforcement Learning
• Data-Driven Model Predictive Control
• Artificial Intelligence
• +7 more

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.