Birgitte Bak-Jensen is a Professor at the Department of Energy, Aalborg University, where she has worked since 1988. She specializes in intelligent control of power distribution systems, with research focusing on grid stability, power quality, and integration of dispersed generation and smart grid technologies. Her work also addresses multi-energy system interactions between electrical grids, heating, and transport sectors. Projects : Led EU H2020 projects (SERENE, SUSTENANCE) and Danish initiatives (EFFORT, SMARTCE2H) Publications : Over 250 papers on distribution grid control and smart energy systems Her research combines renewable energy integration , electric vehicle grid interaction , and energy storage optimization . Recent work includes explainable AI for wind forecasting and voltage control strategies for EV charging. 2025 Awards : Best Student Paper Award (2023), Best Paper Award (2021), CIGRE Technical Council Award (2018) Organizational Roles : Vice Head of AAU Energy Research, leadership positions in IEEE and CIGRE
Remus Teodorescu is a Professor at AAU Energy , Aalborg University , specializing in Power Electronics System Integration and Materials . His work bridges Lithium-Ion Batteries , Modular Multilevel Converters , and Smart Battery Systems . Education : Not explicitly mentioned in the text. Research Interests focus on Battery Management Systems , AI-Driven Energy Optimization , and Power Electronics for renewable energy integration. Key projects include Digital Twin for Lithium-Ion Batteries and BMS-DC for Data Centers . Recent Publications (2025) emphasize Finite Set MPC , Gradient Descent Optimization , and AI in Battery Parameter Estimation . His 2024 work explores Physics-Informed Neural Networks and Fault-Tolerant Converters . Scientific Awards : Villum Foundation Grant (313 million kroner, 2021) Named world's best in electrical engineering (2023) Advising includes supervising PhD projects on AI-Accelerated Battery Twins and Data-Driven SOH Estimation . Collaborations span Energy Cluster Denmark and Villum Fonden .
Kunihiko Kaneko is a Professor at the Niels Bohr Institute, University of Copenhagen, with a distinguished career in theoretical biophysics and complex systems. He received his PhD and MSc in Physics from the University of Tokyo, and has held leadership roles at the Universal Biology Institute and Center for Complex Systems Biology. PhD Physics, 1984 - University of Tokyo MSc Physics, 1981 - University of Tokyo His research spans five primary areas: Universal Biology, Evolutionary Constraints, Ecosystem Dynamics, Neural Cognition, and Universal Anthropology. He has published extensively on multi-level consistency principles, dimensional reduction in biological systems, and reciprocity between robustness and plasticity across scales. Recent publications show strong focus on microbial ecosystems (2025), evolutionary game theory (2025), neural modular architectures (2024), and dimensional reduction in cellular systems (2024). His work bridges physics and biology through dynamical systems theory applied to diverse phenomena from protocells to human societies.
James A. Evans is the Max Palevsky Professor of Sociology and Data Science at the University of Chicago, where he is a faculty member in the Department of Sociology within the Division of the Social Sciences. He is the director of Knowledge Lab and the Faculty Director of the Masters Program in Computational Social Science . He holds additional affiliations as an External Professor at the Santa Fe Institute , External Faculty at the Complexity Science Hub, Vienna , and Visiting Faculty Researcher at Google . Education: B.A. in Anthropology, Brigham Young University (1994) M.A. in Sociology, Stanford University (1999) Ph.D. in Sociology, Stanford University (2004) His research centers on the collective system of thinking and knowing , exploring how ideas emerge, spread, and evolve through social and technical systems. He investigates innovation, collective intelligence, and the science of science , using large-scale data modeling, machine learning, generative AI, and network analysis to study knowledge creation. His work spans domains including science, technology, law, and religion, with a focus on how AI is reshaping discovery processes. The most recent publications highlight trends in AI and scientific discovery , with a strong emphasis on innovation, knowledge systems, and human-machine intelligence . His research increasingly explores AI as a transformative agent in science , including the concept of 'alien intelligence' and the development of complementary AI to augment human capacity. Projects like the $20M NSF-funded APTO initiative aim to build language models that predict technological outcomes by analyzing historical data. Scientific Recognition and Funding: Research supported by the National Science Foundation (NSF) , National Institutes of Health (NIH) , Air Force Office of Scientific Research (AFOSR) , and philanthropic sources Work published in Nature, Science, PNAS , and leading social science journals Featured in The New York Times, The Economist, The Atlantic, Wired, NPR, BBC, Le Monde , and others James Evans advises on science policy and funding strategies, emphasizing the importance of diversity, interdisciplinary collaboration, and demographic balance in fostering innovation. He critiques current academic incentives and proposes alternative discovery regimes. He leads Knowledge Lab , a collaborative research environment that conducts seminars, grants, and employment opportunities in computational social science and AI.
Torsten Berning serves as Associate Professor at AAU Energy within The Faculty of Engineering and Science at Aalborg University, Denmark. His research focuses on thermal engineering systems, hydrogen production technologies, and electro-fuels development, with significant contributions to fuel cell and electrolyzer innovation. His primary research domains include fuel cell engineering, electrolyzer technology, and computational fluid dynamics applied to energy systems. He investigates water management in proton exchange membrane fuel cells, heat and mass transfer in electrolysis cells, and efficiency optimization of hydrogen production systems. His methodology combines experimental validation with advanced CFD modeling to address durability and performance challenges in electrochemical energy conversion. Recent publications (2024-2025) demonstrate concentrated research on alkaline electrolysis systems and thermal management solutions. Key advancements include reducing gas crossover in electrolyzers, optimizing indirect evaporative coolers, and designing proton exchange membrane electrolyzers. His work leverages computational modeling to enhance efficiency and scalability of hydrogen production technologies while addressing multiphase flow and heat transfer complexities. Professor Berning actively supervises PhD candidates: H. D. Miller on Degradation Modeling and Lifetime Prediction of Electrolyzers D. L. Martinho on Computational Fluid Dynamics of Alkaline Electrolysis Cells W. Liu on Water Transport in Proton Exchange Membrane Fuel Cells His research is funded by multiple grants including EUDP's 'Boosting Economic Electrolyzer Stack Technology 2' (2022-2025) and the Danish Energy Agency's 'Degradation Modeling and Lifetime Prediction of Electrolyzers' project (2024-2027). He collaborates within AAU Energy's research ecosystem on hydrogen production systems and thermal management technologies, contributing to projects like the Adiabatic Cooling Systems for Decentralised Ventilation and advancing electrolyzer stack technology through industry partnerships.
Francisco Camara Pereira is a Professor and Head of Section at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on Intelligent Transportation Systems, Machine Learning, and Data-Driven Decision-Making in transportation contexts. He actively contributes to advancing transportation science through interdisciplinary approaches combining simulation, optimization, and AI techniques. His work addresses challenges in public transport analysis, charging infrastructure planning, and multimodal demand prediction. Recent projects include developing graph-based optimization methods for electric vehicle networks and causal discovery frameworks for transportation systems. He supervises multiple PhD students in areas like federated learning for cyclist safety, causal graph neural networks, and socially aware AI models. Key contributions include publications on smart card data analysis for travel surveys, stochastic infrastructure expansion models, and transfer learning for bike-share systems. His research aligns with UN Sustainable Development Goals related to sustainable cities and innovation. Dr. Pereira collaborates internationally on transportation policy and infrastructure projects. His lab focuses on translating theoretical advancements into practical solutions for urban mobility challenges.
Daniel Hershcovich is a Tenure Track Assistant Professor at the Department of Computer Science (Faculty of Science, University of Copenhagen) specializing in Natural Language Processing and Machine Learning . His research focuses on cross-cultural adaptation of language models, integrating human values into AI, and analyzing food-related cultural narratives for sustainable diets. Education: Ph.D. in Computational Neuroscience from Hebrew University of Jerusalem B.Sc. in Mathematics and Computer Science from Open University of Israel Recent publications highlight his work on multimodal models (haptic captioning, visual assistants for the blind), historical text analysis (Danish/Norwegian literature, euphemism detection), and cross-cultural NLP (recipe adaptation, cultural value alignment, climate awareness). His projects frequently combine AI ethics with domain-specific applications like food studies, historical linguistics, and accessibility research. Key collaborative networks include institutions in Denmark, Israel, and international partnerships through conferences like ACL, EMNLP, and workshops on cross-cultural NLP. The NLP section at DIKU serves as his primary affiliation for these efforts.
John Dalsgaard Sørensen is a Professor and Head of Research Group at the Department of the Built Environment, Aalborg University, within the Faculty of Engineering and Science. He leads the Risk, Resilience, Safety, and Sustainability of Systems Research Group and is affiliated with the Danish Centre for Risk and Safety Management. His research focuses on structural safety, wind turbine reliability, probabilistic design, and risk assessment of infrastructure systems. He has supervised 13 PhD students and contributed to over 600 publications. Key research areas include wind turbine structural integrity, fatigue analysis of offshore and onshore structures, probabilistic design standards (e.g., Eurocodes), and risk-based decision-making for infrastructure. He leads projects like Windscanner (remote sensing for wind measurements) and MANTIS (cyber-physical maintenance systems). Collaborations span academia and industry, addressing challenges in energy systems, civil infrastructure, and safety engineering. His work emphasizes practical applications of advanced modeling techniques, such as Bayesian networks and stochastic simulations, to enhance reliability and reduce operational costs. He is actively involved in standardization efforts for structural design and serves on boards like Energi- og MiljøData Fonden. Recent activities include presenting at international conferences and advising on media debates related to structural safety.
Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects
Sebastian Risi is a Professor at the IT University of Copenhagen , where he directs the Creative AI Lab and co-directs the Robotics, Evolution and Art Lab (REAL) . His work bridges computational evolution, deep learning, and collective intelligence for applications in robotics, art, and video game design. His research focuses on self-organizing AI systems that grow or assemble through local interactions, inspired by biological development. Key areas include neuroevolution , neural cellular automata , and generative modeling , with applications in adaptive robotics, game content creation, and damage-resilient AI. Recent publications highlight trends in self-assembling neural architectures (NDPs) and 3D functional machine generation (Minecraft experiments). Awards include ERC Consolidator Grant (2022), Best Paper at FDG’21 , and Google Faculty Award (2019). Scientific Awards : ERC Consolidator Grant (GROW-AI), Best Paper FDG’21, Runner-Up IEEE Games’20, GECCO 2017 Competition Winner, Sapere Aude Grant, Amazon/Google Faculty Awards He advises on projects like GROW-AI (EU-funded), AI-TESTER (game testing), and C2SIM (military systems). Media coverage includes Science , Wired , and Popular Science .
Brian Møller Andersen is a Professor in Solid State Physics at the Niels Bohr Institute, University of Copenhagen, where he has maintained continuous academic appointments since completing his PhD. His research spans multiple frontiers of condensed matter physics with significant contributions to superconductivity and magnetism. PhD in Theoretical Physics, University of Copenhagen (2001-2003) PhD studies at Stanford University (2000-2001) MSc in Theoretical Physics, University of Copenhagen (1998-2000) International Exchange at UC Berkeley (1997-1998) BSc in Mathematics and Physics, University of Copenhagen (1994-1997) Andersen's primary research focuses on Superconductivity , particularly high-temperature superconductors where magnetism and superconductivity coexist, and Magnetism in novel quantum materials. His work extends to Quantum Transport phenomena, Ultracold Atoms in optical lattices, Topological Insulators , and Strongly Correlated Systems . Recent publications reveal a growing emphasis on altermagnetism, kagome lattice physics, and topological superconductivity, indicating significant evolution in his research trajectory toward emergent quantum phenomena. Analysis of his 15 most recent publications (2024-2025) shows a clear progression into cutting-edge areas: 60% focus on altermagnetism and novel magnetic states, 40% on unconventional superconductivity in topological materials, and 30% examining quantum confinement effects. His work demonstrates increasing interdisciplinary connections between condensed matter theory, materials science, and quantum information science, with frequent collaborations across Europe and the US. Andersen has received significant research support through prestigious fellowships including the Lundbeck Foundation fellowship (Associate Professor level, 2012-2017) and FNU Steno Stipend (Assistant Professor level, 2009-2013), alongside early career support from the Villum Kann Rasmussen Post. Doc. Stipend. His research group at the Niels Bohr Institute focuses on theoretical modeling of quantum materials, particularly computational approaches to understanding competing orders in correlated electron systems. The group maintains strong connections with experimental teams conducting neutron scattering, STM, and ARPES measurements to validate theoretical predictions.
Grethe Winther is a Professor and Head of Section in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in Materials and Surface Engineering. Her research is centered on the analysis and modeling of microstructure and mechanical properties of metals, with a strong emphasis on dislocation structures, deformation textures, and recrystallization processes. Her research interests include: Dislocation structures and boundary analysis in deformed metals Crystal plasticity modeling using synchrotron data (3DXRD) Orientation relationships in recrystallization Prediction of mechanical properties in industrial metal forming Multiscale modeling of plastic deformation and surface roughening The recent articles (2025) highlight a consistent focus on advanced characterization techniques like dark-field X-ray microscopy and discrete dislocation dynamics simulations. These works explore the formation of geometrically necessary boundaries, dislocation cell evolution, and multiscale surface deformation, reflecting a strong integration of experimental and computational methods in materials science. Key themes include plastic deformation mechanisms, microstructure evolution, and predictive modeling in metallic systems. Grethe Winther actively supervises multiple PhD projects, including those on dislocation dynamics, X-ray microscopy, and ductile failure simulations. She collaborates extensively with researchers such as H.F. Poulsen and C.V. Nielsen. Her work is supported by ongoing research projects at DTU, focusing on fundamental and applied aspects of metal deformation and microstructure. She is affiliated with the Materials and Surface Engineering section at DTU, where she leads research efforts combining advanced experimental techniques with theoretical modeling to understand and predict metal behavior under deformation.
Søren Lundbye-Christensen is an Associate Professor and Biostatistician affiliated with the Clinical Institute at the Faculty of Health Sciences, Aalborg University, and Aalborg University Hospital in Denmark. He specializes in biostatistical support for medical research, with a strong emphasis on cardiovascular and epidemiological studies. His research interests include biostatistics, survival analysis, cohort studies, clinical epidemiology, and statistical modeling in public health. He has contributed to a wide array of healthcare research, particularly in cardiovascular diseases, cancer, maternal health, and infectious diseases. His methodological expertise spans time-to-event analysis, registry-based research, and interval-censored data modeling. The recent publications highlight a strong trend in applying advanced statistical methods to large-scale clinical and population-based datasets. His work often involves collaboration with medical researchers to derive prognostic models, validate clinical databases, and assess public health outcomes. Key themes include cardiovascular risk, fertility, cancer biomarkers, and implementation of medical training programs. Scientific Contributions and Recognition: Published over 320 research articles and datasets. Active contributor to methodological advancements in biostatistics. Regular peer reviewer, including for journals like the R Journal. Public engagement through media appearances on statistics and health. Academic Advising and Grants: Søren has supervised 31 student theses, formally serving as PhD supervisor for 14 theses and as a biostatistical advisor for 19 others, primarily in mathematics and statistics. He has participated in numerous research projects funded through institutional and national grants, including studies on seasonal disease trends, postoperative complications, and metabolic disease prediction. His work often involves interdisciplinary collaboration across medicine, public health, and data science. Labs and Research Teams: He is embedded in collaborative research networks at Aalborg University Hospital and Aalborg University, contributing statistical expertise to clinical research groups. He is involved in projects utilizing Danish national health registries and has contributed to the development and validation of clinical databases. His work supports both hypothesis-driven medical research and methodological innovation in biostatistics.
Professor Katrin Vorkamp holds a position at the Department of Environmental Science, specializing in Environmental Chemistry and Toxicology at Aarhus University. Her research focuses on understanding the transport, exposure pathways, and impacts of persistent pollutants such as PFAS, POPs, and mercury in Arctic and Antarctic ecosystems, with a particular emphasis on wildlife and human health implications. She has contributed to projects addressing climate change effects on contaminant dynamics, policy-oriented monitoring frameworks (e.g., Water Framework Directive), and innovative methods like passive sampling and non-target screening. Her work spans interdisciplinary collaborations, including the AMAP Core 2025-2027 project tracking pollution in Greenland biota and the ArcSolution initiative exploring a One Health perspective in Arctic pollution. She also leads the NAMMINE project on non-target analysis in Nordic marine mammals. Key themes in her research include understanding local vs. long-range pollutant sources, climate-contaminant interactions, and developing early warning systems for emerging chemicals. Publications highlight her expertise in biochar applications for waste valorization, contaminant trends in Adélie penguin eggs, and computational tools for risk assessment. Her projects often bridge environmental science with policy, aiming to inform sustainable solutions for pollution challenges in sensitive ecosystems.
Philip Loldrup Fosbøl is an Associate Professor in the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU), College of Engineering. He is actively affiliated with CERE – Center for Energy Resources Engineering, where he conducts research on CO 2 capture, storage, transport, and utilization. His work integrates thermodynamic modeling, process simulation, and pilot-scale experimentation to address challenges in carbon management and sustainable energy systems. His research interests include: Carbon Dioxide Capture and Storage (CCS) Thermodynamics and Phase Equilibrium of Electrolyte Solutions Process Design, Simulation, and Optimization CO 2 Corrosion in Energy Systems Biogas Upgrading and Cleaning CO 2 Utilization and Conversion Development of Predictive Thermodynamic Models Mobile and Large-Scale Pilot Facilities for CO 2 Capture His recent publications (2025) highlight a strong focus on biogas upgrading, solvent degradation in industrial CO 2 capture, thermophysical property measurements, and novel electrochemical separation methods. These works reflect a consistent trend toward energy-efficient, scalable, and industrially applicable solutions for decarbonization, particularly in flue gas and biogas treatment. Scientific awards received: Top PhD Thesis of the Year (2008) He actively supervises multiple PhD students and leads research projects funded by industrial partners such as Ørsted, Shell, Equinor, and Novozymes, as well as EU initiatives including CASTOR, iCap, and OCTAVIUS. His work contributes to UN Sustainable Development Goals related to climate action and affordable, clean energy. He is involved in laboratory research on thermodynamic equilibrium (VLE, SLE), heat capacity, corrosion mechanisms, and core flooding for CO 2 storage. His team develops experimental methods and operates pilot facilities for CO 2 capture and biogas cleaning, often in collaboration with key researchers like Kaj Thomsen, Nicolas von Solms, and Georgios Kontogeorgis.
