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Didier SornetteView profile

Professor

• Complex Systems
• Risk Analysis
• Financial Crises and Bubbles
• +11 more

Didier Sornette is Professor of Entrepreneurial Risks at the Department of Management, Technology and Economics (D-MTEC), Swiss Federal Institute of Technology Zurich (ETH Zurich). He is also Professor of Finance at the Swiss Finance Institute, Associate Member of the Department of Physics and Department of Earth Sciences at ETH Zurich, Principal Investigator at the Future Resilient Systems Center at the National University of Singapore, and Specially Appointed Professor at the Institute of Innovative Research, Tokyo Institute of Technology. His research centers on the theory and application of complex systems, particularly focusing on the prediction and control of extreme events—termed 'dragon-kings'—in financial markets, geophysical systems, and socio-economic networks. He employs nonlinear dynamical models, statistical analysis, and feedback mechanisms to detect early warning signals of crises. The available publication highlights his work at the intersection of complexity science and democratic governance, suggesting a growing interest in applying systemic risk methodologies to societal structures. This reflects a broad trend in his research: leveraging quantitative models for real-world systemic instabilities across disciplines. Director, Financial Crisis Observatory (FCO) Co-founder, ETH Risk Center Principal Investigator, Future Resilient Systems, National University of Singapore Specially Appointed Professor, Tokyo Institute of Technology He leads interdisciplinary teams working on InnovWiki, a collaborative platform integrating idea development with prediction markets and data visualization. His lab initiatives emphasize open collaboration, crowd-based evaluation, and real-time risk monitoring. While specific awards and students are not listed in the provided text, his leadership in major research centers and global observatories underscores significant recognition and mentorship activity.

Gregor GiebelView profile

Researcher

• Wind Power Forecasting
• Renewable Energy Integration
• Wind Farm Flow Control
• +7 more

Dr. Gregor Giebel is a senior researcher and Head of Section in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU), specifically within the Renewable Plants in Energy Systems group. He is actively engaged in advancing wind energy technologies and their integration into modern power systems. His leadership extends to major international and EU-funded initiatives, including IEA Wind Task 51 and the NEST Facilities research infrastructure. Gregor Giebel's research focuses on short-term wind power forecasting , large-scale integration of wind into electricity grids , wind farm flow control , and condition monitoring of wind turbines . His work is instrumental in improving the reliability and efficiency of renewable energy systems. He has made significant contributions to international standardization through the IEC and is a key figure in global wind energy research coordination. The recent publications highlight a strong trend toward weather-driven energy forecasting , modeling wind farm impacts on weather systems , and developing national research infrastructures for energy transition . These works reflect a deep integration of meteorology, energy systems engineering, and policy-relevant research. His scientific recognition includes: Poster Award at the WindEurope Offshore Conference, Copenhagen 2019 Poster Award on WindEurope Conference, Copenhagen 2021 Dr. Giebel is a Principal Investigator (PI) on multiple high-impact projects such as NEST Facilities , IEA Wind Task 51 , and GreenHyScale . He is renowned for his success in research funding, with a proposal success rate exceeding 50%. He has led the EU Marie Curie Initial Training Network Train 2 Wind and previously headed the FarmConners EU Coordination Action on wind farm control. He leads and is deeply involved in the NEST Facilities —a distributed Danish research infrastructure for energy transition—and contributes significantly to the Weather2X and GreenHyScale projects, which aim to advance green hydrogen production and weather forecasting for energy systems.

• Animal Science
• Greenhouse Gas Emissions
• Environmental Engineering
• +5 more

Michael Jørgen Hansen serves as a Senior Researcher in Environmental Engineering at Aarhus University's Department of Biological and Chemical Engineering. His research focuses on agricultural emissions reduction, particularly methane and odor control from pig production systems. His work bridges environmental science, agricultural engineering, and climate policy development. Research interests span Greenhouse Gas Emissions from livestock operations, Waste Management technologies, Odor Control systems using mass spectrometry and olfactometry, and Methane Reduction strategies through dietary interventions and housing innovations. His fingerprint analysis reveals strong specialization in pig production systems (100%), methane emissions (60%), and emissions measurement techniques (42%). Hansen leads multiple active research projects including STOREMIS (Greenhouse gas emissions from slurry storage), PIGMET (methane prediction modeling), and a 2024-2025 project on odor regulation in pig barns. His research outputs include 114 publications with significant activity in 2022-2025, featuring prominent work on fiber-type impacts on methane production and robotic slurry management systems. As primary investigator for the 2024 odor regulation project and co-investigator on multiple international collaborations, Hansen actively contributes to Danish climate technology policy through DCA - National Center for Food and Agriculture. His media presence includes coverage in Danish agricultural press regarding CO2 tax avoidance technologies and pig odor measurement systems.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +5 more

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

Søren Rud KristensenView profile

Professor

• Health Economics
• Healthcare Quality Improvement
• Healthcare Incentive Design
• +3 more

Søren Rud Kristensen is a Professor at the Department of Public Health, University of Southern Denmark and holds an Honorary Senior Lecturer position at Imperial College London. His research focuses on healthcare organization and performance, particularly incentive design for quality improvement, with expertise in microeconometric analysis of large administrative datasets. He has held roles including Senior Lecturer (2017-2021) at Imperial College London. Key research areas include payment for performance systems, integrated care models, and clinical guideline implementation. He has published extensively on healthcare quality, patient safety, and health policy with 71+ peer-reviewed articles. His work addresses challenges like hospital competition effects, spillover mechanisms of financial incentives, and the impact of adverse events on patient outcomes. He has received multiple research grants including support for Manchester research stays and funding from Sygekassernes Helsefond. Teaching responsibilities include modules on integrated health care, quality management, and data-driven quality development. His current projects focus on regulating knowledge use in healthcare (PINCH project) and evaluating resource constraints in primary care. Labs/teams involvement includes collaborations through the Danish Centre for Health Economics (DaCHE) and international networks analyzing health systems in England, Brazil, Mozambique, and Zimbabwe. His work bridges economic theory with practical healthcare system improvements.

Peter Bach AndersenView profile

Researcher

• Electric Vehicle Integration
• Grid Services
• Battery Energy Storage
• +4 more

Peter Bach Andersen serves as Head of Section and Senior Researcher at the Department of Wind and Energy Systems, Technical University of Denmark (DTU). Based at Elektrovej 329A in Kgs. Lyngby, he leads research in electric vehicle integration and prosumer energy systems with an ORCID identifier 0000-0002-5202-3584. His work spans grid services, battery storage, and smart charging infrastructure within DTU's EV Lab ( www.evlab.dk ). Andersen's research focuses on electric vehicle grid integration , specializing in charging infrastructure planning, battery energy storage systems, and virtual power plant concepts. His fingerprint analysis reveals dominant expertise in Electric Vehicle Engineering (100%), Battery Engineering (13%), Ancillaries (12%), and Power Engineering (11%). Current projects emphasize flexibility quantification, grid service delivery through EV clusters, and Nordic grid characteristics. His work contributes significantly to UN Sustainable Development Goals related to clean energy and sustainable cities. His publication portfolio shows strong trends toward grid-interactive EV systems with recent emphasis on conditional connection agreements, fast-charging urban impacts, and data-driven battery health prognosis. The research demonstrates increasing focus on practical implementation challenges and market integration aspects of vehicle-grid systems. Andersen actively supervises four PhD students (Menchaca, Striani, Unterluggauer, Sevdari) across projects including charging infrastructure planning, EV clustering methods, and urban charging infrastructure impacts. He leads the FLOW project (2022-2026) investigating flexible energy systems for optimal EV integration while participating in multiple EU-funded initiatives. His research group maintains strong industry connections through DTU's EV Lab, focusing on real-world validation of grid services using commercial EVs and chargers.

• Cardiac Surgery Outcomes
• Risk Assessment Models
• Echocardiography
• +4 more

Sune Damgaard is a Clinical Associate Professor at the University of Copenhagen's Faculty of Health and Medical Sciences within the Department of Clinical Medicine. He specializes in Cardiothoracic Surgery at Rigshospitalet (Copenhagen University Hospital), where he maintains active clinical and research roles. His academic profile is associated with the Department of Clinical Medicine and Rigshospitalet's Heart and Lung Surgery division. Dr. Damgaard's research focuses on expanding traditional cardiac surgery risk assessment models beyond physiological parameters. His work investigates how social, emotional, behavioral, and functional factors impact surgical outcomes. Key areas include validating modified EuroSCORE systems, developing multidimensional risk prediction tools, and studying echocardiographic techniques during cardiac procedures. He has pioneered research on tricuspid annular plane systolic excursion measurement and coronary bypass graft patency assessment. His publication trends reveal consistent emphasis on integrating patient-reported outcomes into surgical risk models. Recent work (2018-2022) demonstrates growing focus on psychosocial predictors of cardiac surgery outcomes, with multiple studies examining how non-physiological factors contribute to postoperative complications. Earlier research (2012-2015) centered on technical aspects of graft patency and intraoperative monitoring. Dr. Damgaard maintains active clinical practice at Rigshospitalet's Cardiothoracic Surgery department while conducting translational research through the University of Copenhagen's medical faculty. His work bridges clinical cardiology, surgical innovation, and health services research with particular relevance to improving risk stratification in cardiac surgery.

Filipe RodriguesView profile

Associate Professor

• Machine Learning
• Intelligent Transportation Systems
• Transportation Science
• +7 more

Filipe Rodrigues is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in intelligent transportation systems and transportation science. His work integrates machine learning, artificial intelligence, and behavioral modeling to improve urban mobility and public transport systems. His research interests lie at the intersection of machine learning , transportation science , and behavioral modeling . He specializes in discrete choice modeling , reinforcement learning , graph neural networks , and smart card data analytics . His work contributes to sustainable urban mobility, leveraging big data and AI for proactive traffic control and public transport optimization. The recent publications highlight a strong trend toward integrating AI and behavioral science in transportation. Key themes include ride-sourcing driver behavior , public transport trip validation , autonomous fleet control , and causal machine learning . These works predominantly employ deep learning , Bayesian modeling , and offline reinforcement learning techniques, often applied to real-world datasets from Denmark and beyond. Scientific Contributions: Active contributor to journals like Transportation Research Part C and Journal of Choice Modelling . Supervises multiple PhD projects on AI in transportation and causal modeling. Regular presenter at major transportation and AI conferences. Advising and Grants: Filipe Rodrigues is the main or co-supervisor of several PhD students including O. B. Lassen, F. M. F. Santos, A. Nguyen, and X. Wu. He leads and participates in funded research projects such as 'Proactive traffic control through AI and Big Data' and 'Causal Graph Neural Networks for machine learning meta-modelling', indicating sustained grant support. His collaborative network spans institutions in Europe and beyond. Labs and Teams: He is part of the Intelligent Transportation Systems research group at DTU, collaborating closely with researchers like F. C. Pereira and C. M. L. Azevedo. The team focuses on data-driven mobility solutions, combining simulation, machine learning, and behavioral insights.

Rasmus WaagepetersenView profile

Professor

• Spatial Statistics
• Quantitative Genetics
• Markov Chain Monte Carlo Methods
• +3 more

Rasmus Waagepetersen is a Professor in the Department of Mathematical Sciences at Aalborg University, affiliated with The Faculty of Engineering and Science. His research focuses on spatial statistics, quantitative genetics, and statistical methodology for spatial point processes. He leads and participates in interdisciplinary projects such as urbanLab (spatial data analysis for urban planning) and studies on microbiome interactions in agricultural systems. Key research areas include spatial point processes, Markov chain Monte Carlo methods, and statistical inference for complex ecological and biomedical data. His work frequently involves collaborations with environmental and biological scientists, as seen in projects analyzing root microbiota assemblies in legumes and climate data for building simulations. Waagepetersen has contributed to methodological advancements in spatial statistics, including goodness-of-fit tests, likelihood-based inference for log Gaussian Cox processes, and quasi-likelihood approaches for case-control point pattern data. His research has been supported by grants from institutions like the Villum Foundation. Key Projects: urbanLab, Klimadata til fugtsimuleringer, Nod factor signaling in plant microbiota. Grants: Multiple projects funded by the Villum Foundation and Danish research councils. His recent publications address topics such as space-time point processes, microbiome data analysis, and statistical modeling in education. Waagepetersen maintains an active research group and collaborates internationally on both theoretical and applied statistical problems.

• Multiple Sclerosis
• Neurology
• Neuropathic Pain
• +7 more

Henrik Boye Jensen is a Clinical Associate Professor at the Department of Regional Health Research, IRS - Sygehus Lillebælt, Forskningsenhed for Neurologi (Kolding), affiliated with the University of Southern Denmark. His work bridges clinical neurology and research, with a strong focus on multiple sclerosis, stroke, and neurorehabilitation. Institution: University of Southern Denmark Department: Department of Regional Health Research Research Unit: Research Unit for Neurology, Kolding Academic Rank: Clinical Associate Professor His research interests center on multiple sclerosis , particularly disease progression, pharmacological treatments, and the role of exercise in neuroprotection. He also investigates stroke rehabilitation and diagnostic challenges in neurological conditions such as Lyme neuroborreliosis. His work frequently employs advanced methodologies including randomized controlled trials , cohort studies , and neuroimaging (MRI) . The 15 most recent articles reflect a strong trend in clinical neuroscience , with emphasis on MS phenotypes , pharmacological interventions , exercise as therapy , and diagnostic accuracy . His publications span high-impact journals in neurology and rehabilitation, demonstrating consistent scholarly output. Scientific Awards: No specific awards listed in the provided text. Henrik Boye Jensen has been actively involved in advising and research projects. He has contributed to at least three supervised works, including a Ph.D. thesis, and is a working partner in the RoboRehab project on stroke rehabilitation. He has participated in multiple collaborative research initiatives and has delivered presentations in both academic and public settings. He is also involved in public discourse through media contributions on neurological health issues. Labs and Teams: He is associated with the Research Unit for Neurology in Kolding and collaborates extensively within the Region of Southern Denmark and with national and international research groups, particularly in MS and stroke research.

Anders Henry NielsenView profile

Researcher

• Plasma Physics
• Fusion Energy
• Tokamak Devices
• +7 more

Anders Henry Nielsen is a Senior Scientist in the Department of Physics at the Technical University of Denmark (DTU), specializing in Plasma Physics and Fusion Energy. He is based at DTU’s Fysikvej campus in Kgs. Lyngby, Denmark, and maintains an active research profile with over 350 publications. His work is central to advancing understanding in magnetic confinement fusion, particularly through computational modeling and experimental collaboration with major tokamak facilities worldwide. His research interests lie at the intersection of plasma turbulence, edge physics, and fusion energy. He investigates phenomena such as zonal flows, coherent structures, and transport scaling in tokamak plasmas. His work often involves developing and applying advanced numerical models, including coupling Monte Carlo methods with 2D fluid models like HESEL, to simulate neutral particle behavior and turbulence in the plasma edge. He has contributed to major experimental campaigns on devices such as TCV, ASDEX Upgrade, and EAST, focusing on heating, fueling, and stability. His recent publications highlight trends in computational plasma physics, parametric instabilities, and cross-field transport. These works span disciplines including plasma turbulence, magnetic confinement, and fusion reactor engineering, with subfields like Monte Carlo simulations, electron cyclotron resonance heating, and synthetic diagnostics. His research consistently addresses key challenges for ITER and DEMO, such as power threshold scaling and heat flux management. Anders Henry Nielsen has supervised multiple PhD students, including R. Gerru Miguelañez, G. Avdeeva, J. M. B. Olsen, and J. Madsen, on projects related to zonal flow dynamics, neutral injection, and turbulence modeling. He has received research funding from various sources, including national and international fusion programs, and has been involved in projects funded by research councils and institutional grants. He is affiliated with the Plasma Physics and Fusion Energy section at DTU, where he collaborates closely with leading researchers such as V. Naulin, J. J. Rasmussen, and S. Kragh Nielsen. His team contributes to both theoretical and experimental aspects of fusion science, participating in international collaborations and presenting findings at major conferences. He has organized academic events, such as the Ninth Sino-Danish Autumn School on Fusion Plasma Physics and Technology.

• Climate Dynamics
• Numerical Weather Prediction (NWP)
• Atmospheric Modeling
• +3 more

Professor Eigil Kaas is affiliated with the Niels Bohr Institute at the University of Copenhagen . His work spans climate dynamics , numerical weather prediction (NWP) , and atmospheric modeling . As former Section Head of Climate and Computational Geophysics , he leads research on climate-chemistry coupling and sea ice impacts. Education : MSc (1987) and PhD (1993) in Meteorology from University of Copenhagen Research Focus : Climate dynamics and physics Numerical methods in atmospheric models Machine learning for weather prediction Arctic sea ice-climate interactions Thunderstorm electricity and radiation Coupled atmosphere-ocean modeling Article Trends : Recent work combines neural networks with radiative transfer optimization Focus on storm dynamics and gamma-ray flashes Extreme precipitation modeling under climate change Pioneering tidal flow studies in Faroe Island fjords Teaching Legacy : Instructor of Atmospheric Physics and Dynamical Meteorology courses Developed zonally averaged climate model for educational use Mentored 12 PhD/MSc students with DMI/ECMWF collaborations Professional Roles : Chairman of BFI Group 28 (Geosciences & Climate) Scientific Advisory Committee member at ECMWF Project lead in EU ENSEMBLES and PEGASOS initiatives

Zhanhao ZhangView profile

Research Fellow

• Model Predictive Control
• Process Control
• Time-Delay Systems
• +3 more

Zhanhao Zhang is a Postdoc researcher at the Department of Applied Mathematics and Computer Science (DTU Compute) of the Technical University of Denmark, affiliated with the Scientific Computing Center for Energy Resources Engineering. His work centers on developing advanced control strategies for industrial processes with emphasis on sustainability and computational efficiency in energy-intensive sectors. His research specializes in Model Predictive Control (MPC) for time-delay systems, numerical discretization methods, and real-time implementation of cyber-physical control systems. Key application domains include zero-emission industrial processes, cement production blending operations, and oil/gas zero-discharge systems, where he bridges theoretical control algorithms with practical industrial constraints through close industry collaboration. Analysis of his 2022-2024 publications reveals consistent innovation in extending MPC methodologies to handle complex industrial realities like time delays and discontinuities while targeting environmental sustainability. His work demonstrates strong integration of computational mathematics with process engineering to solve critical challenges in carbon-intensive industries. Zhang completed his PhD project "Model Predictive Control for Zero Emission Industrial Processes" (2021-2024) under Professor John Bagterp Jørgensen at DTU, resulting in his doctoral thesis and multiple high-impact publications. This industry-collaborative project focused on developing control frameworks for decarbonizing industrial operations. As an active member of DTU's Scientific Computing Center for Energy Resources Engineering, he contributes to interdisciplinary research that applies high-performance computing and mathematical modeling to optimize energy resource extraction, processing, and utilization while reducing environmental impact.

• Condensed Matter Physics
• Superconductivity
• Magnetic Materials
• +3 more

Morten Holm Christensen is an Assistant Professor at the Physics of Ice, Climate and Earth department of the Niels Bohr Institute , University of Copenhagen. His work focuses on condensed matter physics, particularly in superconductivity, magnetic materials, and quantum phenomena in complex electronic systems. His research explores unconventional superconductivity in materials such as kagome metals and iron-based pnictides, with a focus on phase transitions, electronic correlations, and the interplay between magnetism and superconductivity. Key topics include time-reversal symmetry-breaking superconductivity, orbital-selective electron-phonon coupling, and nematic fluctuations in superconductors. Recent studies highlight tunability of superconducting properties through strain engineering, theoretical modeling of charge density wave instabilities, and topological superconductivity induced by magnetic textures. His work bridges experimental observations with advanced theoretical frameworks, contributing to the understanding of exotic quantum phases in materials. No scientific awards are explicitly listed in the provided information. Morten’s research is supported by the Niels Bohr Institute’s interdisciplinary environment, leveraging expertise in condensed matter physics and materials science.

Irena VodenskaView profile

Professor

• Network Theory in Finance
• Systemic Risk Modeling
• Complexity Science in Macroeconomics
• +12 more

Irena Vodenska is Professor of Finance and Director of Finance Programs at Boston University’s Metropolitan College, Department of Administrative Sciences. She holds a PhD in statistical finance and an MA in economics from Boston University, an MBA from Vanderbilt University, and a BS in computer information systems from the University of Belgrade. She is also a Chartered Financial Analyst (CFA) charter holder. Her research is at the intersection of finance, complexity science, and artificial intelligence, focusing on systemic risk modeling, ESG investments, and financial network dynamics. She has led major interdisciplinary research projects funded by the National Science Foundation, the European Commission, and the U.S. Army Research Office. PhD, Statistical Finance – Boston University MA, Economics – Boston University MBA – Owen Graduate School of Management, Vanderbilt University BS, Computer Information Systems – University of Belgrade Dr. Vodenska’s research interests include network theory in finance, systemic risk propagation, AI-powered ESG analysis, cryptocurrency price forecasting, and financial regulation. She employs big data, machine learning, and natural language processing to analyze financial news, market dynamics, and corporate sustainability. Her work investigates how climate disinformation spreads via social networks and influences public policy and governance. The recent articles highlight a consistent focus on modeling financial and economic systems using network science and AI. Trends include systemic stress testing, sentiment analysis in financial markets, cascading failures, and the interplay between macroeconomic indicators and financial networks. Her work spans econophysics, behavioral finance, public health economics, and ethical AI in fintech. National Science Foundation (NSF) research grant (2023) NSF EAGER Award (2014–2015) European Commission FET Open Grant (2012–2014) U.S. Army Research Office (ARO) Grant (2020–2021) MEXT Post-K Computer Grant, Japan (2016–2019) Alexander Hamilton Fulbright Fellowship (1994) Owen Graduate School Fellowship (1995–1996) Dr. Vodenska teaches core finance courses such as Investment Analysis and Portfolio Management, Derivatives Securities, and Financial Regulation and Ethics. She co-developed the MET AD 678 course with Professor Tamar Frankel from BU Law, emphasizing real-world case studies and ethical decision-making. Her research grants have supported innovative work in systemic risk modeling, AI for ESG, and financial network stability. She is actively involved in mentoring, conference organization, and editorial roles in leading journals. She is a key organizer of the International School and Conference on Network Science (NetSci) and the Big Data in Economics, Science, and Technology (BEST) Conference. Her lab and research team focus on complexity in financial systems, bringing together economists, physicists, computer scientists, and data analysts to study global financial stability and sustainability.