Frede Blaabjerg is a Professor at Aalborg University (AAU Energy) , affiliated with the Faculty of Engineering and Science . Since 1998, he has pioneered power electronics research in applications such as wind turbines , photovoltaic (PV) systems , reliability engineering , and Power-2-X technologies. Education : PhD in Electrical Engineering (1995, Aalborg University) Honorary Degrees : Honoris Causa at University Politehnica Timisoara (2017) and Tallinn Technical University (2018) His research focuses on power electronics control , system optimization , and reliability for renewable energy and electric mobility . Recent work includes grid-forming converters , virtual synchronous generators , and smart EV charging systems. Key publication trends span 15+ years , with over 3,733 peer-reviewed articles and 900+ journal papers in power electronics , renewables , and energy storage . Notable book series: Control of Power Electronic Converters and Systems (4 volumes, Elsevier). Scientific Awards : 46 IEEE Prize Paper Awards 2020 IEEE Edison Medal 2019 Global Energy Prize 2014 IEEE William E. Newell Power Electronics Award Leadership Roles : Editor-in-Chief, IEEE Transactions on Power Electronics (2006–2012) Chairman, Danish Council for Research and Innovation Policy (2020–) President, IEEE Power Electronics Society (2019–2020)
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.
Erik Bjørnager Dam is a Professor in the Machine Learning section at the Department of Computer Science, University of Copenhagen (UCPH). His research spans theoretical foundations of machine learning to practical applications in medical data analysis, sustainability, and materials science. His key research interests include: Small-scale and resource-efficient deep learning Medical image analysis and segmentation Sustainable and environmentally conscious AI development Graph neural networks for materials science Resource-constrained AI systems Professor Dam's recent publications demonstrate a strong focus on making AI more accessible and sustainable while maintaining high performance standards. His work on 'Performance Per Resource Unit' metrics addresses critical challenges in deploying AI in resource-limited environments, particularly in healthcare applications. His research bridges theoretical machine learning with practical implementations across multiple domains. His notable professional activities include: Co-founding Cerebriu A/S (since 2018) Co-founding Biomediq A/S (since 2008) Delivering lectures on AI's role in green transition (April 24, 2023) Media contributions on deep learning applications in plant research (September 13, 2018) With 74 documented research outputs, Professor Dam maintains an active research profile with significant contributions in 2023-2025 across medical imaging, sustainable AI, and materials science applications.
Jes Frellsen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU) since 2016. Previously, he held academic positions at the IT University of Copenhagen (2016-2019), postdoctoral roles at University of Cambridge (2013-2016) and University of Copenhagen (2011-2013). Education: PhD in Bioinformatics (2011), University of Copenhagen MSc in Bioinformatics (2007), University of Copenhagen BSc in Mathematics and Computer Science (2005), University of Copenhagen EAP Exchange at University of California, Santa Cruz (2004-2005) Research Focus Jes Frellsen specializes in statistical machine learning , particularly generative AI and deep generative models with applications in bioinformatics . His work integrates Bayesian inference , directional statistics , and Markov chain Monte Carlo methods to address challenges in macromolecular structure prediction and missing data imputation . Recent efforts explore uncertainty quantification in image segmentation and generative modeling for materials science. Advising & Collaborations He actively supervises PhD students and postdoctoral researchers in projects spanning news recommendation systems , medical imaging , and 3D structure generation . Collaborations include work with Zoubin Ghahramani (Cambridge) and Thomas Hamelryck (Copenhagen), with contributions to protein structure prediction and statistical methods in structural bioinformatics .
Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.
Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at the Central European University in Vienna, and a Research Professor at the HUN-REN Alfréd Rényi Institute of Mathematics in Budapest. He leads the Computational Human Dynamics Lab, focusing on data-driven modeling of social and biological systems. He is also the Editor-in-Chief of the journal Advances in Complex Systems . His research interests lie at the intersection of network science, human dynamics, and socioeconomic systems. He specializes in temporal and spatial networks, modeling contagion processes (both social and biological), and analyzing large-scale human behavioral datasets. His work integrates computational methods with real-world data to understand complex social phenomena such as mobility patterns, migration, segregation, and epidemic spread. He is particularly known for using remote sensing and digital trace data to infer poverty and socioeconomic conditions in urban areas. The recent publications highlight a strong trend in applying network science and machine learning to societal challenges. His work spans high-impact journals in complex systems, data science, and computational social science, with recurring themes in epidemic modeling, urban analytics, socioeconomic inference, and the structure of temporal and spatial networks. The research is highly interdisciplinary, combining physics, computer science, and social science methodologies. He has been invited to speak at major events such as the Conference on Complex Systems, the Lake Como School on Complex Networks, and workshops on data for vulnerability assessment. He served as general co-chair of CCS 2021 in Lyon, demonstrating leadership in the complexity science community. General Co-Chair, Conference on Complex Systems (CCS) 2021, Lyon Invited speaker, 4th Workshop on Data for the Wellbeing of the Most Vulnerable @ ICWSM'23 Invited speaker, Complexity72h Workshop Invited lecturer, Lake Como School on Complex Networks Invited talk, Hungarian Academy of Sciences on COVID-19 modeling While specific grant details are not listed, his coordination of projects on segregation, migration, and poverty inference—often in collaboration with the Complexity Science Hub—suggests active involvement in externally funded interdisciplinary research. He advises students through the Department of Network and Data Science at CEU, though specific advisees are not named. His lab, the Computational Human Dynamics Lab, serves as a hub for data-driven research on social systems.
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.
Henrik Jeldtoft Jensen is a Professor of Mathematical Physics and leads the Centre for Complexity Science at Imperial College London. His work spans multiple disciplines, focusing on the statistical mechanics of complex systems, with applications in physics, biology, neuroscience, and finance. Professor, Mathematical Physics Leader, Centre for Complexity Science Institution: Imperial College London His research interests lie at the intersection of theoretical physics and complex systems. He is best known for developing the Tangled Nature Model of evolving ecosystems, which has been extended into financial modeling through the Tangled Finance approach. His work in brain dynamics involves analyzing fMRI and EEG data using tools from statistical physics. He has made significant contributions to self-organized criticality and stochastic dynamics of complex systems, particularly in condensed matter and evolutionary contexts. The recent publications reflect a strong trend toward interdisciplinary complexity science, integrating concepts from physics, biology, economics, and neuroscience. Keywords across these works include complexity, statistical mechanics, dynamical systems, and network theory, with subfields ranging from neural avalanches to financial instability and biodiversity modeling. Henrik Jensen is the author of two influential books: Self-Organized Criticality and Stochastic Dynamics of Complex Systems (with Paolo Sibani), which have been widely cited across disciplines. He has supervised numerous PhD and postdoctoral researchers through the Centre for Complexity Science, though specific names are not listed. His research has been supported by grants from UK research councils and international collaborations, particularly in interdisciplinary complexity projects. He is affiliated with the Centre for Complexity Science, a multidisciplinary research hub at Imperial College London that brings together physicists, mathematicians, biologists, and social scientists to study complex adaptive systems.
Christian Kühn is a Professor of Multiscale and Stochastic Dynamics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He has been an External Faculty member at the Complexity Science Hub Vienna since 2017, reflecting his interdisciplinary engagement in complex systems research. His academic background includes a BSc in Mathematics from Jacobs University Bremen (2005), an M.A.St. from the University of Cambridge (2006), and a PhD in Applied Mathematics from Cornell University (2010). He held postdoctoral positions at the Max Planck Institute for the Physics of Complex Systems in Dresden and the Vienna University of Technology, where he also served as an APART-Fellow and Leibniz Fellow. Christian Kühn's research lies at the intersection of differential equations, dynamical systems, and mathematical modeling. He focuses on multiscale problems, the impact of noise and uncertainty in deterministic and stochastic systems, and adaptive networks. Central phenomena of interest include bifurcations, pattern formation, and scaling laws. His work bridges theoretical developments with applications in epidemiology, neuroscience, and complex network dynamics. His recent publications (2021–2024) reflect a strong trend in analyzing nonlinear and stochastic dynamics on networks, with applications ranging from epidemic modeling to synchronization and critical transitions. Key themes include explosive phenomena, adaptive network behavior, moment closure methods, and non-Markovian systems, demonstrating a consistent focus on foundational aspects of dynamical systems with practical relevance. Notable scientific awards include: Richard-von-Mises Prize, GAMM (2017) Lichtenberg Professorship, VolkswagenStiftung (2016) Best Paper Award, TU Vienna (2014) Leibniz Fellow, Oberwolfach (2013) APART-Fellow, Austrian Academy of Sciences (2012) While specific details about advised students are not provided, his role as a full professor and active researcher suggests involvement in mentoring graduate students and postdoctoral researchers. His work has been supported by prestigious grants such as the Lichtenberg Professorship. He leads research in multiscale and stochastic dynamics, contributing to both theoretical advances and interdisciplinary applications. Kühn is part of vibrant research environments at TUM and the Complexity Science Hub Vienna, collaborating with leading scientists in network science, applied mathematics, and complex systems. His work continues to advance the understanding of critical transitions and nonlinear behavior in high-dimensional and stochastic systems.
Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.
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.
Shuai Zhao is an Assistant Professor at the AAU Energy Department, Faculty of Engineering and Science, Aalborg University. His research focuses on applying machine learning and artificial intelligence techniques to enhance reliability and condition monitoring in power electronic systems, with specific interests in lifetime estimation, fault diagnosis, and health management of critical components like capacitors and semiconductor devices. Institution: Aalborg University School: Faculty of Engineering and Science Department: AAU Energy Email: szh@energy.aau.dk His research spans multiple domains including: Physics-informed machine learning for power converter systems Remaining useful life prediction with hybrid Bayesian deep learning Thermal transient analysis and stress emulation methods IoT-enabled monitoring schemes for semiconductor devices Neural network applications in lithium-ion battery prognostics Recent publications show a strong trend toward integrating domain-specific physics with machine learning frameworks to address real-world challenges in: Power electronics reliability under operational stress Anomaly detection in multivariate time-series data Robust fault diagnosis for railway traction systems Temperature estimation in electric vehicle motors Imbalanced data handling in diagnostic systems Capacitance degradation modeling under environmental factors Current projects demonstrate collaboration with leading institutions on: AI-assisted long-term maintenance strategies Physics-informed neural network architectures Smart agricultural monitoring systems via IoT platforms Advanced particle filter methods for life prediction
Peter C. Petersen is an Associate Professor in the Department of Neuroscience within the Faculty of Health and Medical Sciences at the University of Copenhagen. Holding a Civilingeniør (MSc) in Technical Physics from DTU and a PhD in Neuroscience, he specializes in systems-level neural mechanisms using electrophysiological approaches. His educational background includes: Civilingeniør (MSc) in Technical Physics, DTU PhD in Neuroscience Petersen's research focuses on neural dynamics in memory and motor systems, combining in vivo electrophysiology with computational modeling. He investigates hippocampal place cells for spatial working memory and rotational dynamics in spinal cord networks, while developing neurotechnology tools like CellExplorer for single-neuron analysis. His work bridges experimental neuroscience, engineering, and data science to decode circuit-level computations. Recent publications (2020-2024) reveal a dual emphasis on hippocampal memory mechanisms (e.g., temperature effects on sharp wave ripples) and innovative methodology (e.g., 3D-printed microdrives). This trajectory demonstrates consistent advancement from tool development to fundamental discoveries in neural coding, with increasing collaboration intensity as evidenced by multi-institutional authorship. Scientific awards: No specific awards were documented in the source material. While explicit advising details are absent, his leadership in software/hardware development (CellExplorer, microdrive systems) implies active mentorship of technical researchers. Grant information isn't specified, though high-impact publications suggest sustained funding for neurotechnology and systems neuroscience projects. Petersen directs the Petersen Lab (https://petersenlab.org/), which employs chronic electrophysiology in rodent models to study memory and movement. The lab maintains strong ties with the Buzsáki lab (hippocampal research) and continues collaborations initiated during his NYU Langone Health tenure (2016-2022), reflecting an integrated approach to neural circuit analysis across institutions.
Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.
