جست‌وجوی استاد و پژوهشگر

Andrea Crovettoمشاهده پروفایل

دانشیار

• Photovoltaics
• Solar Energy Conversion
• Thin Film Technology
• +۳ مورد دیگر

Andrea Crovetto is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the National Centre for Nano Fabrication and Characterization and the Department of Nanofabrication. His research focuses on advanced materials for photovoltaic applications, including solar cell technologies, thin films, and semiconductor materials. His work contributes to UN Sustainable Development Goals related to affordable and clean energy. Key research interests include photovoltaic materials discovery, tandem solar cell design, and semiconductor characterization. Notable achievements include developing monolithic selenium/silicon tandem solar cells and pioneering studies on phosphosulfide semiconductors. He has authored over 90 publications and datasets, including high-impact articles in journals like JPhys Energy and PRX Energy . Dr. Crovetto has received the Young Scientist Award (2016) and actively supervises PhD students in projects such as Experimental Discovery of Phosphosulfide Materials for Solar Cells and Thiophosphate Thin Films for Quantum Technology . His research also involves collaborations on material synthesis, computational modeling, and device fabrication. He has presented at international conferences, including talks on monolithic tandem solar cells and materials discovery methodologies. His lab, based at DTU’s Produktionstorvet , integrates experimental and theoretical approaches to advance sustainable energy technologies.

Anders Koed Madsenمشاهده پروفایل

استاد

• Digital Methods
• Urban Belonging
• Smart Cities
• +۱۲ مورد دیگر

Anders Koed Madsen is a Professor at the Department of Culture and Learning, Faculty of Humanities and Social Sciences, Aalborg University. He is affiliated with the Technoanthropological Laboratory and leads research initiatives including AI for the People and MASSHINE. His work centers on digital methods, urban belonging, smart cities, and citizen science, often integrating ethnographic and participatory approaches. His research interests lie at the intersection of technology and society, focusing on digital placemaking , urban sensing , pragmatist philosophy , and inclusive urban planning . He explores how digital tools can be used to understand lived experiences in cities, especially among marginalized communities. His methodological focus includes participatory digital methods, geospatial photovoice, and computational ethnography. The recent articles highlight a consistent trend toward using digital and ethnographic methods to study urban life, technology assessment, and democratic participation. Themes include generative AI , digital epistemology , friction in machine reasoning , and inclusive city planning . His work increasingly incorporates large language models and open-source toolkits to democratize research and planning processes. Scientific Awards: European Union Prize for Citizen Science (2023) World Summit Awards (WSA) shortlist, Urban Belonging Project (2024) Ziman Award 2020: TANTlab AAU Talent (2018) Videnskabsministeriets EliteForsk-rejsestipendium (2011) Anders Koed Madsen has been actively involved in research grants and collaborative projects such as the Urban Belonging Project , Digital Placemaking and Soft City Sensing Research Network , and GE-AI: Generative Ethnographic AI . He frequently collaborates with interdisciplinary teams and institutions like IT University of Copenhagen and Gehl Architects. He advises on public engagement and has contributed to national and international discourse on digitalization and urban futures. He is a core member of the Technoanthropological Laboratory , which fosters interdisciplinary research on technology and society. The lab supports initiatives in digital methods, citizen science, and urban innovation. Madsen also contributes to public understanding through media engagement and workshops, promoting humanistic perspectives in technological development.

• Perception Engineering
• Extended Reality
• Virtual Reality
• +۷ مورد دیگر

Mark Bo Jensen is an Assistant Professor (tenure track) at the Department of Engineering Technology and Didactics, Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research is centered on Perception Engineering and Extended Reality technologies, particularly Virtual Reality (VR), with applications in human cognition, computer graphics, and scientific visualization. His research interests lie at the intersection of engineering and cognitive sciences, focusing on creating immersive and convincing extended reality experiences. Jensen applies his over 10 years of expertise in real-time computer graphics to advance VR systems for perception modeling, geometric data visualization, and material appearance simulation. His work contributes to fields such as medical diagnostics, 3D annotation, and photorealistic rendering. The recent publications highlight a strong trend in leveraging VR for scientific tasks, such as anatomical landmark annotation and visual field testing, as well as advancing core graphics techniques like meshlet optimization and diffusion-based stereo image generation. His research integrates computer vision, graphics algorithms, and human-centered design. While no scientific awards are currently listed, his active participation in research projects and consistent publication output indicate a growing academic profile. He has contributed to interdisciplinary collaborations involving medical, biological, and engineering domains. Jensen has been involved in advising and research projects, including serving as a PhD student in the 'Virtual Reality-Based Visualization of Geometric Data' project and currently as a project participant in 'AL-EYE: The Visual Aid'. These projects reflect his focus on applied VR solutions and data understanding. His work is conducted within the Energy Technology and Computer Science division at DTU, where he contributes to advancing perception-driven technologies and their practical implementation in scientific and medical contexts.

• Physics of Climate
• Climate Dynamics
• Turbulence
• +۹ مورد دیگر

Peter D. Ditlevsen is a Professor at the Niels Bohr Institute , University of Copenhagen, specializing in Physics of Ice, Climate and Earth (PICE) . With a background in theoretical physics, he transitioned to climate dynamics and turbulence. Dr. Scient (2004), University of Copenhagen PhD (1991), Technical University of Denmark Research Interests : Focuses on Tipping Points in the Earth System , especially AMOC collapse , using stochastic dynamical systems , alpha-stable processes , and nonlinear climate modeling . His work bridges climate physics , dynamical meteorology , and time series analysis . Recent Publications : 2025 work on ice-core-based Dansgaard–Oeschger event modeling , 2024 studies on AMOC multistability and complex system predictability , and 2023 Nature Communications paper on AMOC collapse early warning (cited 4000+ times in media). Scientific Leadership : Leads CriticalEarth H2020 (2021-24) and contributed to TiPES (2019-23). Holds Carlsberg Fellowship and Ole Rømer Prize . Outreach : Produces weekly climate science podcast with David Trads, delivers 4-6 public lectures/year, and has appeared in 40+ media outlets. Teaches Electrodynamics , Thermodynamics , and Turbulence courses.

Emmanouil Vasilomanolakisمشاهده پروفایل

دانشیار

• Cybersecurity
• Internet of Things
• Network Security
• +۳ مورد دیگر

Emmanouil Vasilomanolakis is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). He specializes in Cybersecurity Engineering, focusing on areas such as honeypot technology, cyber deception, IoT/OT security, and network security. His research contributes to the UN Sustainable Development Goals related to innovation and infrastructure. His research interests include: Cyber deception techniques and honeypot design for detecting and mitigating cyber attacks Security of Internet of Things (IoT) and Operational Technology (OT) devices, particularly their exposure to cyber threats Analysis of botnets and cybercrime activities on darkweb markets Social engineering attacks and human aspects of cybersecurity Development of tools for vulnerability analysis and network traffic monitoring His recent publications explore advanced cyber-deception strategies, the security of exposed IoT/OT devices, and the analysis of cybercrime activities. Key areas include device identification, vulnerability detection in industrial systems, and the impact of social engineering in cybersecurity. He currently supervises several PhD students including Kasper Elzer, António Cordeiro Urbano, Aigerim Safargalieva, Davide Maddaloni, and collaborates on projects like "Advanced cyber-deception techniques" and "Defending legacy and modern networks with cyber-deception". His research is supported by grants focused on collaborative security and deception-based defenses. He is part of research teams developing honeypot technologies and cyber deception frameworks, contributing to datasets like the hybrid IoT/OT honeypot collection.

Fariba Karimiمشاهده پروفایل

استاد

• Algorithmic Fairness
• Computational Social Science
• Network Science
• +۷ مورد دیگر

Fariba Karimi is a Professor of Social Data Science at Graz University of Technology and leads the Algorithmic Fairness research group at the Complexity Science Hub (CSH) in Vienna. Her work sits at the intersection of computational social science, network science, and AI ethics, with a strong focus on fairness, inequality, and bias in algorithmic systems. She holds a PhD in Physics and Computational Science from Umeå University, Sweden (2015), and was a Postdoctoral Researcher at GESIS – Leibniz Institute for Social Sciences, Germany. Her research combines large-scale data analysis, agent-based modeling, and network science to study how social structures and algorithms interact to shape disparities, particularly for underrepresented groups. Computational Social Science Algorithmic Fairness and Bias Network Science and Homophily Digital Humanism Urban Inequality and Implicit Bias Gender and Age Equity in Academia Her recent publications reveal a consistent focus on understanding and mitigating algorithmic and structural inequities. Themes include the visibility of minorities in networks, the impact of segregation on health and cognition, gender citation gaps in physics, and the development of fair ranking and recommendation systems. She employs interdisciplinary methods to analyze both digital and real-world social systems. Fariba Karimi has received several prestigious recognitions, including: Young Scientist Award from the German Physical Society (2023) ERC Starting Grant (2024) Nomination for the Hedy Lamarr Award (2021) She leads the 'Humanized Algorithms' project at CSH, funded by the ERC, and co-leads an EU Horizon project (MAMMOth) on multi-criteria fairness in AI. She advises PhD researchers such as Lisette Espín-Noboa and collaborates with institutions like the University of Mannheim and the Centre for Social Sciences. Her work contributes to both academic knowledge and practical solutions for fairer AI systems and more equitable societies.

Renaud Lambiotteمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Temporal Networks
• +۷ مورد دیگر

Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

Jes Frellsenمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Generative AI
• Deep Learning
• +۵ مورد دیگر

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 .

Thomas Ebelمشاهده پروفایل

استاد

• Power Electronics
• High-Voltage Electrical Engineering
• Dielectric Materials
• +۳ مورد دیگر

Thomas Ebel is Professor and Head of the Centre for Industrial Electronics at the University of Southern Denmark (SDU) , Institute of Mechanical and Electrical Engineering. A leading expert in power electronics, high-voltage engineering and capacitor technology, he directs large, multi-partner research projects and teaches/supervises at both graduate and PhD levels. Education & Career Path Prof. Ebel holds the academic title Dr. rer. nat. and has been appointed full Professor at SDU. He concurrently serves as Head of Section at the Centre for Industrial Electronics, orchestrating cross-disciplinary research teams and infrastructure. Research Interests Power Electronics & Power Conversion: advanced converter topologies, WBG devices (GaN, SiC), high-frequency magnetics, grid-forming control. Dielectric Materials & Capacitors: polymer and hybrid nanocomposite dielectrics, self-healing metallized film capacitors, aluminium electrolytic capacitors, lifetime modelling and reliability. High-Voltage Engineering & Breakdown Physics: breakdown mechanisms in nanocomposites, corona and partial discharge, insulation coordination. IoT & Data-Driven Monitoring: real-time condition monitoring, digital twins, data-driven RUL estimation for power components. Publication Trends Across 133 research outputs (2018-2025) the dominant themes are (i) construction and reliability of 700 V-class aluminium polymer electrolytic capacitors, (ii) GaN-based power converter optimisation, (iii) hybrid AC/DC microgrid control and harmonic mitigation, and (iv) nanocomposite dielectrics for next-generation capacitors. The 15 most recent articles (2025) reinforce these directions while adding socio-technical energy analytics and green-vehicle powertrains. Scientific Awards Tek Innovation Prize 2023 – awarded for outstanding contributions to power electronics research and industrial innovation. Advising & Funding Prof. Ebel currently supervises ~10 PhD candidates and post-docs including L. Tavares, M. A. Khan, R. Maheshwari, S. Mateen, A. N. Pinky and others. He is Principal Investigator or Head Coordinator of six active projects (2024-2027) valued at >€8 M, spanning ultra-high-efficiency drives, hydrogen-PtX converters, self-healing capacitors and hybrid power-plant concepts. Laboratory & Teams He heads the High-Voltage Power Electronics Laboratory at SDU, equipped with 700 V/200 A capacitor test rigs, GaN/SiC converter prototyping benches, and environmental chambers for accelerated ageing studies. The centre collaborates with 20+ industrial partners and coordinates the international IEA Wind Task 50 on hybrid power plants.

Klaus Mosthafمشاهده پروفایل

دانشیار

• Contaminant Transport in Porous Media
• Groundwater Contamination
• Numerical Modeling
• +۱۰ مورد دیگر

Klaus Mosthaf is an Associate Professor in the Department of Environmental and Resource Engineering at the Technical University of Denmark (DTU), where he conducts research on contaminant transport and numerical modeling in porous and fractured media. His work focuses on protecting groundwater resources through advanced modeling of contamination from substances like PFAS, chlorinated solvents, and pesticides. He is actively involved in teaching and academic leadership, coordinating the Nordic Masters program Enviro5Tech and the Sino-Danish Center module on Pollutants and Pollution Control. Research Interests: Transport processes in porous media Groundwater contamination and remediation Numerical modeling of reactive transport Aquifer Thermal Energy Storage (ATES) Fractured and variably saturated geologies Coupled porous-medium and free-flow systems His recent publications reveal a strong focus on PFAS fate, bioremediation in ATES systems, and tracer-based characterization of glacial tills. He employs tools like COMSOL Multiphysics, Python, and FEFlow to develop predictive models grounded in laboratory and field data. Scientific Contributions: Active contributor to over 60 publications in hydrogeology and environmental engineering Key developer of models for coupled processes in subsurface systems Organizer and speaker at international conferences on multiphase flow and contaminant transport Advising and Grants: Klaus Mosthaf supervises multiple PhD students and has coordinated significant research projects, including those on PFAS transport and bioremediation. He is the main supervisor of two active PhD projects and has previously co-supervised two others. His leadership extends to board membership in the Danish Academy of Technical Sciences on Soil and Groundwater (ATV Jord og Grundvand), where he fosters collaboration among academia, consultants, and public authorities. Laboratories and Research Groups: His work is conducted within DTU Sustain, a leading research environment in environmental engineering, where he collaborates with experts in hydrogeology, bioremediation, and sustainable technologies. He is deeply integrated into a network of national and international collaborators focused on groundwater protection and sustainable subsurface utilization.

Didier Sornetteمشاهده پروفایل

استاد

• Complex Systems
• Risk Analysis
• Financial Crises and Bubbles
• +۱۱ مورد دیگر

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.

Maria Del Rio-Chanonaمشاهده پروفایل

استادیار

• Network Science
• Agent-Based Modeling
• Large Language Models
• +۷ مورد دیگر

Maria Del Rio-Chanona is an Assistant Professor in the Department of Computer Science at University College London (UCL), where she conducts interdisciplinary research at the intersection of network science, machine learning, and economic modeling. She is also a member of the External Faculty at the Complexity Science Hub since January 2025, reflecting her ongoing engagement with complex systems research. Her academic background includes a PhD in Mathematics from the University of Oxford, where she was part of the Institute for New Economic Thinking at the Oxford Martin School, and undergraduate studies in Physics at UNAM, Mexico. Her research interests center on understanding socioeconomic transformations using computational methods. She specializes in applying Large Language Models (LLMs), Agent-Based Models (ABMs), and network analysis to study the impacts of generative AI, the net-zero transition, and global crises like the COVID-19 pandemic on labor markets, economic resilience, and public discourse. Her work often involves analyzing large-scale digital trace data, including online labor platforms and social media, to uncover behavioral and structural shifts. The recent publications reveal a consistent focus on labor market dynamics in the face of technological disruption. Her work examines how generative AI reshapes demand for freelance skills, reduces public knowledge sharing on platforms like Stack Overflow, and influences mental health discourse in relation to employment decisions. She also investigates broader economic modeling, including pandemic shock propagation and employment transitions during decarbonization. Among her scientific recognitions is the Emerging Scientific Award from the Complex Systems Society in 2023. She has collaborated with international policy organizations such as the International Monetary Fund (IMF) and the International Labour Organisation (ILO), underscoring the policy relevance of her research. Maria Del Rio-Chanona has held prestigious research positions, including as a James S. McDonnell Foundation (JSMF) Postdoctoral Fellow at the Complexity Science Hub and a Visiting Fellow at the Growth Lab, Harvard Kennedy School. These roles have enabled her to lead and contribute to high-impact interdisciplinary projects. While no current advisees are listed, she actively engages with PhD opportunities and interdisciplinary research networks. She is involved in multiple research initiatives, including the development of HuBERT, an NLP algorithm for extending the Seshat Global History database, and projects studying labor transitions in net-zero scenarios. Her work on the HiST-LLM benchmark for historical knowledge in LLMs highlights her innovative integration of AI with social science.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۵ مورد دیگر

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.

Allan Hanburyمشاهده پروفایل

استاد

• Information Retrieval
• Data Mining
• Natural Language Processing
• +۱۳ مورد دیگر

Allan Hanbury is a Full Professor for Data Intelligence at the Faculty of Informatics, TU Wien, and a faculty member at the Complexity Science Hub Vienna. He leads the Data Science Research Unit and serves as the Faculty Representative for financial affairs and internationalization. He holds a PhD in Applied Mathematics from Mines ParisTech and a Habilitation in Practical Informatics from TU Wien. PhD in Applied Mathematics, Mines ParisTech, 2002 Habilitation in Practical Informatics, TU Wien, 2008 Bachelor’s and Master’s in Physics and Applied Mathematics, University of Cape Town His research focuses on information retrieval, data mining, natural language processing, and information extraction, with applications in healthcare, legal, and patent domains. He has coordinated major EU projects including Khresmoi, VISCERAL, KConnect, and DoSSIER, the latter training 15 PhD students. He is co-founder of contextflow, a spin-off commercializing radiology search technology. His recent publications (2024–2022) highlight a strong trend in systematic literature review automation, neural re-ranking, large language models, and domain-specific information extraction. Key themes include improving citation screening, patient-trial matching, evaluation metrics, and dataset creation for offensive language and legal text. His work combines technical innovation with real-world impact in medical, legal, and scientific communication contexts. Allan Hanbury has received no explicitly mentioned scientific awards in the provided text. He actively supervises numerous PhD and master’s students and leads large research projects such as DoSSIER, Transparent Automated Content Moderation, and PLFDoc, funded by FWF, WWTF, and EU. His group develops tools for evidence synthesis, clinical data extraction, and legal document analysis. He also contributes to AI and data strategy in Austria and Europe. He leads the Data Science Research Unit at TU Wien and is involved in multiple interdisciplinary projects including BRISE (building regulation analysis), CDL-RecSys, and TACo, focusing on legal and scientific document processing. His work bridges academia and industry through spin-offs like contextflow and collaborations with Deutsche Telekom, Siemens, and FMA.

Henrik Jeldtoft Jensenمشاهده پروفایل

استاد

• Complexity Science
• Statistical Mechanics
• Mathematical Physics
• +۷ مورد دیگر

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.