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Amartya Sanyal is a Tenure Track Assistant Professor at the Department of Computer Science (DIKU), University of Copenhagen, specializing in Machine Learning. He also serves as an Adjunct Professor at the Indian Institute of Technology Kanpur (2023–2025). His research focuses on critical areas of AI safety, data privacy, and robust learning. University: University of Copenhagen Department: Department of Computer Science Academic Rank: Assistant Professor Adjunct Role: IIT Kanpur (2023–2025) His work addresses challenges like differential privacy , data poisoning attacks , machine unlearning , and robust mixture learning . Recent publications analyze privacy-preserving techniques for large language models, fairness in collective action algorithms, and certified data release mechanisms. Amartya has received the Villum Young Investigator Award (2025). His research outputs emphasize online learning , adversarial robustness , and privacy-utility tradeoffs through rigorous theoretical frameworks and practical implementations. Scientific Award: Villum Young Investigator Award His collaborations span institutions like IIT Kanpur and involve interdisciplinary projects with industry partners. Current activities include talks on privacy with correlated data and machine unlearning advancements.

• Machine Learning
• Data Privacy
• Robust Learning
• +3 more

Alessandra Meddis serves as an Assistant Professor in the Section of Biostatistics within the Department of Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences. Her academic work centers on developing and applying advanced statistical methodologies for longitudinal and time-to-event data analysis, with significant contributions to public health research in Denmark and internationally. Her institutional affiliation is clearly established through university contact details and departmental listings. Her primary research interests include correlated survival data analysis, competing risk modeling, informative cluster size methodology, causal inference techniques for observational studies, and environmental epidemiology applications. Dr. Meddis has developed specialized expertise in handling complex survival data structures while maintaining focus on real-world public health problems, particularly in HIV comorbidity patterns, environmental exposure effects, and pandemic-related mortality analyses. Her methodological innovations directly address challenges in clustered and censored data common across medical research domains. Analysis of Dr. Meddis's recent publication record reveals a consistent trajectory of high-impact interdisciplinary research spanning clinical medicine, epidemiology, and statistical methodology. Her work appears in leading journals across biostatistics, infectious diseases, and public health, demonstrating strong collaborative networks with clinical researchers and epidemiologists. Key thematic areas include HIV treatment outcomes, environmental health exposures, and critical care applications during the pandemic, with recent methodological papers advancing survival analysis techniques for complex data structures. Scientific Awards: No scientific awards were specified in the available institutional profile. Advising and Grants: The institutional profile does not provide details regarding graduate student supervision or specific research grant funding. Her collaborative publications suggest involvement in multi-investigator projects including the COCOMO HIV cohort study and pandemic-related research initiatives. Labs and Teams: Dr. Meddis is affiliated with the Section of Biostatistics within the Department of Public Health, though specific laboratory facilities or dedicated research teams are not described in the source material. Her extensive co-authorship patterns indicate active participation in multiple research consortia across medical specialties.

• Longitudinal Data Analysis
• Time-to-Event Analysis
• Survival Analysis
• +5 more

Diego Garlaschelli is Professor of Theoretical Physics at the IMT School for Advanced Studies in Lucca, Italy, and at the Lorentz Institute for Theoretical Physics, University of Leiden, the Netherlands. He leads the NETWORKS research unit at IMT and the Econophysics and Network Theory group at Leiden. He is also an external faculty member at the Complexity Science Hub in Vienna and an associate member of the Enrico Fermi Research Center in Rome. His affiliations reflect a strong international and interdisciplinary research profile in network science and statistical physics. He holds a master's degree in theoretical physics from the University of Rome III (2001) and a PhD in Physics from the University of Siena (2005). His postdoctoral experience includes positions at the Australian National University, the University of Siena, the University of Oxford, and the Sant’Anna School of Advanced Studies in Pisa. Garlaschelli’s research spans network theory, statistical physics, econophysics, financial complexity, ecological networks, and social dynamics. He applies maximum entropy models, information theory, and random graph frameworks to understand complex real-world systems. His teaching includes courses in Network Theory, Econophysics, and Complex Systems at both PhD and MSc levels. The 15 most recent publications highlight a consistent focus on network reconstruction, ensemble inequivalence, renormalization, and applications to financial and socio-economic systems. Key themes include statistical inference in networks, resilience, and multi-scale modeling, with publications in top journals such as Nature Reviews Physics , Physics Reports , Science , and Physical Review Letters . His scientific awards include the Best Paper Award at the 6th International Workshop on Self-Organizing Systems (2012) and the Jan Kijne Prize (2013) as supervisor. He has secured multiple grants from NWO, the European Union, and the Royal Society, and has supervised over 40 students at PhD, master’s, and bachelor’s levels. He also mentors postdocs and visiting scientists. Garlaschelli leads and organizes major international workshops and schools in network science and complex systems. He serves on scientific committees and is an active referee for journals like Nature and Physical Review Letters , as well as funding agencies including the ERC and NWO.

• Network Theory
• Statistical Physics
• Econophysics
• +7 more

Ninette Pilegaard is a Professor and serves as Deputy Head of Division and Head of Section for Transport Policy at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). Her academic career spans multiple research domains with significant contributions to transportation policy and economics. Her research interests focus on transportation policy analysis with particular expertise in: Road charging systems and pricing mechanisms Bicycle infrastructure and safety analysis Commuting behavior and accessibility impacts Car ownership and usage patterns Relationship between transportation accessibility and labor market outcomes Dr. Pilegaard's scholarly work demonstrates a strong empirical approach combining transportation engineering with economic analysis. Her recent publications reveal a consistent focus on evidence-based policy evaluation, particularly in the Danish context. She frequently employs quasi-natural experimental methods to assess transportation policy impacts, with particular attention to road pricing, cycling infrastructure, and the economic implications of transportation systems. Her research has been supported by major funding bodies including Innovation Fund Denmark and Forskningsrådsfinansiering, and has resulted in publications in high-impact transportation journals such as Transportation Research Parts A and D, and Journal of Safety Research. As an academic supervisor, Dr. Pilegaard has served as Main Supervisor for PhD projects, including the 'Productivity and agglomeration' project. She has been actively involved in numerous research collaborations both within DTU and with external partners. Her laboratory work focuses on transportation data analysis, particularly utilizing Danish transportation datasets to examine policy impacts. She has developed expertise in analyzing hospital data for traffic safety research and has contributed to methodologies for assessing infrastructure effects on traffic accidents.

• Transportation Policy
• Road Charging Systems
• Bicycle Infrastructure
• +6 more

Carsten Jahn Hansen is an Associate Professor in sustainable place development and planning at Aalborg University's Department of Sustainability and Planning, part of The Technical Faculty of IT and Design. His research focuses on innovations in planning for sustainable place-making, addressing climate change, socio-economic balance, and environmental sustainability. He leads the Planning for Urban Sustainability (PLUS) research group and participates in projects like the PLANNORD research network and EU-funded initiatives such as PlanCoast . Hansen holds a PhD in Spatial Planning and a Master's in Surveying, Planning, and Land Management from Aalborg University. Key affiliations include roles with the Danish Town Planning Institute (Dansk Byplanlaboratorium), ESPON (European Spatial Planning Observatory Network), and OECD/EU policy advisory work. His expertise spans coastal governance, participatory planning, and the integration of cultural heritage into spatial development. He has contributed to over 111 publications, including works on non-mandatory planning frameworks and climate adaptation strategies. Hansen has advised on regional and municipal planning policies, emphasizing trust-based governance and community engagement. His research explores the interplay between strategic planning and grassroots participation, with a focus on urban and rural resilience. Notable contributions include analyses of Denmark's development planning systems and the role of soft planning instruments in addressing complex spatial challenges.

• Sustainable Urban Planning
• Spatial Development
• Climate Change Adaptation
• +3 more

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.

• Theoretical Biophysics
• Complex Systems
• Chaos
• +6 more

Jesper Fels Birkelund is a Tenure Track Assistant Professor at the Department of Sociology, University of Copenhagen. His research focuses on education systems, ethnic inequalities, and social mobility, leveraging advanced statistical methods on register and survey data. He teaches courses such as Basic Statistics, Sociology in Danish Society, and Advanced Welfare, Inequality, and Mobility. His work has been published in journals like Social Forces and European Sociological Review . His research on education examines how schooling impacts cognitive and social-psychological skills, influencing long-term labor market outcomes. He has shown vocational training enhances conscientiousness, yielding earnings comparable to academic tracks. In ethnic inequality studies, he analyzes high aspirations among immigrant students despite poor academic performance, proposing counterfactual models to assess systemic challenges for minority students. In social mobility research, he explores how parental resources (human, cultural, social, economic capital) shape children’s career trajectories, particularly when parents and children share the same field of study. He uses firm linkage data to study mechanisms like parental networks and inherited family businesses. Awards: 2022 ECSR Prize for Best PhD Thesis Teaching: Basic Statistics (BA), Sociology in Danish Society (BA), Education and Social Inequality (BA/MA), Advanced Welfare, Inequality, and Mobility (MA) His work integrates micro-class approaches with intergenerational transmission theories, contributing to debates on educational policy and labor market equity. Office hours for Spring 2025: Monday 15:00–16:00 in room 16.0.57.

• Education Policy
• Ethnic Inequalities
• Social Mobility
• +4 more

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

• Computational Statistics
• Causal Inference
• Machine Learning
• +3 more

Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications

• Algorithms
• Data Structures
• Big Data
• +7 more

Giulio Cimini is Associate Professor of Theoretical Physics in the Department of Physics at the University of Rome Tor Vergata and a Research Associate at the 'Enrico Fermi' Research Center. He is a statistical physicist with a strong interdisciplinary focus on complex networks and their applications in socio-economic systems. His research interests include: Statistical Physics of Complex Networks Reconstruction and Validation of Economic Networks Social Network Interactions and Financial Markets Systemic Risk and Financial Contagion Scientific Success, Fitness, and Complexity Adaptive Social Recommendation Codon Usage Bias and Protein Interaction Networks His recent publications reveal a strong trend in applying statistical physics to real-world networks, particularly in finance and social systems. Key themes include the modeling of systemic risk in supply chains and financial networks, the dynamics of collective action on platforms like Reddit (e.g., the GameStop short squeeze), and the development of network reconstruction methods using maximum entropy and optimal transport frameworks. His work often combines empirical analysis with theoretical modeling. Scientific awards and recognitions include: Associate Editor, Frontiers in Physics – Interdisciplinary Physics Board Member, Network Science Society Member, Council of the Complex Systems Society Steering Committee, CCS/Italy He has advised or collaborated with numerous researchers, particularly in projects related to economic networks and complex systems. His work has been supported by Italian national grants such as PRIN and PNRR. He leads or co-leads research projects including RENet and C2T. His research is conducted within interdisciplinary teams involving physicists, economists, and computer scientists, often in collaboration with institutions like ISC-CNR, IMT Lucca, and the Network Science community.

• Statistical Physics
• Complex Networks
• Economic Networks
• +12 more

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.

• Soft Robotics
• Physical Reservoir Computing
• Physical Deep Learning
• +5 more

Fabio Pierella is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Department of Wind and Energy Systems Flows, specializing in Wind Turbine Design Division. His research focuses on offshore wind energy systems, fluid dynamics, and structural engineering. He has contributed to projects like OC6 Phase IV and the DeRisk database, validating numerical models for floating offshore wind structures and extreme wave loads. Key research interests include computational fluid dynamics (CFD), hydrodynamic load modeling, and the design of large-scale floating wind turbines. His work spans numerical simulations, experimental validation, and database development for extreme sea states. Pierella has presented at international conferences on topics like wave-structure interaction and turbine control systems. He received the Best Poster Presentation Award (2024) and contributed to datasets such as the DeRisk Database, which provides critical wave data for offshore wind turbine design. His research emphasizes practical applications, including monopile structural integrity under extreme loads and control strategies for floating platforms. Pierella's activities include conference presentations on ultra-large floating turbines (EMULF2 project) and the impact of wave shape on 15MW turbine loads. His interdisciplinary approach integrates computational models with experimental results to address challenges in offshore renewable energy systems.

• Offshore Wind Energy
• Fluid Dynamics
• Structural Engineering
• +3 more

Fabio Caccioli is a Professor of Complex Systems at University College London (UCL), where he conducts interdisciplinary research at the intersection of physics, economics, and network science. His research focuses on complex systems and statistical mechanics , with applications to economic and financial systems . Key interests include systemic risk , complex networks , science of science , and non-equilibrium statistical mechanics . His work aims to model and understand emergent phenomena in socio-economic systems using tools from theoretical physics. Fabio Caccioli earned his PhD in Statistical Physics from the International School for Advanced Studies (SISSA) in Trieste, Italy. He previously held research positions at the Centre for Risk Studies at the University of Cambridge and as a Postdoctoral Fellow at the Santa Fe Institute. His career reflects a strong trajectory in complexity science applied to real-world systems, particularly financial networks and risk propagation. Though publication titles are not listed, his ongoing work appears consistently published from 2010 through 2025, indicating sustained scholarly output in his domain. f.caccioli@ucl.ac.uk

• Complex Systems
• Statistical Physics
• Systemic Risk
• +4 more

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.

• Network Science
• Human Dynamics
• Temporal Networks
• +11 more

Jeppe Rich is a Professor at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His primary research focuses on statistical and mathematical modeling applied to transport-related challenges, including transport demand modeling, discrete choice models, freight transport, cost-benefit assessments, and strategic long-term demand models. Rich holds a Mathematical Planning qualification from the University of Aarhus (1989–1995). He has held external positions as a Senior Consultant at Atkins A/S (2001–2002) and as a Researcher at the National Environmental Research Institute (1995–1998). His work aligns with UN Sustainable Development Goals related to sustainable cities and communities (SDG 11) and climate action (SDG 13). His research interests span transport policy, transportation science, and the application of advanced modeling techniques to address urban mobility challenges. Notable areas include EV infrastructure planning, bicycle network optimization, and cost-benefit analysis of transport projects. He supervises several PhD students in topics like electric freight transport, micromobility safety, and urban charging infrastructure. Rich has published extensively on transport policy, demand modeling, and sustainable mobility solutions. His work emphasizes interdisciplinary approaches to solving complex transport challenges, combining engineering, economics, and data science methodologies.

• Transport Demand Modeling
• Discrete Choice Models
• Freight Transport Modelling
• +3 more