جست‌وجوی استاد و پژوهشگر

Tove Helsمشاهده پروفایل

دانشیار

• Traffic Safety
• Transportation Engineering
• Road User Behavior
• +۷ مورد دیگر

Tove Hels is an Associate Professor in the Department of the Built Environment at the Faculty of Engineering and Science, Aalborg University, Denmark. She is a key member of the Traffic Research Group, focusing on transportation safety, road user behavior, and traffic policy. Her work bridges engineering, public health, and social science to improve road safety outcomes. Research Interests: Her research spans traffic safety, cyclist-motorist interactions, speed enforcement, accident risk modeling, and the impact of vehicle technologies. She investigates both infrastructure design and behavioral factors influencing road safety, with a strong emphasis on data-driven analysis and policy evaluation. The recent publications reveal a consistent focus on improving cyclist safety through infrastructure (e.g., advanced stop boxes, roundabout design), addressing under-reporting of traffic injuries, and evaluating behavioral interventions for speeding. Her work integrates epidemiological methods, statistical modeling, and real-world policy applications. Scientific Contributions: Principal Investigator in the Danmarks Trafikulykker cohort study (2025–2029) Project participant in EASE: Intervention against speed offenders (2019–2025) Author of over 29 research outputs including journal articles, reports, and policy briefs Frequent contributor to national media and advisory bodies like Dansk Vejforening Advising and Grants: While no formal students are listed, her leadership in major funded research projects indicates a supervisory role in training junior researchers. She has secured and contributed to significant research grants related to traffic safety monitoring and intervention evaluation. Labs and Teams: She is part of the Traffic Research Group at Aalborg University, collaborating closely with researchers such as H. Lahrmann, T.K.O. Madsen, and A.V. Olesen. The group conducts field studies, data linkage projects, and policy evaluations with national impact.

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.

Anders Rønn-Nielsenمشاهده پروفایل

دانشیار

• Applied Probability Theory
• Lévy-Based Modelling
• Statistical Methods in Efficiency Analysis
• +۳ مورد دیگر

Anders Rønn-Nielsen is an Associate Professor at the Department of Finance and Center Coordinator at the Center for Statistics at Copenhagen Business School (CBS). He holds a M.Sc. in Statistics from University of Copenhagen and a PhD in Statistics and Probability Theory from Aarhus University. His research focuses on applied probability theory, particularly Lévy-based spatial models, and statistical efficiency analysis. His academic credentials include: M.Sc. in Statistics, University of Copenhagen PhD in Statistics and Probability Theory, Aarhus University Research interests span: Lévy processes and spatial stochastic modeling Extreme value theory applications in finance and natural sciences Nonparametric production frontier analysis Efficiency measurement methodologies His publications (18+ articles) emphasize theoretical probability and statistical applications in efficiency analysis. He has served as external examiner at Aarhus University and Copenhagen University for master’s and PhD examinations (2017–2019). His teaching responsibilities include advanced probability theory courses and statistical methods training for economics students.

Emilia Mendesمشاهده پروفایل

استاد

• Software Engineering
• Empirical Software Engineering
• Human-Centric Software Engineering
• +۱۲ مورد دیگر

Emilia Mendes is a Full Professor in the Department of Electrical and Computer Engineering at Aarhus University . Her research focuses on Empirical Software Engineering , particularly human-centric approaches, evidence-based decision-making, and the application of machine learning and statistical techniques in software development. Current research themes: Human-Centric Software Engineering, Evidence-Based Research, Statistical/Machine-Learning Techniques, and Value-Based Software Engineering. Developed tools for team climate forecasting, capability measurement, and value-based decision-making. Research Trends: Her work bridges software engineering with psychology (personality traits, team dynamics), machine learning (effort estimation, dementia prognosis), and value-based frameworks for decision-making. She emphasizes industrial applications, including agile methodologies, cross-company predictions, and Bayesian network modeling. Scientific Impact & Awards: 10,018 citations, h-index 58. Ranked #32 in Empirical Software Engineering Scholars (Google Scholar). Ranked #20 in Top Computer Science Scientists in Sweden (2023). Top 2% scientist in the world (2019, 2020, 2022; only female in Sweden for SE in 2022. Nine best paper awards at international conferences. Editorial board member: Information and Software Technology , ACM Computing Surveys , former roles at IEEE Transactions on Software Engineering and others. Grants & Leadership: Awarded €11.921.603 in research grants. Held leadership roles as General Chair (EASE 2017), PC Co-Chair (EASE 2012, ESEM 2012), and active participant in 200+ academic events.

Martin Aumüllerمشاهده پروفایل

مدرس

• Algorithms
• Algorithm engineering
• Algorithmic privacy
• +۶ مورد دیگر

Martin Aumüller is a Lecturer in Theoretical Computer Science Algorithms at the IT University of Copenhagen . He serves as Head of Education and Master of Software Design , focusing on algorithm engineering, differential privacy, and similarity search. Research interests include: Algorithm engineering for high-dimensional data Locality-sensitive hashing and nearest neighbor search Privacy-preserving machine learning Fairness in approximate search algorithms Benchmarking and evaluation of similarity search tools Publications trends highlight his work on approximate nearest neighbor search , privacy-preserving techniques , clustering algorithms , and scalable outlier detection in high-dimensional spaces. His recent projects (2024-2025) focus on fairness, differential privacy, and efficient indexing. Grants and projects : DIREC (2020-2025): Digital Research Centre Denmark (Innovation Fund Denmark) DIREC: Bias and Benefit of Approximate Nearest Neighbor Search (2022-2025): Principal Investigator (Innovation Fund Denmark) BARC (2017-2024): Basic Algorithms Research Copenhagen (Villum Fonden) SSS (2014-2019): Scalable Similarity Search (European Commission)

Mads Stenbo Nielsenمشاهده پروفایل

دانشیار

• Credit Risk
• Corporate Bonds
• Business Cycle and Credit Risk
• +۳ مورد دیگر

Mads Stenbo Nielsen is an Associate Professor in the Department of Finance at Copenhagen Business School, affiliated with the Center for Statistics. He holds a M.Sc. in Statistics from the University of Copenhagen and a Ph.D. in Finance from CBS. His research focuses on credit risk dynamics, corporate capital structure, and default correlation modeling, with publications in journals like Journal of Financial Intermediation and Journal of Financial Econometrics . His primary research areas include credit risk analysis, corporate bond markets, and the interplay between economic cycles and financial risk. Notable contributions explore systemic risk in synthetic credit markets and the value of bond underwriter relationships. He also serves as a Portfolio Manager at BankInvest since August 2019, bridging academic and practical finance roles. Teaching responsibilities include courses on Statistics, Introductory Finance, and Term Structure Theory. His work has been presented at conferences such as the Financial Management Association International and the Paris December Finance Meeting. He contributes to the Centre for Financial Frictions and maintains external roles in financial advisory and portfolio management.

Mogens Fosgerauمشاهده پروفایل

استاد

• Transport Economics
• Discrete Choice Modeling
• Travel Behavior
• +۷ مورد دیگر

Mogens Fosgerau is a Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in transport policy and transportation science. His work spans econometrics, travel behavior modeling, and transport economics, contributing to sustainable urban mobility and policy design. Institution: Technical University of Denmark Department: Department of Technology, Management and Economics Email: mogens.fosgerau@econ.ku.dk ORCID: https://orcid.org/0000-0002-6452-5215 His research focuses on discrete choice modeling, travel time valuation, scheduling preferences, and congestion pricing. He develops theoretical and empirical models to understand how individuals make travel decisions under uncertainty and how these behaviors affect urban transport systems. His work integrates economic theory with data-driven methods, often using large-scale datasets and advanced econometric techniques. The recent trend in his publications highlights innovations in perturbed utility models for route choice, stochastic traffic assignment, and the analysis of induced demand for cycling. His research bridges transportation science, behavioral economics, and operations research, with applications in urban planning and policy evaluation. Scientific awards received include: The International Choice Modeling Conference (ICMC) award for Most Innovative Application (2022) Best Overall Paper Award, ITEA Conference (2015) Best Paper Awards from BIVEC-GIVET (2007), Kuhmo-Nectar (2008) Hedorfs Fonds Pris for Transportforskning (2011) Mogens Fosgerau has supervised PhD students such as Fentie Abegaz and has been involved in multiple externally funded research projects, including URBAN (Innovation Fund Denmark), IRUC (Danish Council for Strategic Research), and Horizon 2020 initiatives. He has also served on review panels, including for the Norwegian Research Council, and contributed to peer review and editorial duties. He is actively engaged in research networks and has presented his work at international conferences. His projects often involve interdisciplinary collaboration with researchers in economics, engineering, and urban planning.

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.

• Statistics
• Machine Learning
• Functional Data Analysis
• +۳ مورد دیگر

Anton Rask Lundborg is a Postdoctoral Research Fellow at the Department of Mathematical Sciences, University of Copenhagen. He works in the areas of Statistics, Machine Learning, and Functional Data Analysis. Key research areas: Statistics, Machine Learning, Functional Data Analysis, Causal Inference, Bioinformatics, Nonparametric Statistics Research groups: SPT (Statistical Learning Theory), CoCaLa (Collaborative Causal Learning) His recent work focuses on causal feature selection, variable significance testing, and functional data analysis applications. Publications span journals like Journal of the American Statistical Association , Briefings in Bioinformatics , and Annals of Statistics . Methodological innovations include the Projected Covariance Measure and conditional independence testing in Hilbert spaces. Explore his full research profile and publications via ORCID or the University of Copenhagen's Mathematical Sciences website .

Andreas Bjerre-Nielsenمشاهده پروفایل

دانشیار

• Educational Public Policy
• AI for Public Policy
• Social Networks
• +۶ مورد دیگر

Andreas Bjerre-Nielsen is an Associate Professor at the Department of Economics and Copenhagen Center for Social Data Science (SODAS) within the Faculty of Social Sciences at the University of Copenhagen. His work bridges economics and data science to analyze education-related behavior and policies. Research Focus: School choice, digital technology in education, predictive analytics for interventions, and social network effects. Methodology: Combines econometrics with machine learning techniques to evaluate policy impacts. Research Trends: Recent publications emphasize algorithmic fairness in college admissions, socioeconomic impacts of school boundary policies, and behavioral insights from large-scale datasets. His 2025 Scientific Reports study reveals nation-scale social network dynamics. Awards and Grants: Tietgen Prize (2021) for young social science researchers 2024: Independent Research Fund Denmark grant for 'Coded Clues' project 2023: Major grant for school choice research Collaborations: Works with Danish Ministry of Children and Education through UDDanKvant unit, and collaborates with multidisciplinary researchers including Sune Lehmann and David Dreyer Lassen.

• Exoplanet atmospheres
• High-energy astrophysics
• Microlensing events
• +۳ مورد دیگر

Nanna Bach-Møller is a postdoctoral fellow at the Niels Bohr Institute, University of Copenhagen, specializing in astrophysics and planetary research. She completed her PhD in 2024 with a dissertation on exoplanet atmospheres in high-energy radiative environments. Research Interests Exoplanet atmospheres and their interaction with high-energy radiation Microlensing events and brown dwarf characterization Planetary system dynamics and orbital evolution Stellar activity and its impact on exoplanet observations Her work combines theoretical modeling with observational data analysis, focusing on understanding atmospheric processes in extreme environments and the dynamics of planetary systems. Publications Dr. Bach-Møller has published extensively in leading journals such as Astrophysical Journal , Astronomy & Astrophysics , and Monthly Notices of the Royal Astronomical Society . Her recent work includes studies on cloud particle charging, transmission spectroscopy of exoplanets, and precision measurements of brown dwarf masses through microlensing. Collaborations She collaborates with international teams, including the MiNDSTEp Consortium, OGLE Collaboration, and MOA Collaboration, contributing to large-scale surveys and high-impact research projects.

Arne Feddersenمشاهده پروفایل

استاد

• Sports Economics
• Applied Regional and Urban Economics
• Media Economics
• +۳ مورد دیگر

Arne Feddersen is a Professor at the Department of Business and Sustainability within the University of Southern Denmark . His academic work spans Sports Economics , Applied Regional and Urban Economics , and Media Economics , with a focus on empirical analyses of sports events, betting markets, and regional development. He holds a Dr. rer. pol. degree (Doctor of Economics) and maintains an ORCID profile (0000-0001-8935-4021). Research highlights include: Economic impacts of Formula One, Tour de France, and Olympic Games Competitive balance dynamics in professional sports Sentiment bias in betting markets (NBA, NFL, European football) Agglomeration effects from transport infrastructure Media demand analysis for sports broadcasts Recent work analyzes event hosting valuation , gambling market anomalies , and sports regulation frameworks . He serves on editorial boards and departmental councils while supervising courses in sports economics and management.

• Machine Learning
• Remote Sensing
• Geospatial Representation Learning
• +۳ مورد دیگر

Stefan Oehmcke is an Assistant Professor at the Machine Learning Section of the Department of Computer Science , University of Copenhagen. His research focuses on applying machine learning techniques to environmental and geospatial analysis, particularly in forest ecology, tree monitoring, and climate impact studies. Research Trends: His recent publications emphasize deep learning for LiDAR data processing, multi-modal geospatial representation, and sustainable AI practices. Key Collaborations: Frequently collaborates with researchers in environmental science, remote sensing, and climate change (e.g., Martin Brandt, Christian Igel). Applications: Develops tools for forest biomass estimation, tree mortality mapping, and urban safety analysis using satellite imagery. While no specific educational background or scientific awards are mentioned in the provided texts, Oehmcke's work demonstrates technical innovation in AI explainability and environmental monitoring, with significant contributions to journals like Remote Sensing of Environment and Nature Communications .

Kasper Green Larsenمشاهده پروفایل

استاد

• Machine Learning
• Theoretical Computer Science
• Algorithms and Data Structures
• +۳ مورد دیگر

Kasper Green Larsen is a Professor in the Department of Computer Science at Aarhus University. His research focuses on theoretical computer science, machine learning, algorithms, and data structures. He has made significant contributions to boosting algorithms, PAC learning theory, and computational geometry. His work often bridges algorithm design with complexity theory, addressing challenges in optimization, memory efficiency, and lower bounds analysis. Key research areas include: Algorithmic Learning Theory (e.g., boosting, bagging, and PAC learners) Data Structure Design (e.g., invertible Bloom tables, succinct representations) Computational Complexity (e.g., lower bounds for dynamic and oblivious algorithms) Geometric Algorithms (e.g., hierarchical searching, range queries) Recent publications emphasize foundational advancements in learning theory (e.g., optimal weak-to-strong learning) and data efficiency (e.g., memory-reduced Bloom filters). His work frequently appears in top conferences like IJCAI, ICALP, and SODA, reflecting rigorous theoretical contributions with practical implications.