جستجوی استاد راهنمای دانشگاهی

• Educational stratification
• Social mobility
• Quantitative methods
• +۴ مورد دیگر

Kristian Bernt Karlson is a Professor in the Department of Sociology at the University of Copenhagen, affiliated with the Faculty of Social Sciences. He serves as Director of Graduate Studies in Social Science and leads the ERC-funded SIBMOB project on siblings' social mobility. His research focuses on educational stratification, social mobility, and quantitative methods, with notable contributions to intergenerational mobility analysis and nonlinear probability models. Karlson has held tenured positions since 2016 and serves on editorial and advisory boards including Sociological Science and the Nils Klim Prize Committee. Education: PhD, Faculty of Arts, Aarhus University (2013) MA Sociology, University of Copenhagen (2009) BA Sociology, University of Copenhagen (2006) Research Interests: Karlson investigates mechanisms behind educational and socioeconomic inequality, including sibling mobility comparisons and the role of educational expectations. He co-leads the UddanKvant research center and has led major projects funded by the ERC, Danish government ministries, and international foundations. Key Achievements: ERC Starting Grant (2021–2026) for SIBMOB project 2023 ASA Leo Goodman Mid-Career Award 2022 EAS Raymond Boudon Early Career Achievement Award Co-developed influential methodology for comparing logit/probit models (top-1% cited paper) Grants & Projects: Principal Investigator on 7 externally funded projects (including NORFACE, Rockwool Foundation) Managing interdisciplinary education research center (UddanKvant) Labs/Teams: Active in the Danish National Centre for Social Research (SFI), NORFACE network, and international collaborations with institutions like Oxford University and Yale University.

Morten Andersenمشاهده پروفایل

دانشیار

• Fluid Dynamics
• Mathematical Modeling
• Biological Systems
• +۳ مورد دیگر

Morten Andersen is an Associate Professor at Roskilde University's Department of Science and Environment Mathematics and Physics (IMFUFA), with expertise in fluid dynamics, biological systems modeling, and blood cancer research. He holds a PhD from Technical University of Denmark (2013) and an MSc from Roskilde University (2009). His research focuses on three key areas: fluid dynamics of vortices, enzyme-substrate interactions for sustainable biofuel production, and mathematical modeling of blood cancers like leukemia and myeloproliferative neoplasms (MPNs). Collaborations include work with Roskilde Hospital on MPN treatments and Roskilde University chemists on biofuel projects. His recent work includes studies on vortex interactions in fluids, modeling stem cell dynamics under ruxolitinib treatment, and analyzing neutrophil-to-lymphocyte ratios in mortality prediction. He previously worked at engineering firm DHI (2013–2014) before returning to academia in 2015. Publications span fluid mechanics, mathematical oncology, and biomedical engineering. His research integrates computational models with clinical data to address challenges in hemodialysis, cancer therapy optimization, and systems biology of aging and inflammation.

Jesper Schmidt Hansenمشاهده پروفایل

استاد

• Fluid Mechanics
• Molecular Dynamics
• Nanofluidics
• +۴ مورد دیگر

Jesper Schmidt Hansen is a Professor at the Department of Science and Environment, Mathematics and Physics (IMFUFA) at Roskilde University, Denmark. He also holds an Adjunct Professor position at Swinburne University of Technology. His research focuses on fluid mechanics, molecular dynamics, nanofluidics, and viscoelasticity, with applications in both theoretical and computational studies. Central to his work are topics like vorticity dynamics, nanoconfined fluids, and generalized hydrodynamic models. He has contributed to projects such as 'Dyre', exploring non-equilibrium processes in viscous liquids. His educational contributions include studies on mathematics challenges in natural science undergraduate programs. Hansen has authored over 60 publications and actively participates in workshops, seminars, and peer reviews, including sessions on 'Generalized Hydrodynamics' and 'Viscous Liquids'. His research often bridges fundamental physics with applied problems, such as modeling bitumen behavior for sustainable materials and understanding tumor growth dynamics. Hansen utilizes advanced computational tools like molecular dynamics simulations and collaborates internationally, evidenced by his extensive external network and cross-institutional projects. Key research themes include the hydrodynamic limits of nano-scale systems, electro-osmotic flow optimization, and the development of improved viscoelastic models for complex fluids. His work frequently addresses questions at the interface between microscopic dynamics and macroscopic continuum descriptions, emphasizing non-equilibrium phenomena.

• Science Education
• Problem-Based Learning (PBL)
• Physics Education
• +۳ مورد دیگر

Martin Niss is an Associate Professor in the Department of Science and Environment Mathematics and Physics (IMFUFA) at Roskilde University, Denmark. He is affiliated with the Science Studies Centre for Research on Problem-oriented Project Learning. His research focuses on science education, problem-based learning (PBL), physics education, and the history of physics modeling. Key projects include PRIMAS (2010–2013), promoting inquiry in math/science education, and a 2020 project on PBL dynamics in natural science. His work spans comparative didactics, formative evaluation, and interdisciplinary approaches. He has published extensively on student peer feedback mechanisms, challenges in undergraduate mathematics, and historical analyses of physics concepts like phase transitions. Notable collaborations include comparative studies across higher education contexts and investigations into scientific demarcation criteria. Dr. Niss has actively participated in academic activities, including organizing the 2023 ‘Symposium for Comparative Subject Didactics’ and presenting at conferences on inquiry-based learning and physics pedagogy. His contributions extend to media engagements, such as a 2018 RU Radio podcast discussing math challenges and physics concepts.

• Data Science
• Network Science
• Computational Social Science
• +۷ مورد دیگر

Vedran Sekara is an Associate Professor in Data Science at the IT University of Copenhagen, focusing on the intersection of Network Science, Computational Social Science, and Algorithmic Auditing. His work examines how data-driven technologies impact societal systems, with particular attention to biases in algorithmic decision-making and humanitarian applications of machine learning. Research Interests: Algorithmic fairness in social systems Human mobility modeling and pandemic response Socioeconomic indicator estimation via digital footprints Climate change communication strategies Machine learning for humanitarian work Recent Projects: Principal Investigator for DIREC: Digital Research Centre Denmark (2020-2025) Lead investigator in DIREC Mobility Analysis Project (2022-2023) Awards: Recipient of Årets Forskningsmiljø 2022 Award for collaborative research excellence Media Engagement: Frequently discusses algorithmic impacts on Danish media including PRM/Hvordan påvirker algoritmer vores samfund? (April 2021) and Nu vil regeringen og opruste mod cyberangreb (June 2021). Key Collaboration Areas: Extensive international research networks in mobility science, computational social science, and algorithmic auditing.

Paul Hünermundمشاهده پروفایل

استاد مدعو

• Causal Inference
• Econometrics
• Innovation Economics
• +۳ مورد دیگر

Paul Hünermund is a Tenure Track Adjunct Professor at the Department of Strategy and Innovation, Copenhagen Business School. His research focuses on causal inference, econometrics, and innovation economics, with notable contributions to strategic management methodologies and AI-driven innovation systems. Current research trends in his publications include: (1) causal inference techniques in management science; (2) impact of AI on knowledge diffusion and innovation ecosystems; (3) control variable selection in empirical analysis; and (4) post-conflict innovation dynamics. His recent work appears in journals like Econometrics Journal and Organizational Research Methods . He has contributed to debates on innovation policy, AI applications for SMEs, and research productivity challenges, with media appearances in Danish and international outlets. His work aligns with UN Sustainable Development Goals related to innovation and sustainable economic growth. Supervised work includes 6 projects, though specific advisees aren't named in the provided texts. The ORCID identifier 0000-0001-9163-038X is associated with his academic profile.

Rasmus Tangsgaard Varneskovمشاهده پروفایل

استاد

• Econometrics
• High-Dimensional Statistics
• Asset Pricing
• +۳ مورد دیگر

Rasmus Tangsgaard Varneskov is a Professor of Statistics and Financial Econometrics at the Department of Finance, Copenhagen Business School (CBS). He is also employed by Alphadyne Asset Management. His research focuses on econometrics, high-dimensional statistics, asset pricing, and financial economics, with a strong emphasis on time series analysis and financial econometrics methodologies. Before joining CBS, he was a postdoctoral researcher in Finance at Northwestern University's Kellogg School of Management. He holds affiliations with the Center for Big Data in Finance (BIGFI) and the Center for Statistics at CBS. His educational background includes advanced studies in statistics and econometrics, though specific degrees are not detailed in the provided text. Rasmus' research interests span advanced statistical methodologies for financial data, including volatility estimation, bootstrap techniques, predictive regressions, and structural change analysis. His work has been published in top journals such as Journal of Econometrics , Journal of Financial Economics , and Quantitative Economics . A key achievement is his 2023 Econometric Theory Multa Scripsit award for prolific and impactful contributions. His publications address topics like Laplace transforms of volatility, consistent inference in predictive regressions, and dynamic hedging strategies. While no formal advisees are listed, his industry collaborations (e.g., with Nordea and Alphadyne) suggest applied research engagement. He contributes to CBS's research initiatives through participation in interdisciplinary centers focused on big data and financial statistics.

• Formal Methods
• Privacy Engineering
• Probabilistic Programming
• +۳ مورد دیگر

Raúl Pardo Jimenez is an Associate Professor in the Department of Software Engineering at the IT University of Copenhagen, affiliated with the Software Quality Research (SQUARE) group. His research focuses on formal methods and privacy engineering, with particular emphasis on probabilistic programming and privacy risk quantification in data analytics. His research spans three interconnected domains: Formal verification of privacy compliance systems like GDPR implementation Probabilistic modeling for quantitative privacy risk assessment Open source sustainability impacts on software quality metrics Recent publications demonstrate a consistent focus on developing rigorous computational methods for privacy preservation, with works appearing in Springer's Lecture Notes in Computer Science and Empirical Software Engineering journals. He leads the REIDENT project (2018-2022), funded by Villum Fonden, which developed Bayesian probabilistic programming approaches for reidentification risk assessment in sensitive datasets.

Kai Budrikasمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Forensic Genetics
• Statistical Genetics
• +۱ مورد دیگر

Kai Budrikas is a Research Fellow in the Department of Data Science at IT University of Copenhagen, specializing in statistical genetics and forensic DNA evidence analysis. His work focuses on developing unified statistical frameworks for complex forensic identification problems. Active project: A Unified Statistical Framework for Complex Forensic DNA Evidence (2022-2027) funded by Independent Research Fund Denmark Current affiliations: ITU PhD Club (Chair), Young Statisticians Denmark (Member) Upcoming collaborations: Netherlands Forensic Institute & Vrije University Amsterdam (visiting researcher, 2024-2025) His research intersects forensic genetics, statistical modeling, and computational biology. Collaborations include Denmark's ITU and Netherlands-based institutions.

Ahmad Salim Al-Sibahiمشاهده پروفایل

استادیار

• Probabilistic Programming
• Programming Language Theory
• Static Analysis
• +۵ مورد دیگر

Dr. Ahmad Salim Al-Sibahi is an Assistant Professor in the Programming Language and Theory of Computation Section at the University of Copenhagen's Department of Computer Science. He is a core member of the Aleatory Science Team, focusing on probabilistic programming applications in protein folding and inference algorithm optimization. His work combines formal methods with practical machine learning frameworks like Pyro. Educational Background: PhD in Computer Science (2014-2017), IT University of Copenhagen MSc in Computer Science (2014), IT University of Copenhagen Postdoctoral Researcher at University of Copenhagen (2017-2019) Research Interests: His primary focus lies at the intersection of probabilistic programming and computational biology. He develops efficient inference algorithms for probabilistic models, particularly in protein structure prediction and voucher scanning applications. His earlier work includes formal verification of program transformations using inductive refinement types and static analysis techniques. Awards: Best Paper Award at GPCE 2018 Contributions: He contributed to dependently-typed language Idris and co-developed family-based model checking techniques. His research bridges theoretical programming language constructs with applied problems in bioinformatics and software modernization.

Kristian Olesenمشاهده پروفایل

دانشیار

• Strategic Spatial Planning
• Urban Sustainability
• Housing Policy
• +۶ مورد دیگر

Kristian Olesen is an Associate Professor in strategic spatial planning at the Department of Sustainability and Planning, Aalborg University, affiliated with The Technical Faculty of IT and Design. He is a member of the Planning for Urban Sustainability (PLUS) research group and serves as program coordinator for the Urban Planning and Management master’s program. His work focuses on linking spatial planning to politics, urban infrastructure investments, and neoliberalization trends in Denmark. Research Interests: Strategic spatial planning at multiple scales (transnational to neighborhood) Urban redevelopment of deprived neighborhoods via housing associations Integration of energy and urban planning for Positive Energy Districts Neoliberalization of Danish spatial planning systems Urban governance and public participation Projects: Lead investigator for PED-JUST (2025-2027): Energy transition strategies in disadvantaged neighborhoods FLEXPOSTS (2022-2025): Flexible positive energy district systems Fremtidens Boligorganisation (2021-2025): Housing associations as urban developers Teaching: 10+ years leading problem-based learning in urban planning aligned with UN SDGs Focus on sustainable development goals integration in curricula Collaborations: Active partnerships with Realdania, Landsbyggefonden, and Innovation Fund Denmark International collaborations on Nordic housing policy and urban development

Arthur Zimekمشاهده پروفایل

استاد

• Outlier Detection
• Clustering Algorithms
• High Dimensional Data Analysis
• +۳ مورد دیگر

Arthur Zimek is a Professor and Head of the Data Science section at the Department of Mathematics and Computer Science, University of Southern Denmark. He holds a Dr. rer. nat. habil. (Habilitation) from Ludwig-Maximilian University of Munich. His research focuses on outlier detection, clustering algorithms, high-dimensional data analysis, and machine learning. Zimek has held guest professorships at TU Vienna and has been a postdoctoral fellow at the University of Alberta. Education includes a Diploma in Bioinformatics, Magister Artium in Philosophy, and a Diploma in Theology, all from German institutions. His work emphasizes algorithm development and evaluation, with contributions to open-source tools like ELKI. He has received awards including the ACM SIGKDD 2017 Notable Article and the 2025 Author Service Award. Key research areas include clustering validation, semi-supervised learning, and interpretable machine learning. Projects involve cardiovascular disease prediction and synthetic data evaluation. He teaches courses on data mining, statistical learning, and spatiotemporal data analysis.

Marco Chiarandiniمشاهده پروفایل

دانشیار

• Operations Research
• Optimization Algorithms
• Integer Programming
• +۱۰ مورد دیگر

Marco Chiarandini is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), with a secondary affiliation to the SDU Climate Cluster. His research focuses on Operations Research, Optimization Algorithms, and their applications in domains such as transportation, healthcare, and education. He has led or contributed to projects addressing dynamic traffic estimation, dementia care app design, and sustainable software development. Chiarandini has supervised students including P. Tampakis and C. McCarthy, and his work has been featured in interdisciplinary collaborations with institutions globally. His recent articles explore topics like goal-scoring prediction in football and urban traffic modeling, reflecting his expertise in data science and algorithmic design. His academic contributions span over 50 publications, emphasizing methodological advancements in optimization and heuristic algorithms. He actively participates in conferences and peer review, and has coordinated EU-funded projects like Data Science for University Management (2019–2023). Chiarandini’s teaching includes courses on linear programming and metaheuristics, integrating practical applications with theoretical foundations.

Simon Lebech Cichoszمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Diabetes
• +۴ مورد دیگر

Simon Lebech Cichosz is an Associate Professor at the Department of Health Science and Technology, Aalborg University's Faculty of Medicine. His work bridges biomedical engineering with clinical applications, focusing on AI/ML-driven solutions for diabetes management and chronic disease diagnostics. He actively contributes to editorial boards and Horizon Europe initiatives while leading multi-year research projects. Education: PhD in Predictive Models in Diabetes (2018) M.Sc. in Biomedical Engineering (2016) B.Sc. in Biomedical Engineering (2011) Research Interests: Specializes in AI/ML applications for diabetes diagnostics, including complications like neuropathy and gastroparesis. Develops predictive models using continuous glucose monitoring data, aiming to personalize healthcare through machine learning. Explores glycemic variability analysis and early detection of comorbidities. Scientific Contributions: Editorial Board, Journal of Diabetes Science and Technology (2024–) Registered Expert for Horizon Europe (2025–) 145+ research outputs including AI models for diabetic complication prediction Teaching Approach: Advocates problem-based learning (PBL) with clinical case studies. Integrates real-world project assignments and external collaborations to prepare healthcare sector professionals.

Marina Georgatiمشاهده پروفایل

استادیار

• Sustainable Energy Planning
• Spatial Analysis
• Urban Demography
• +۴ مورد دیگر

Marina Georgati is an Assistant Professor at Aalborg University, affiliated with the Technical Faculty of IT and Design and the Department of Sustainability and Planning . Her research focuses on sustainable energy systems, spatial analysis, and migration patterns, with a strong emphasis on machine learning applications in urban demography and environmental planning. She leads the Sustainable Energy Planning Research Group and collaborates on interdisciplinary projects such as the Heat Roadmap Europe 5 and iDesignRES initiatives. Key Projects: iDesignRES: Open-source tools for renewable energy system design HRE5: Strategic heat planning across Europe FUME: Migration scenarios analysis for Europe BalticRIM: Maritime cultural heritage management Research Interests: Georgati’s work bridges energy sustainability, urban demography, and computational geography. She develops machine learning models for spatial disaggregation of population data, analyzes migration dynamics using gridded datasets, and evaluates heat potential in district heating systems. Her methodologies include gradient boosting, deep learning, and compositional data analysis. Grants & Activities: Principal Investigator/Participant in 5 EU-funded projects (2017–2027) Peer reviewer for Environmetrics , International Journal of GIS , and others Organizer of academic workshops on data science applications Labs/Teams: She contributes to the Data Science Lab x Academia collaboration and leads the Sustainable Energy Planning Research Group, fostering interdisciplinary innovation in energy and urban systems.