Find Academic Supervisors

Jimmy Martinez-Correa is an Associate Professor in Applied Economics at the Copenhagen Business School (CBS) . His research focuses on decision-making under risk, uncertainty, and ambiguity, with significant contributions to experimental economics, behavioral finance, and risk management. He has published in top-tier journals like the Journal of Financial Economics and Journal of Money, Credit and Banking . PhD : Risk Management & Insurance (Georgia State University, 2012) M.A. & B.A. : Economics (Universidad de los Andes, Colombia) His research spans behavioral economics, econometrics, and machine learning applications for causal analysis. Recent work examines index insurance welfare consequences, bankruptcy reorganization incentives, and mortgage run-off behavioral responses. Key recent publications include: 2025 – Welfare Consequences of the Compound Risks of Index Insurance 2022 – Measuring the Ex-ante Incentive Effects of Creditor Control Rights 2022 – Responses to Eliminating Saving Commitments: Evidence from Mortgage Run-offs He has received the Sapere Aude Young Research Talent grant from the Danish Government and is affiliated with: Center for Economic Analysis of Risk (CEAR) , Georgia State University Pension Research Center (PeRCent) , CBS Danish Finance Institute His work integrates field experiments, econometric modeling, and behavioral insights to address practical policy and financial challenges. Current research explores ambiguity preferences, household risk management, and criminal behavior through field experiments.

• Decision-making under risk and uncertainty
• Behavioral economics and finance
• Risk management and insurance
• +4 more

Søren Wengel Mogensen is an Associate Professor at the Department of Finance, Copenhagen Business School, Denmark. His research focuses on developing advanced statistical and machine learning methodologies for complex systems analysis. Research Interests: Dr. Mogensen's work spans causal inference, stochastic processes, survival analysis, and time-series modeling. Key themes include: Causal discovery algorithms for industrial and biological systems Graphical representations of dependencies in high-dimensional data Time-varying mediation in survival contexts Bayesian networks for cascade modeling Publication Trends: His recent articles (2021-2025) demonstrate a strong emphasis on theoretical-statistical innovation with applications in healthcare, industrial monitoring, and computational finance. Dominant methodologies include kernel-based independence tests, continuous-time Bayesian networks, and constrained stochastic process modeling.

• Causal Inference
• Stochastic Processes
• Survival Analysis
• +3 more

Constanza Catalina Fierro Mella is a PhD Fellow and Postdoc in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen's Faculty of Science. Her research bridges computational linguistics with philosophical inquiry into knowledge representation in language models. Her primary research interests include Multilingual Language Models , Knowledge Representation , Mechanistic Interpretability , and Cross-cultural NLP . Fierro's work examines how language models remember and represent facts across languages, with particular attention to the epistemological foundations of artificial intelligence. Her publication record shows a strong focus on understanding the inner workings of language models, with recent papers exploring how multilingual models remember facts, the intersection of philosophy and mechanistic interpretability, and knowledge representation in large language models. Her research often involves collaboration with Anders Søgaard and other members of Copenhagen's NLP group. Fierro has received significant attention for her work, with papers like 'Challenges and Strategies in Cross-Cultural NLP' garnering over 100 citations. Her research spans both theoretical foundations and practical applications of natural language processing. She has contributed to diverse areas including multimodal learning (investigating connections between vision and language models), healthcare applications (predicting hospital readmissions), and historical document analysis (date recognition in parish records).

• Natural Language Processing
• Multilingual Language Models
• Knowledge Representation
• +5 more

N. Meltem Daysal is an Associate Professor in the Department of Economics at the University of Copenhagen, Faculty of Social Sciences. She is a member of the Center for Economic Behavior and Inequality (CEBI), a Research Fellow at IZA (Institute for the Study of Labor), and a Research Fellow at CESifo. Dr. Daysal currently serves as an associate editor at Journal of Health Economics. Her educational background includes a B.A. in Economics from Bogazici University (2003) and a Ph.D. in Economics from University of Maryland, College Park (2009). Dr. Daysal is an applied microeconomist whose research lies at the intersection of health and labor economics. Her ongoing work addresses policy debates concerning increasing medical expenditures and rising wealth inequality. She focuses on two main areas: (1) the effects of health shocks on families and how medical interventions alleviate these effects, and (2) the effects of housing wealth on society. Her research methodology primarily involves using register data and exploiting natural experiments to identify causal effects. Her work spans child development, medical interventions, intra-household spillovers, and broader health and labor economics topics. Analysis of Dr. Daysal's recent publications (2015-2025) reveals a strong focus on causal identification in health economics using administrative data. Her research consistently examines how health shocks and medical interventions affect not only direct recipients but also family members through spillover effects. She has made significant contributions to understanding intergenerational wealth transmission, particularly regarding housing wealth, and has increasingly incorporated machine learning techniques into health policy analysis. Her work often addresses policy-relevant questions with implications for social safety net programs and health care interventions. Dr. Daysal has published extensively in top economics journals including American Economic Journal: Economic Policy, American Journal of Health Economics, Review of Economics and Statistics, Journal of Public Economics, and Journal of Health Economics. Her research has received press coverage from outlets such as VoxEU, Fortune, and Yahoo Finance, indicating its policy relevance and public impact. As an advisor and researcher, Dr. Daysal collaborates extensively with economists across institutions, particularly leveraging Danish administrative data to address causal questions in health and labor economics. While specific grant information isn't detailed in the provided text, her research output suggests sustained funding for data access and research assistance. She appears to be an active mentor to junior researchers through collaborative projects. Dr. Daysal's research is closely affiliated with the Center for Economic Behavior and Inequality (CEBI) at the University of Copenhagen, which serves as a hub for her collaborative work in behavioral and health economics. Her use of Danish administrative data suggests strong connections with Danish government agencies that manage these registers.

• Health Economics
• Labor Economics
• Child Development
• +2 more

Ulrik Plesner Jacobsen is an Academic employee at the Department of Public Health, Section for Health Data Science and AI at the University of Copenhagen. His research focuses on applying advanced computational methods to biomedical data, particularly in the context of diabetes and complex diseases. Dr. Jacobsen's research interests span multiple interdisciplinary fields at the intersection of data science and healthcare. His work primarily focuses on: Artificial intelligence and machine learning applications in healthcare Analysis of multi-omics data (genomics, proteomics, metabolomics) Development of methods for causal inference in biomedical datasets Application of Bayesian networks and deep learning to incomplete medical data Identification of molecular mechanisms underlying type 2 diabetes Integration of diverse biological data types for disease understanding His recent publications demonstrate a strong focus on integrating diverse biological data types to uncover causal relationships in complex diseases, particularly type 2 diabetes. His work combines cutting-edge AI techniques with rigorous statistical approaches to address challenges in biomedical data analysis, including missing data imputation and the identification of drug-omics associations. The research often involves large-scale international collaborations through consortia like IMI DIRECT. Dr. Jacobsen actively collaborates with international research teams across Europe as evidenced by the extensive author lists on his publications. His work has appeared in high-impact journals including Nature Biotechnology, Nature Communications, and PLOS Genetics, demonstrating the significance of his contributions to computational biomedical research.

• AI and machine learning in healthcare
• Diabetes research
• Omics data analysis
• +5 more

Theo Rüter Würtzen serves as an Instructor in the Department of Computer Science at the University of Copenhagen, specializing in causal inference and machine learning methodologies. His research focuses on: Developing algorithms for causal structure learning Quantifying uncertainty in graphical models Advancing probabilistic reasoning frameworks Optimizing adjustment identification techniques His 2024 UAI conference publication introduces a novel distance metric for causal discovery, demonstrating significant contributions to machine learning theory with applications in high-dimensional data analysis. This work reflects current trends in interpretable AI and robust causal inference methods.

• Machine Learning
• Causal Inference
• Artificial Intelligence
• +2 more

Nadja van 't Hoff is an Assistant Professor in the Department of Economics at the University of Southern Denmark, Faculty of Social Sciences. Her research lies at the intersection of econometrics and data science, with a strong focus on causal inference methodologies and their applications in public policy, labor economics, and health behavior. Her research interests include: Causal Inference and Identification Strategies Instrumental Variable Analysis and LATE Frameworks Difference-in-Differences Estimation Excise Taxation and Nutrition Policy Returns to Education and Labor Market Outcomes Machine Learning Applications in Econometrics The recent articles highlight a consistent focus on developing and applying advanced causal inference techniques—particularly using multiple instrumental variables and difference-in-differences—to real-world policy questions such as the impact of food taxation and the causal effects of substance use on labor productivity. Her work bridges theoretical econometrics with empirical policy evaluation. Scientific contributions and recognition: Active peer reviewer for Empirical Economics Regular presenter at academic conferences Research contributor to high-impact policy-related projects She is actively involved in research and teaching, having supervised or taught courses in regression analysis and statistics. Her grant and project involvement includes studies on gender differences in health behavior, cannabis and alcohol use effects on labor productivity, and the societal impacts of unhealthy food taxation. She collaborates with prominent economists such as G. Mellace, C. M. Dahl, and A. Lewbel. She is a member of the Department of Economics research unit and contributes to academic service through committee membership and peer review. Her research has been disseminated widely through conference presentations and working papers, with ongoing engagement in both national and international collaborations.

• Causal Inference
• Econometrics
• Instrumental Variable Analysis
• +9 more

Jonas Peters is a Professor of Statistics at ETH Zurich, Switzerland, since 2023. Previously, he held positions at the University of Copenhagen and the Max Planck Institute for Intelligent Systems. His research focuses on causal inference, machine learning, and distribution generalization. He earned his PhD from ETH Zurich and the Max Planck Institute, with earlier studies at the University of Heidelberg and the University of Cambridge. His research interests span causal discovery, machine learning applications in Earth system science, and developing statistical methods robust to distributional shifts. Notable contributions include foundational work on causal inference frameworks, such as the Elements of Causal Inference book and the invariant prediction method. Peters has been recognized with awards like the Test of Time Award (runner-up) at ICML 2022 and the ASA Causality in Statistics Education Award. His work bridges theory and practice, emphasizing real-world applications. Peters has supervised numerous PhD students and postdocs, contributing to advancements in causal learning algorithms and statistical testing. His open positions and community service reflect his commitment to fostering academic collaboration and sustainability in academia. Education: PhD in Mathematics, ETH Zurich and Max Planck Institute (2012) MSc/MPhil Mathematics, University of Cambridge (2007) BSc Mathematics, University of Heidelberg (2006) Peters' publications address causal structure recovery, kernel methods for independence testing, and time series analysis. His work emphasizes causal reasoning in machine learning and climate science, with applications to policy learning and dynamical systems. He is an active member of statistical societies and serves on editorial boards of leading journals. His outreach efforts include popular science articles and books like The Raven's Hat , aiming to make complex concepts accessible. Peters advocates for sustainable academic practices, notably reducing travel for environmental reasons since 2020.

• Causality
• Machine Learning
• Earth System Science
• +5 more

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.

• Causal Inference
• Econometrics
• Innovation Economics
• +3 more