David Glavind Skovmand is an Associate Professor in Financial Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He specializes in interest rate modeling, pricing of derivatives, and risk management. Education: PhD (2008) and MSc in Economics (2004) from Aarhus University His research focuses on mathematical modeling of benchmark interest rates like LIBOR, SOFR, and ESTER. Key areas include transition risk, multicurve term structures, and backward-looking benchmarks. He applies stochastic processes and numerical analysis to derivative pricing and risk assessment. Recent publications analyze LIBOR transition complexities, SOFR term structure dynamics, and multicurve frameworks. His work emphasizes practical implications for financial markets and regulatory compliance. Skovmand also teaches and supervises in mathematical finance, financial econometrics, and numerical analysis. He serves as Head of Studies at his department and contributes externally as a lecturer at Copenhagen Business School and Reykjavík University.
Nikolaj Siersbæk is a Lecturer at the Department of Economics, Faculty of Social Sciences, University of Southern Denmark. His research spans microeconomics, financial crisis analysis, health economics, and ethical policy frameworks. Education: PhD in Economics (University of Southern Denmark, 2019) Research Interests: He focuses on applied microeconomics, analyzing disincentive effects in welfare systems, multidimensional welfare comparisons during financial crises, and health economics issues like migraine treatment costs and hereditary angioedema quality-of-life metrics. Publication Trends: Recent works emphasize health economics (migraine therapies, angioedema), economic welfare analysis (EU financial crisis), and policy-driven microeconomic modeling. Scientific Awards: Scholarship from Bank of Funen (2016) Scholarship from The Denmark-America Foundation (2016) Scholarship from The Oticon Foundation (2016) Scholarship from The Augustinus Foundation (2016) Scholarship from Knud Højgaard's Foundation (2016) Teaching: Taught microeconomics to BSc Economics and Mathematics-Economics students (2014-2018), including business context applications.
Arthur Matsuo Yamashita Rios De Sousa is an Assistant Professor in the School of Computing at the Tokyo Institute of Technology, where he conducts interdisciplinary research bridging applied mathematics, statistical physics, and data science. His work focuses on the modeling and analysis of complex systems through stochastic processes, time series analysis, and network science. His primary research interests include: Applied Mathematics and Probability Theory Statistical Physics and Econophysics Complex Systems and Network Science Time Series Analysis and Forecasting Symbolic Dynamics and Entropy-Based Methods Power-Law and Heavy-Tailed Distributions His recent publications demonstrate a consistent focus on developing quantitative methods for analyzing financial time series, sales data, and other real-world complex systems. The articles span topics such as volatility modeling, network-based market analysis, multiscale entropy, and symbolic dynamics, reflecting a strong integration of theoretical physics and practical data science applications. Notable trends in his research include the use of entropy measures for regime detection, modeling of heavy-tailed phenomena in economics, and the application of complex networks to multivariate systems. These efforts contribute to both fundamental understanding and practical tools in econophysics and data-driven science. There are currently no listed scientific awards or honors in the provided text. Dr. Yamashita advises students in computational and quantitative research, particularly in areas related to data analysis of complex systems. While specific grant funding is not mentioned, his sustained publication record suggests active research support. He likely contributes to collaborative projects involving financial data modeling, nonlinear dynamics, and interdisciplinary applications of statistical physics. He is involved in academic events such as the Econophysics Colloquium and workshops at the Complexity Science Hub, indicating participation in international research networks focused on complex systems science.
Peter Yen is a Senior Research Fellow at the School of Physical and Mathematical Sciences (SPMS), Nanyang Technological University (NTU), Singapore. His research lies at the intersection of physics, data science, and economics, with a focus on applying topological methods to complex systems, particularly financial markets. He holds a Ph.D. in Condensed Matter Physics from National Central University (NCU), Taiwan. Ph.D. in Condensed Matter Physics, National Central University (NCU), Taiwan Postdoctoral experience at NCU and National Sun Yat-Sen University (NSYSU) His research interests center on complex adaptive systems , leveraging topological data analysis (TDA) and persistent homology to extract meaningful structural features from high-dimensional data. His work spans condensed matter physics , biophysics , and econophysics , with a recent emphasis on identifying topological signatures preceding financial market crashes. He also has expertise in first-principles calculations of quantum materials, including multiferroics and 2D systems. The recent publications highlight a strong trend toward interdisciplinary applications of topology , particularly in financial and economic systems. The articles demonstrate a consistent use of persistent homology to analyze time series, networks, and market dynamics, aiming to detect early warnings of systemic risk. Keywords include econophysics, data science, computational modeling, and complex networks. Currently, no scientific awards are listed. Peter Yen has not advised any students listed in the provided information, and no grant details are available. His career trajectory shows a strong research focus with affiliations at leading institutions in Taiwan and Singapore. He is actively involved in the complexity science community, participating in events such as the Econophysics Colloquium and the Complexity Science Hub Workshop, indicating engagement with international research networks.
Jing Qin is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where she contributes to research and teaching in mathematical statistics and applied probability. She is also affiliated with the SDU Climate Cluster, reflecting her interdisciplinary engagement in climate and health modeling. Research Interests: Her work spans actuarial mathematics, financial mathematics, bioinformatics, and mathematical modeling. She specializes in extreme value theory, conditional risk measures, tail dependence, and statistical methods for RNA folding. Her research integrates rigorous theoretical development with practical applications in insurance, finance, and biomedical data analysis. The recent publications indicate a strong trend in developing robust, nonparametric estimators for conditional extremes, particularly in bivariate and covariate-adjusted settings. Her work often involves asymptotic analysis and simulation studies, with applications in reinsurance, epidemiology, and genomics. Collaborative research with Y. Goegebeur and A. Guillou is prominent. Scientific Awards: No specific awards listed in the provided text. Advising and Grants: Jing Qin supervises BA, MA, and PhD students in mathematical and statistical research. She is actively involved in externally funded projects, including as Principal Investigator (PI) on a research project analyzing the impact of extreme weather on emergency hospital demand. She has participated in projects funded by the Novo Nordisk Foundation, Carlsberg Foundation, and the Danish Research Council, covering areas from intellectual disability genetics to RNA bioinformatics. Labs and Teams: She is part of collaborative networks in statistical extremes and bioinformatics, and contributes to the SDU Climate Cluster, suggesting active participation in interdisciplinary research teams focused on climate, health, and data science.
Nicolai Siim Larsen is an Assistant Professor (Tenure track) in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in statistical modeling with applications in finance and healthcare analytics. His work bridges theoretical statistics and real-world data challenges. Education : Ph.D. in Stochastic Financial Models based on Matrix-Analytic Methods, Technical University of Denmark (2022) Dr. Larsen's research centers on multivariate phase-type distributions and matrix-analytic methods , with significant contributions to financial risk modeling and healthcare analytics . His expertise spans survival analysis, sensor data processing, and price optimization frameworks, addressing complex stochastic phenomena in insurance and disease progression monitoring. Analysis of his publications reveals a cohesive focus on advancing numerical computational methods for multivariate data structures. His 2022 Ph.D. thesis established foundations for joint density functions and infinitely divisible distributions, with subsequent work directly applied to commercial pricing strategies and edema patient monitoring systems. Statistical analysis and price optimisation of DJ rental services (2022) AI Denmark: Monitoring disease progression in Oedema patients (2022) KomDigital: Optimal pricing strategies for price monitoring (2022) Stochastic Financial Models based on Matrix-Analytic Methods (PhD project 2018-2023) He actively contributes to academic service as an internal examiner for Statistical Genetics (2025) and instructor for R-based data science workshops, demonstrating commitment to both research leadership and pedagogical development within DTU's Statistics and Data Analysis section.
Matthias Albrecht Fahrenwaldt serves as an Adjunct Professor in the Department of Mathematical Sciences at the University of Copenhagen, contributing to the institution's academic mission from his office at Universitetsparken 5 in Copenhagen, Denmark. His appointment falls under the university's overarching structure for mathematical research and education. His scholarly focus spans foundational and applied mathematical disciplines, with primary emphasis on Mathematics , Probability Theory , Statistics , and Financial Mathematics . These interconnected fields drive his theoretical investigations and potential applications in quantitative analysis. Professional correspondence may be directed to fahrenwaldt@math.ku.dk , reflecting his active engagement with the academic community despite the limited biographical details available in the source material.
Dagim Belay is a Tenure Track Assistant Professor at the Department of Food and Resource Economics, University of Copenhagen. He also maintains significant affiliations including as an Invited Researcher at JPAL, MIT (2024-Present), Founding Member of NIERA - Network of Impact Evaluation Researchers in Africa (2018-Present), Research Fellow at CeBIL - Center for Advanced Studies in Biomedical Innovation Law, UCPH (2018-Present), Fellow at BITSS - Berkeley Initiative for Transparency in the Social Sciences, UC Berkeley (2017-Present), and Fellow at the Center for Effective Global Action (CEGA), UC Berkeley (2016). Dagim Belay earned his Ph.D. in Economics from the University of Copenhagen (UCPH), where he also completed a postdoctoral position. He has been a visiting fellow at both UC Berkeley and Harvard University, establishing a strong international academic network. His primary research focuses on Applied Microeconomics (Agriculture, Environment, and Natural Resources) and Development Economics , with secondary interests in Health Economics and Economics of Innovation and Intellectual Property Rights . His current research agenda explores asymmetric information in economic decision-making, technological innovation in agriculture, climate change adaptation and mitigation strategies, agricultural and environmental externalities, economics of antimicrobial resistance, intellectual property rights frameworks, and impact evaluation methodologies. Analysis of his recent publications reveals a consistent focus on agricultural policy, antimicrobial resistance, and development economics. His work typically employs economic theory, empirical analysis, and randomized experiments to generate actionable policy insights on global challenges in agriculture, health, and environmental sustainability. His research demonstrates increasing international collaboration, particularly with researchers in Africa and Europe, addressing critical issues such as antibiotic use in agriculture, food safety regulations, and market mechanisms for sustainable farming practices. His notable professional recognitions include: Invited Researcher, JPAL, MIT (2024-Present) Founding Member, NIERA - Network of Impact Evaluation Researchers in Africa (2018-Present) Research Fellow, CeBIL - Center for Advanced Studies in Biomedical Innovation Law, UCPH (2018-Present) Fellow, BITSS - Berkeley Initiative for Transparency in the Social Sciences, UC Berkeley (2017-Present) Fellow, Center for Effective Global Action (CEGA), UC Berkeley (2016) Dagim Belay teaches courses in Impact Evaluation, Agricultural and Food Policy, and Development Economics. His research has garnered significant attention from academic and policy communities, with several publications being highlighted by multiple news outlets. His work shows substantial engagement on academic platforms, with some papers accumulating dozens of readers on Mendeley and other scholarly networks. His research collaborations span multiple countries including Denmark, England, Senegal, Ethiopia, and the United States, reflecting a robust international network dedicated to addressing global agricultural and health challenges through rigorous economic analysis and policy-relevant research.
Mogens Steffensen holds key positions at the University of Copenhagen's Faculty of Science: Head of the Department of Mathematical Sciences (since 2023) Professor of Life Insurance Mathematics (permanently since 2012, initially appointed 2008) Editor of the Scandinavian Actuarial Journal (since 2006) Associate Editor of Insurance: Mathematics and Economics (since 2013) His research spans core areas in quantitative finance and insurance: Actuarial mathematics with emphasis on life insurance and pension systems Financial mathematics including stochastic portfolio optimization Applications of Markov processes and jump-diffusion models in risk management Recent publications (2024-2025) demonstrate interdisciplinary innovation across actuarial science and financial engineering, addressing challenges like epidemic impacts on insurance, fairness in risk classification, and optimal reinsurance design. These works consistently employ advanced mathematical techniques including stochastic control theory and smoothing methods to solve real-world problems in pension planning and market valuation. Scientific Awards: No specific awards were mentioned in the provided text. Advising and Grants: The available documentation does not specify student supervisees or research grants. As department head and active researcher, Professor Steffensen undoubtedly oversees academic mentoring and secures funding, but concrete details remain unreported in the source materials.
Yevhen Havrylenko serves as a Postdoctoral Researcher in the Department of Mathematical Sciences at the Faculty of Science, University of Copenhagen, Denmark. His appointment is based at Universitetsparken 5, 2100 København Ø, where he conducts advanced research in quantitative finance and actuarial modeling, with contact details including phone +4535327906 and email yh@math.ku.dk. His research program integrates mathematical finance, actuarial science, and machine learning to solve complex risk management problems. Key focus areas include stochastic volatility modeling for portfolio optimization under value-at-risk constraints, neural network applications for detecting variable interactions in generalized linear models, and game-theoretic approaches to risk-sharing mechanisms in insurance products. This interdisciplinary work bridges theoretical mathematics with practical financial and insurance industry challenges, emphasizing computational rigor and real-world applicability. Analysis of his recent publications reveals a cohesive research trajectory centered on mathematical innovation in risk assessment. His work consistently employs dynamic programming, stochastic processes, and machine learning to address market incompleteness, variable interaction detection, and strategic insurer-reinsurer dynamics. The publications demonstrate strong methodological synergy across finance and insurance domains, with increasing emphasis on computational techniques for complex risk modeling. No scientific awards, student advisement records, or research grants are documented in the available profile. Similarly, no affiliations with specific research laboratories or collaborative teams beyond publication co-authors (Escobar-Anel, Zagst, Heger, Hinken) are specified.
