Claudia Strauch is an Associate Professor at the Department of Mathematics, Aarhus University . She is currently on leave and can be reached via email at strauch@math.au.dk. Her research focuses on nonparametric methods for stochastic processes, particularly in diffusion dynamics and control theory. Research Interests: Stochastic control, nonparametric estimation of diffusions, Hamiltonian systems, and machine learning applications to stochastic dynamics. Selected Projects: Exploring nonparametric modeling via stochastic PDEs (2021–2024) Learning diffusion dynamics for optimal control (2021–2024)
Prashant Singh is a Postdoctoral Researcher (Researcher) in the Biocomplexity group at the Niels Bohr Institute, University of Copenhagen. His work focuses on theoretical and statistical physics with applications in biological systems, leveraging analytical methods to solve complex non-equilibrium problems. His research spans statistical physics, non-equilibrium thermodynamics, and theoretical biophysics, investigating phenomena like active matter phase transitions, positional information in boundary-driven systems, and stochastic thermodynamics. Key interests include diffusion with resetting, entropy production inference, and reaction-diffusion modeling, often revealing fundamental trade-offs in biological information processing and thermodynamic efficiency. Analysis of his 2024-2025 publications shows a cohesive focus on exact solutions for non-equilibrium systems, particularly single-file diffusion under resetting and active matter with long-range interactions. Recurring themes include cost-time optimization, boundary effects on information transmission, and the thermodynamic constraints of biological computation. No information is available regarding student advising or research grants. He is embedded within the Biocomplexity research group at the Niels Bohr Institute, which employs theoretical frameworks to study collective dynamics in biological systems, including pattern formation, cellular information processing, and emergent behavior in active matter.
Morten Mørup is Professor at DTU Compute, Technical University of Denmark. His research develops machine learning methods for life sciences, focusing on tensor decompositions, Bayesian inference, and complex network analysis. Education: PhD from DTU Informatics (2008) with research visits to Stanford and UC Berkeley. Research Expertise: Unsupervised learning, neuroimaging data analysis, and statistical network modeling applied to neuroscience and educational analytics. Awards: EliteForsk travel scholarship (2006), Lundbeck Foundation Fellowship (2012), and Ingeborg og Leo Dannins Scholarship (2021). Recent Publications focus on graph representations, educational data mining, and speech separation models.
Anders Rahbek is a Professor at the Department of Economics, Faculty of Social Sciences, University of Copenhagen. He has held this position since 2007 and has also served as a visiting Professor at Oxford University during Hilary Terms 2011-2012. His academic career at the University of Copenhagen spans from Assistant Professor (1996-1999) to Associate Professor (1999-2007) and finally to Professor (2007-present). Education: PhD in Econometrics, Institute of Mathematical Sciences (IMF), University of Copenhagen, 1996 Cand.Scient.Oecon (M.Phil), Mathematics and Economics, IMF, 1992 MSc in Econometrics, London School of Economics, 1991 MA in Mathematics, University of Pennsylvania, 1988 Professor Rahbek's research focuses on financial econometrics and time series analysis , with particular expertise in bootstrap methods, GARCH and volatility modeling, cointegration analysis, duration modeling, and count models. His work bridges theoretical econometrics with practical applications in financial and macroeconomic data analysis. He has developed innovative approaches for analyzing time series with time-varying volatility and has made significant contributions to bootstrap methodology in econometrics. His recent publications (2020-2025) demonstrate a continued focus on boundary problems in statistical testing, bootstrap methodology for complex time series models, and applications to financial volatility modeling. Key themes include GARCH-X models, cointegration in high-dimensional settings, Hawkes processes, and threshold autoregressions. His work often involves collaboration with leading econometricians like Giuseppe Cavaliere, Heino Bohn Nielsen, and Rasmus S. Pedersen. Scientific Awards: NYKREDIT RESEARCH AWARD (2014) Research Prize 2012: Reinholdt W. Jorck and Wife's Foundation (2012) Professor Rahbek has secured significant research funding as Principal Investigator, including multiple DFF-Advanced Grants (2012-2026) focusing on bootstrap methods and duration models in econometrics. He serves as Associate Editor for Econometric Theory and has previously held editorial positions at Econometrics Journal, Scandinavian Journal of Statistics, and Journal of Time Series Analysis. His Google Scholar h-index stands at 30 (as of December 2023). He is actively involved in international research networks, having initiated the Econometric Time Series European Research Network (ETSERN) in 2008. His teaching includes Financial Econometrics, Advanced Econometrics, and introductory Econometrics courses, with focus on volatility models, cointegration, and likelihood-based methods.
Trifon I. Missov is an Associate Professor at the University of Southern Denmark (SDU), affiliated with the Faculty of Business and Social Sciences and the Interdisciplinary Centre on Population Dynamics (CPop). His research focuses on mortality modeling, actuarial science, and the demography of aging. He investigates topics such as mortality deceleration, longevity patterns, and the impacts of pandemics like COVID-19 on life expectancy. His work integrates statistical methods with demographic theory to address questions about aging populations and health inequalities. Education details are not explicitly mentioned in the provided text. His contributions include developing methods for estimating mortality improvements at advanced ages and analyzing life expectancy losses during the pandemic. Missov collaborates on projects like the SCOR Chair in Mortality Research and contributes to databases such as COMPADRE and COMADRE. His research also explores theoretical frameworks for understanding human aging, including evolutionary perspectives and heterogeneity in aging processes. Key themes in his publications include mortality forecasting using modal age at death approaches, Bayesian inference for historical mortality data, and the application of regression trees to study mortality deceleration. He has contributed to international studies on lifespan inequality and the demographic consequences of public health crises.
David Lando is a Professor of Finance at Copenhagen Business School, affiliated with the Department of Finance and the Center for Big Data in Finance (BIGFI). He holds a Master’s degree in Mathematics-Economics from the University of Copenhagen and a PhD in Statistics from Cornell University. His research focuses on credit risk modeling, financial risk management, derivatives markets, and corporate capital structure. He authored a seminal monograph on credit risk modeling (Princeton University Press) and has published in top journals like Econometrica and the Journal of Financial Economics. Education: MSc (Math-Econ, U Copenhagen), PhD (Statistics, Cornell) Roles: Former Director of the Center for Financial Frictions (2012–2022), former Chairman of Denmark’s Financial Supervisory Authority (2018–2020), current board member of pension fund P+ His research explores liquidity dynamics in credit markets, sovereign debt relief impacts, and systemic risk. Recent work analyzes emerging market credit spreads post-debt relief and bank equity risk. His articles often bridge theoretical models with empirical validation, emphasizing practical applications for regulators and practitioners. Awards: Best Paper on Quantitative Investments (2006) He advised PhD/Master’s students and consulted for institutions like Danmarks Skibskredit and Shell. His teaching includes advanced credit risk courses, and he maintains an active role in policy through board memberships and regulatory advising. Lando leads research initiatives at CBS and collaborates with international institutions, contributing to both academic discourse and real-world financial policy.
Ege Holger Rubak is an Associate Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science. His research focuses on spatial statistics, point processes, and random matrix theory with applications in computational methods and data analysis. He actively contributes to open-source statistical software development and serves as Principal Investigator for the Novo Nordisk Foundation-funded project AI - Aalborg Intelligence . Academic Rank: Associate Professor Institution: Aalborg University Department: Mathematical Sciences Research Interests: Spatial statistics, determinantal point processes, composite likelihood Email: rubak@math.aau.dk Research Trends: His recent publications emphasize spatial point process diagnostics, simulation methods for determinantal processes, and statistical modeling of complex systems. The 2025 dataset on residential household occupancy reflects interdisciplinary applications in smart energy systems. Collaboration: He collaborates extensively with researchers like Adrian Baddeley, Jesper Møller, and Rasmus Waagepetersen across institutions and disciplines.
Teddy Groves is a Tenure Track Researcher at the Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark (DTU), focusing on quantitative modeling of cell metabolism and computational methods. His work bridges systems biology, machine learning, and biotechnology. Research Themes : Quantitative cell metabolism, neurovascular coupling, CRISPR-based apoptosis resistance, and data-driven bioreactor optimization. Supervision : Actively supervises PhD students in projects related to human glycolysis, multiscale modeling, and cyanobacteria enzyme allocation. Key Contributions : Developments in Bayesian regression, high-dimensional pathway visualization, and CRISPR knockouts in CHO cells.
Søren Kyllingsbæk is a Professor in Cognitive Psychology at the University of Copenhagen, affiliated with both the Department of Psychology and the Department of Computer Science . His research bridges experimental psychology, mathematical modeling, and cognitive neuroscience, focusing on visual attention, short-term memory, and intentional action selection. Education: Cand. Psych. (1997), PhD (2002), Dr. Psych. (2015) at the University of Copenhagen. Kyllingsbæk co-developed the Neural Theory of Visual Attention (NTVA) with Claus Bundesen and Thomas Habekost, aiming to unify perception, attention, and memory. Recent collaborations include Susanne Ditlevsen (Mathematical Sciences) and Barry Giesbrecht (UCSB) on extending NTVA, and Thor Grünbaum on intentional action theory via the CoInAct research group . His 15 most recent articles (2011–2025) emphasize computational models of attention (e.g., Poisson random walk), neural mechanisms of intention retrieval, and interdisciplinary applications of cognitive theory to fields like human performance optimization for the Danish Frogman Corps. Key subfields include visual cognition, biased competition, and agency. Scientific Awards include the Sapere Aude grant (2010) and Fellow status at the Psychonomic Society (2017). He supervises students at all levels in cognitive, neuropsychology, and experimental psychology.
Ana Alina Tudoran is an Associate Professor at Aarhus University, specializing in quantitative methods within business intelligence. Her academic work bridges machine learning, data mining, and consumer behavior analysis. Primary affiliation: Department of Economics Research focus areas: Artificial Intelligence, Customer Lifetime Value modeling, and Hybrid Intelligence systems Notable projects include pandemic-era consumer behavior studies and IoT privacy reviews Her recent publications span supply chain optimization, organizational text analytics, and responsible AI deployment. Tudoran actively contributes to methodological advancements through hybrid PLS-SEM/machine learning frameworks and is a frequent speaker at management conferences.
Martin Bøgsted is a Professor at Aalborg University, serving as Center Director at Clinda (Center for Clinical Data Science) within the Clinical Institute of the Faculty of Health Sciences. With over 30 years of experience spanning academia, public sector, and industry, he leads significant research initiatives in clinical data science and artificial intelligence applications in healthcare. His research interests span clinical data science, artificial intelligence, applied probability theory, statistics, and machine learning with applications in medicine, agriculture, and telecommunications. His fingerprint analysis reveals specialized expertise in Diffuse Large B-Cell Lymphoma (100%), B Cell research (74%), Multiple Myeloma (41%), Overall Survival analysis (40%), Malignant Neoplasm studies (34%), and Personalized Medicine (23%). His recent publications demonstrate a strong focus on AI applications in healthcare, privacy-preserving synthetic data generation, clinical trial participation, and time-to-event prediction models. His work shows a clear trend toward integrating AI for personalized risk assessment and clinical interventions, with significant emphasis on ethical development of health data technologies. Martin Bøgsted actively supervises master's and PhD students, with records indicating 14 PhD students supervised. He leads major research projects including SE3D (Synthetic health data: ethical development and deployment via deep learning approaches) funded by the Novo Nordisk Foundation (2024-2028) and ARISTOTELES (Integrating AI for Personalized Risk Assessment) (2023-2028). He directs the Center for Clinical Data Science (CLINDA) at Aalborg University, which focuses on developing innovative infrastructure, artificial intelligence, and digital tools for multimodal data fusion and predictive modeling in the healthcare sector. His leadership extends to advisory roles including the Working Group for the National Genome Database and the Advisory Board for Research and Infrastructure at the Danish National Genome Center.
Bo Friis Nielsen is a Professor and Head of Section in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on queueing theory, particularly matrix-analytic methods, with applications in telecommunication systems and service optimization. He currently supervises PhD students working on projects with Danske Bank (call center efficiency) and DSB S-tog (passenger counting for transport planning). His teaching includes probability theory and stochastic simulation. Education/Background: Formal academic qualifications not explicitly listed in the text. Affiliations: Head of the Mathematical Modelling Section since 2003, affiliated with DTU Compute. Research interests span theoretical advancements in queueing models and practical applications in transportation, healthcare, and industrial systems. He has collaborated with companies like Danske Bank and DSB S-tog, addressing real-world operational challenges. Recent work includes predictive maintenance strategies for multi-component systems and railway defect analysis. Publications emphasize stochastic modeling, maintenance optimization, and transportation analytics. Projects involve developing tools for public transport efficiency and reliability engineering in wind turbines and power plants.
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.
Alexandros Gelastopoulos is a Research Fellow at the Institute for Advanced Studies in Toulouse with a PhD in Mathematics from Boston University (2019). His research employs mathematical modeling to study biological and social systems, focusing on probability theory, stochastic processes, and reinforcement dynamics in social contexts like cumulative advantage and ranking-based behaviors. Primary research interests include analyzing how popularity rankings influence social systems, studying brain oscillations for memory functions, and exploring decision-making paradoxes. His work often bridges mathematical rigor with behavioral sciences to uncover mechanisms driving social inequalities and neural computations. His publication trends show strong emphasis on social dynamics (e.g., lock-in effects, reinforcement processes) and computational neuroscience (e.g., neural rhythms, memory substrates), using stochastic modeling and empirical validation across disciplines. European Commission's Seal of Excellence (2024) Collaborates extensively with international researchers on projects involving mathematical sociology and behavioral experiments. Teaches game theory and mathematical courses while developing expository materials on real analysis and information theory.
Rudolf Hanel is an Associate Professor at the Medical University of Vienna and a faculty member at the Complexity Science Hub. His interdisciplinary research bridges theoretical physics, complex systems, medical imaging, and socio-economic modeling. He is deeply involved in advancing the foundations of statistical mechanics and non-equilibrium thermodynamics. His research interests include complex systems, non-equilibrium thermodynamics, information theory, medical robotics, and social physics. He investigates how systems evolve far from equilibrium, focusing on phase transitions, tipping points, and the emergence of structure. His work applies these principles to diverse domains such as firm dynamics, social cohesion, and pandemic response. The recent trend in his publications reveals a strong focus on generalized entropy, sample space reduction, network-based social modeling, and practical applications in public health. His articles span foundational physics, computational medicine, and socio-economic systems, reflecting a unifying framework of complexity science across disciplines. Thermodynamics of driven systems Generalized entropy and information theory Social fragmentation and homophily Pooled testing for pandemics Firm performance via information consumption Structure-forming systems Rudolf Hanel has received no explicitly mentioned scientific awards in the provided text. He has not been stated to advise any formal students, though he collaborates widely with researchers such as Stefan Thurner, Jan Korbel, and Peter Klimek. He has contributed to major interdisciplinary grants and projects, particularly through the Complexity Science Hub, including work on pandemic testing strategies and economic modeling. He is a key member of the Complexity Science Hub, where he collaborates on foundational and applied research in complex systems. The Hub serves as a central platform for his work in integrating physics-inspired models into social, biological, and economic systems.
