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.
Matthias Oliver Wilhelm is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the Quantum Mathematics research group. His work focuses on advanced theoretical physics topics including scattering amplitudes in gauge/gravity theories, Feynman integrals, special functions, and applications of machine learning in physics. He has contributed to groundbreaking research at the intersection of quantum field theory and mathematical physics, particularly in understanding gravitational wave phenomena and high-energy particle interactions. Research Interests: His research combines quantum field theory with algebraic geometry and computational methods, exploring topics like elliptic Feynman integrals, post-Minkowskian expansions, and machine learning-driven amplitude calculations. Recent work includes leveraging Calabi-Yau manifolds for gravity-related Feynman integrals and developing transformer-based algorithms for scattering amplitude computations. Awards: He received the Velux Grant - Villum Young Investigator in 2018, recognizing his innovative contributions to theoretical physics. Projects: Leveraging Algebraic Geometry for High-Precision Fundamental Physics (2024-2028, DFF-funded) Thermodynamics of strongly coupled Quantum Field Theory (2019-2027, private foundation-funded) Key Themes in Recent Work: His articles emphasize novel computational techniques (e.g., machine learning for integration-by-parts reduction), formal developments in scattering amplitude theory, and geometric approaches to quantum gravity problems. Notable contributions include classifying Feynman integral geometries for black-hole scattering and advancing elliptic function methodologies in perturbative QFT.
Rasmus T. Varneskov is a Professor in the Department of Finance at Copenhagen Business School, Denmark, with an active research profile evidenced by publications through 2025. His work bridges theoretical econometrics and financial applications, focusing on methodological innovations for market volatility and return prediction. Research interests include: Econometrics Financial Economics Time Series Analysis Volatility Modeling Asset Pricing Structural Change His recent publications (2022-2025) reveal a concentrated focus on bootstrap techniques for volatility estimation and robust inference in predictive regressions. Key contributions address challenges in high-frequency financial data, Laplace transforms for volatility metrics, and handling persistent autoregressive processes with structural breaks, primarily through advanced statistical modeling. Scientific Awards: No awards, fellowships, or medals are documented in the provided text. Advising: Varneskov has supervised 5 works (likely graduate theses), though student names and grant funding specifics are absent. His mentorship activity aligns with his role as a research-active faculty member in econometrics.
Jan Pries-Heje is a Professor at the Department of People and Technology at Roskilde University, specializing in Information Systems and leading the User Driven IT Innovation Research Group. His academic career includes roles at the IT University of Copenhagen and Gothenburg, with expertise in software engineering, project management, and agile methodologies. He holds M.Sc. and Ph.D. degrees from Copenhagen Business School and is certified in ISO 9000 auditing and Bootstrap assessment. Education: cand.merc.dat. (Master of Mercantile Data) Ph.D. in Information Systems (Copenhagen Business School) Research Interests: Project Management Methodologies Design Science Research Agile and Sustainability Practices IT-Driven Organizational Change Public Sector IT Challenges His work explores digital transformation, resistance to change, and sustainability design principles, with a focus on real-world implementation challenges. Professional Roles: Chairman of IFIP Technical Committee 8 (Information Systems) Past President of IRIS (Scandinavian IS Association) Editorial Board Member of MIS Quarterly , Information Systems Journal , and Business & Information Systems Engineering Key Projects: "SourceIT" (16M DKK Innovation Consortium) Green IT-procurement and Game For Green initiatives Advisory & Grants: Consultant for Rigsrevisionens Evalueringspanel and contributor to municipal IT implementation studies. His projects emphasize practical application of theoretical frameworks. Labs/Teams: Leads the User Driven IT Innovation Group, focusing on co-creative methods and boundary management in digital transformation.
Chi Zhang is a Postdoctoral Researcher at the Niels Bohr Institute, University of Copenhagen, specializing in theoretical high energy physics, astroparticle physics, and gravitational physics. His research focuses on advanced mathematical structures in quantum field theory with applications to particle physics and cosmology. His primary research interests include: Theoretical High Energy Physics Scattering Amplitudes Feynman Integrals Elliptic Polylogarithms Quantum Field Theory Particle Physics Analysis of his publication record reveals a concentrated effort on developing computational frameworks for multi-loop scattering amplitudes in supersymmetric gauge theories, particularly using elliptic functions and symbology techniques. His work bridges theoretical high-energy physics with mathematical innovation, targeting precision calculations relevant to collider experiments and gravitational physics. Key collaborations include M. Wilhelm, A. Spiering, and R. Morales across multiple high-impact journals.
Zhengwen Liu is an Assistant Professor at the Niels Bohr Institute, University of Copenhagen, specializing in Theoretical High Energy, Astroparticle and Gravitational Physics. He joined the Niels Bohr International Academy on October 1, 2022, and maintains his office at Blegdamsvej 17, 2100 Copenhagen Ø. His research focuses on the intersection of gravitational physics, high-energy theory, and astroparticle phenomena, with particular emphasis on binary black hole dynamics and gravitational wave physics. His work centers on post-Minkowskian theory for binary systems, employing scattering amplitudes, effective field theory, and computational techniques to model gravitational dynamics at high precision. Recent publications demonstrate expertise in conservative dynamics, radiation reaction, and soft emission theorems across quantum field theory and general relativity. He frequently collaborates with leading researchers including Dlapa, Kälin, and Porto, producing high-impact work in journals like Physical Review Letters and Journal of High Energy Physics . Analysis of his 2023-2025 publications reveals a dominant focus on fourth and fifth post-Minkowskian orders in binary dynamics, with significant contributions to gravitational self-force calculations and machine learning applications for gravitational integrals. His research bridges theoretical high-energy physics with gravitational wave astronomy, addressing both foundational aspects of general relativity and practical waveform modeling. No scientific awards were mentioned in the provided materials. No information regarding student advising or research grants was available in the source texts. Liu operates within the Theoretical High Energy, Astroparticle and Gravitational Physics research group at the Niels Bohr Institute, which forms part of the broader Cosmic Dawn Center (DAWN) collaboration. This group specializes in gravitational wave theory, high-energy scattering in curved spacetime, and connections between quantum field theory and gravity through amplitude techniques.
Jesper Riis-Vestergaard Sørensen is a Tenure Track Assistant Professor in the Department of Economics at the University of Copenhagen, which is part of the Faculty of Social Sciences. His work focuses on developing advanced econometric methods for analyzing complex economic data. His academic background includes: PhD in Economics from University of California at Los Angeles (2012-2018), with dissertation titled "Essays on Nonparametric and High-Dimensional Econometrics" Sørensen's research centers on methodological innovations in econometrics, particularly addressing challenges in high-dimensional data analysis. His work on semiparametric and nonparametric estimation provides economists with flexible tools that avoid restrictive assumptions about functional forms. His contributions to generalized entropy models offer innovative approaches to modeling economic choices that balance theoretical rigor with empirical adaptability. He has also made significant advances in specification testing for semiparametric models and discrete choice modeling under uncertainty, bridging economic theory with practical applications in consumer behavior analysis. His publication record from 2011-2025 reveals a clear progression from foundational work on household demand analysis toward increasingly sophisticated methodological contributions in high-dimensional econometrics. Recent publications (2021-2025) demonstrate particular expertise in parameter selection methods for complex models, with his 2025 paper on vector autoregressions representing cutting-edge work in time series analysis. A recurring theme across his work is the integration of mathematical optimization techniques (particularly convex analysis) with econometric methodology, as exemplified by his research connecting McFadden's discrete choice theory with Rockafellar's optimization principles. Sørensen has been actively engaged with the professional community through presentations at major conferences including the Econometric Society European Meeting (2021), Econometric Society North American Summer Meeting (2021), and the Econometric Society/Bocconi University Virtual World Congress (2020). His invited talk at Duke University's Class of 2018 Microeconometrics Conference highlights recognition of his expertise in specialized methodological areas.
Lasse Bork is a Professor of Finance at the Aalborg University Business School, part of Aalborg University's Faculty of Humanities and Social Sciences. His research focuses on asset pricing, financial econometrics, and empirical finance, with a particular emphasis on macro-finance linkages, housing markets, and energy markets. He has published in top journals such as Management Science , Journal of Banking and Finance , and Real Estate Economics . Bork also holds an external role as Senior Quantitative Researcher at Norlys Energy Trading A/S. Research Interests: Bork's work explores topics like commodity currency hypotheses, housing sentiment indices, and the effects of unconventional monetary policy. His methodologies include factor analysis, dynamic model averaging, and state-space models. Recent projects include analyzing climate risk reporting by pension funds and the impact of Federal Reserve asset purchases on economic outcomes. Awards: Recipient of 'Teacher of the Year 2014' (Aalborg University) Recipient of 'Teacher of the Year 2014' at the Faculty of Social Sciences Årets underviser 2014 (Aalborg University) Grants & Projects: Climate Risk Reporting by Pension Funds: International Evidence (2021–2023) Nordjysk Konjunkturbarometer 2 (2007–2019) Labs/Teams: Collaborations include work with institutions such as KU Leuven, Aarhus University, and participation in international conferences on financial econometrics and macroeconomic policy.
Professor Armelle Guillou holds a faculty position at the University of Strasbourg's Department of Mathematics, with an adjunct affiliation at the University of Southern Denmark. Her statistical research specializes in extreme value theory, bootstrap methods, and inference for censored data. Research develops methodologies for extreme quantile estimation, tail dependence modeling, and risk measure applications, particularly in finance and reinsurance contexts. She directs doctoral research in statistical extremes and dependence modeling. Honored with the Prix Guy Ourisson (2011) for outstanding research contributions.
Owen R. Jones is an Associate Professor in the Department of Biology at the University of Southern Denmark (SDU), affiliated with research groups including CPop, PopBio, and the SDU Climate Cluster. His work focuses on life history variation, population dynamics, and conservation across species. Education: PhD, Biology – Imperial College London (2004) MSc, Ecology – University of Aberdeen (1998) BSc, Biology – University of York (1997) His research centers on understanding the patterns, causes, and consequences of life history variation across the tree of life. Key interests include aging, population modeling, biodiversity, and the impact of climate change on species demography. He utilizes large datasets and statistical tools to analyze demographic trends in both plants and animals. His recent publications show a strong trend in applying computational and statistical methods to ecological questions, including the development of the R package mpmsim for simulating matrix population models and studies on eelgrass, dormice, and forest herbs. His work integrates high-resolution data to model population responses under environmental change. Scientific Awards: Cozzarelli Prize (2016) He has been actively involved in teaching courses such as 'Data handling, visualization and statistics' and 'Population and Evolution'. He has supervised multiple research projects and mentored students, though specific names are not listed. He contributes to major datasets like the COMPADRE Plant Matrix Database and is involved in large-scale collaborative research on climate change and citizen science initiatives. He is active in public engagement, having participated in media appearances and guest lectures on topics such as human demography and species resilience to climate change. His research network spans international collaborations across Europe and beyond.
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.
Rasmus Søndergaard Pedersen is Associate Professor at the Department of Economics, University of Copenhagen , Faculty of Social Sciences. His research focuses on financial and time-series econometrics, with special emphasis on heavy-tailed distributions, time-varying volatility and GARCH-type models. Education: Ph.D. in Economics, University of Copenhagen (2012–2015) M.Sc. in Economics (cand.polit.), University of Copenhagen (2010–2012) Exchange student, University of California, San Diego (2010–2011) B.Sc. in Economics, University of Copenhagen (2006–2010) Research Interests: His work spans time-series econometrics, theoretical econometrics, financial econometrics, multidimensional time-series models, GARCH and BEKK specifications, heavy-tailed processes, bootstrap inference on parameter boundaries, high-dimensional VARs and robust inference in financial markets. Scientific Awards: Winner, Econometric Game 2012 Selected Young Economist, 5th Lindau Meeting on Economic Sciences Research Visits & Collaboration: Visiting researcher, Imperial College London (Jan–Jun 2014) Active participant in Econometric Society World Congress, Lindau Meetings, EC² conferences and numerous workshops on time-series econometrics Teaching & Guidance: He teaches Econometrics C and Financial Econometrics A at the University of Copenhagen and serves on assessment committees and research networks.
Rosario N. Mantegna is a Professor at the University of Palermo and holds visiting positions at Central European University and honorary status at University College London. Since 2017, he has been a member of the External Faculty at the Complexity Science Hub Vienna, reflecting his active engagement in interdisciplinary research on complex systems. His research lies at the intersection of statistical physics and economics, where he is recognized as one of the pioneers of econophysics and economic networks . His work explores financial markets through the lens of complex networks, high-frequency trading, information transfer, and data-driven modeling. He has led and participated in numerous international and national research projects, contributing foundational insights into market structure and dynamics. The recent publications (2017–2023) highlight a strong focus on network analysis in finance , clustering techniques , information theory applications , and fintech evolution . These works span disciplines including physics, finance, computer science, and data analytics, demonstrating a consistent interdisciplinary approach. Key themes include validation of network structures, trader clustering, and systemic risk assessment. His scientific contributions are recognized through editorial roles in major conference proceedings and collaborations with leading researchers in complexity science. While no formal awards are listed, his sustained publication record in high-impact journals and books underscores his influence in the field. Rosario Mantegna advises students and researchers through his leadership in research projects and academic supervision, though specific advisees are not named. He has been involved in significant research grants and collaborative initiatives across Europe, particularly in complexity science and financial network analysis. His work is closely tied to interdisciplinary research groups and networks, including those at the Complexity Science Hub Vienna and Central European University, where he contributes to advancing the science of complex systems.
Charisios Grivas is an Assistant Professor in the Department of Mathematical Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. His research lies at the intersection of econometrics, statistics, and environmental modeling, with a strong emphasis on methodological rigor and robust inference. His primary research interests include Econometrics , Statistical Methods , Non-parametric Econometrics , Resampling , Robust Estimation , and Measurement Error Models . He develops and applies advanced statistical techniques to address challenges in linear and time-varying models, particularly in the presence of data imperfections such as measurement errors. The recent articles demonstrate a consistent focus on improving inference in econometric models—especially through automated bandwidth selection, testing for nonlinear dependence, and robust estimation under measurement error. These works span applications in both theoretical econometrics and environmental research, notably in estimating the carbon dioxide airborne fraction. Scientific Awards: No scientific awards mentioned in the provided text. Charisios Grivas actively collaborates with researchers such as Z. Psaradakis and J.E. Vera-Valdés. While no formal advisees are listed, his publications and ongoing research output suggest active supervision and mentorship. There is no mention of specific grants, but his work on measurement error and environmental statistics may be supported by external funding. His research contributes to methodological advancements with practical implications in climate science and economics.
Heino Bohn Nielsen is a Professor at the Department of Economics , University of Copenhagen , specializing in Econometric Time Series Analysis , Co-Integration , Financial Econometrics , and Bootstrap Methods . He holds a PhD in Economics from the University of Copenhagen (2004). His research focuses on econometric theory and methodology, particularly in volatility modeling and simulation-based inference. He has led major research projects such as " Econometric Modeling of Instabilities in Financial Time Series " (2011–2013) and " Theory of the Bootstrap in Econometric Models with Time Varying Volatility " (2017–2021). His recent publications highlight advancements in unit root testing , GARCH-X models , and penalized quasi-likelihood estimation , reflecting his expertise in handling boundary parameters and instability in econometric models. He serves as an Associate Editor for the Oxford Bulletin of Economics and Statistics and has refereed for top journals like the Journal of Econometrics and Journal of Applied Econometrics . His teaching has been recognized with multiple Invisible Hand Awards and a Department of Economics Teaching Award .
