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Orimar Sauri ArreguiView profile

Associate Professor

• Mathematical Statistics
• Stochastic Processes
• Nonparametric Estimation
• +7 more

Orimar Sauri Arregui is an Associate Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, Denmark. His research lies at the intersection of mathematical statistics, stochastic processes, and financial modeling. Research Interests: His work focuses on ambit fields , trawl processes , Lévy and infinite divisible random fields , and nonparametric estimation in continuous time. He investigates asymptotic behavior, limit theorems, and statistical inference for complex stochastic models, with applications in financial market microstructure and energy flux modeling. The analysis of his recent publications reveals a strong trend in theoretical statistics and probability, particularly in developing and analyzing models driven by non-Gaussian noise and long-range dependence. His work often involves high-frequency data and contributes to the foundations of spatiotemporal modeling. Scientific Contributions: Developed mathematical frameworks for financial market microstructure. Advanced theory for nonparametric estimation of trawl processes. Derived asymptotic error distributions for numerical schemes in stochastic delay equations. Proved local limit theorems for energy fluxes in random fields. Advising and Research Activity: He has been involved in PhD supervision and maintains an active research output, primarily through preprints on arXiv and SSRN. His collaborations span topics in financial econometrics and statistical physics. Though specific grants are not listed, his consistent publication record suggests ongoing research funding. Laboratory and Teams: While no formal lab is mentioned, his work is part of the broader research network in mathematical statistics and financial mathematics at Aalborg University, with notable collaborations in stochastic modeling and econometrics.

Yuri GoegebeurView profile

Associate Professor

• Extreme Value Theory
• Statistical Modeling
• Actuarial Science
• +5 more

Yuri Goegebeur is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the SDU Climate Cluster. His research focuses on extreme value theory, statistical modeling, and actuarial science, particularly in areas like reinsurance pricing, tail risk analysis, and censoring data methods. He has contributed extensively to methodologies for estimating extreme quantiles, tail dependence functions, and risk measures under various conditions. His work integrates advanced statistical techniques with applications in finance, insurance, and environmental risk assessment. Key research interests include extreme value analysis, robust estimation techniques, and the development of risk measures for time series and censored datasets. He has published over 80 articles in peer-reviewed journals and serves on editorial boards for journals like Extremes and Psychometrika . His collaborative projects involve analyzing extreme weather impacts on healthcare systems and advancing multivariate regression models for extreme values. Recent publications emphasize conditional tail moment estimation, dependent risk measures, and applications in reinsurance. His research bridges theoretical statistics with practical challenges in actuarial science and environmental modeling. He has co-led projects funded by institutions like Villum Fonden, focusing on extreme value methodologies for real-world problems such as climate-related health risks.

Andres MasegosaView profile

Associate Professor

• Machine Learning
• Bayesian Networks
• Data Streaming
• +4 more

Andres Masegosa is an Associate Professor at the Department of Computer Science, Aalborg University (AAU), within The Technical Faculty of IT and Design. He is actively involved in the DarkScience project (2022–present), focusing on metagenomic binning and microbial dark matter analysis. His research interests span Bayesian networks, machine learning, probabilistic graphical models, and educational methodologies in computer science instruction. Key research contributions include advancements in PAC-Bayes theory, genome representation learning, and cold posterior effects in Bayesian models. He has published extensively in top venues like Advances in Neural Information Processing Systems and Transactions on Machine Learning Research. His work often bridges theoretical contributions with practical applications in genomics and education. Masegosa leads the development of tools like InferPy for probabilistic modeling and has contributed to open-source projects such as the AMIDST toolbox. His educational research explores learning styles and active learning strategies, emphasizing live coding and programming exercises. Collaborations include interdisciplinary projects with microbiologists and data scientists, reflecting his expertise in computational methods for complex biological systems. His research portfolio demonstrates a strong focus on scalable probabilistic methods and their real-world applications.

Anne Marie SvaneView profile

Associate Professor

• Topological Data Analysis
• Probability Theory
• Mathematical Statistics
• +3 more

Anne Marie Svane is an Associate Professor at the Department of Mathematical Sciences, Aalborg University, specializing in topological data analysis, stochastic geometry, and probability theory. Her research bridges theoretical mathematics with applied statistics, focusing on geometric functionals and computational methods. Education: PhD in Mathematics from Aarhus University. Her recent work explores cobordism obstructions, Gibbs processes, and applications in climate science and medical research. Key contributions include: Advancing kernel persistence methods for topological data analysis. Developing central limit theorems for spatial point processes. Modeling moisture dynamics in building physics with climate data. Investigating the interplay between digital algorithms and geometric structures. She collaborates on interdisciplinary projects like AI-Aalborg Intelligence and contributes to educational initiatives such as Girls' Day in Science.

Torben KnudsenView profile

Associate Professor

• Wind Turbine Engineering
• Process Control
• Control Systems
• +6 more

Torben Knudsen is an Associate Professor at the Department of Electronic Systems, Aalborg University, Denmark. He is affiliated with the Automation & Control Learning and Decisions Lab and holds positions in The Technical Faculty of IT and Design. His research focuses on control systems, wind turbine engineering, stochastic processes, and biomedical applications such as diabetes modeling. Education: Advanced degrees in engineering, though specific details are not explicitly listed. Research Interests: Wind turbine dynamics, offshore wind farm control, stochastic modeling of biomedical systems, fault detection, and optimization techniques. His work integrates control theory with renewable energy systems, particularly addressing challenges in wind turbine aeroelasticity and floating offshore wind farms. Recent projects include the development of health-aware simulation platforms (FOWLTY) and insulin dose optimization algorithms for type 2 diabetes. Key Projects: ICONIC (2023-2027) : Focuses on intelligent control of industrial processes. PRESTIGE (2020-2023) : Explored predictive manufacturing and quality control. Modelling and Control of Type 2 Diabetes (2019-2024) : Developed stochastic models for glucose-insulin dynamics. Knudsen has received the Best Paper Award (2015) for contributions to predictive manufacturing. His research also extends to pandemic modeling, including work on SARS-CoV-2 variant outbreaks and hospital preparedness during epidemics. Advising & Grants: Active in interdisciplinary collaborations, including funding from EU projects like Aeolus and OFFWIND. He contributes to grants focusing on wind farm control, diabetes modeling, and stochastic systems. Labs & Teams: Leads the Automation & Control Learning and Decisions Lab, collaborating with international teams on energy systems and biomedical engineering challenges.

Yoshiharu MaenoView profile

Professor

• Data Science and Engineering
• Complex Systems Theory
• Stochastic Process Theory
• +7 more

Dr. Yoshiharu Maeno is a Professor at the School of Interdisciplinary Mathematical Sciences, Meiji University, where he conducts interdisciplinary research at the intersection of mathematics, data science, and social systems. His work applies advanced mathematical frameworks to real-world complex phenomena in economics, finance, epidemiology, and social networks. Position: Professor Institution: Meiji University School: School of Interdisciplinary Mathematical Sciences Research Interests Dr. Maeno's research focuses on data science and engineering, complex systems theory, and stochastic process theory applied to economics and social sciences. He specializes in modeling reaction-diffusion processes to understand the dynamics of infectious disease spread, financial crises, and misinformation dissemination on social media. His work bridges theoretical mathematics with practical applications in societal challenges. Publication Trends His recent scholarly output demonstrates a consistent focus on applying reaction-diffusion and stochastic models to complex social and economic systems. The publications span interdisciplinary domains including econophysics, computational social science, and mathematical epidemiology, showing a strong trend toward data-driven modeling of real-world dynamic processes. Scientific Awards No specific awards mentioned in the provided text. Advising and Grants While specific students or grant details are not listed, Dr. Maeno's active research program suggests involvement in mentoring graduate students and securing research funding. His participation in events like the Econophysics Colloquium and CSH Workshop indicates collaboration within the global complexity science community. Labs and Teams Dr. Maeno is associated with research initiatives focused on complex systems and data science, likely involving collaborative teams working on interdisciplinary projects. His involvement in workshops and colloquia suggests membership in international research networks studying complexity in economic and social systems.

• History of Mathematics
• History of Analysis
• History of Mechanics
• +2 more

Jesper Lützen is an academic historian of mathematics at the Department of Mathematical Sciences , University of Copenhagen . He is renowned for his expertise in the history of mathematics, particularly focusing on the period 1800–1950, analysis, and mechanics. His current status as a Part-time Lecturer reflects continued engagement despite his emeritus title. Born : October 8, 1951, in Svendborg Education : Student exam (mathematical-physical line), Svendborg State School (1970) B.Sc. in Natural Sciences (mathematics major, physics minor), Aarhus University (1976) Licentiate (Ph.D.) in History of Science, Aarhus University (1980) Doctor of Science, University of Copenhagen (1990) Research Interests : Lützen specializes in the history of mathematical impossibility theorems, ranging from ancient Greek antiquity to modern times. His work spans analysis, mechanics, and the philosophical underpinnings of mathematical concepts. He has extensively explored duality, distributions, and the legacy of figures like Hjelmslev and Juel. Academic Engagement : His 15 most recent publications (2000–2024) reveal a consistent focus on mathematical history, methodology, and foundational debates. Key themes include distribution theory, geometric philosophies, and historical critiques of mathematical rigor. Scientific Awards : Fyns Stiftstidende’s Forskerpris (1982) Børge Jessens Diplom for godt foredrag (1988) Det Naturvidenskabelige Fakultets Formidlingspris (2000) Det Naturvidenskabelige Fakultets Undervisningspris (2003) Editorial and Institutional Roles : Lützen has served on editorial boards for Archive for the History of Exact Sciences and Historia Mathematica , and as a committee member in various academic and outreach initiatives. His outreach efforts include lectures for high school teachers, students, and contributions to the Great Danish Encyclopedia .

• Extreme Value Theory
• Stochastic Processes
• Time Series Analysis
• +3 more

Thomas Valentin Mikosch is a Professor at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on extreme value theory, stochastic processes, and time series analysis with applications in actuarial mathematics and risk management. Extreme value theory for dependent data Heavy-tailed distributions in financial and insurance contexts Statistical inference for stochastic models Recent work includes publications on power-law tails in time series, cluster inference for extremal events, and distance covariance in spatial models. He has served as an editor for journals like Bernoulli and Stochastic Processes and Their Applications and holds memberships in prestigious organizations such as the Royal Danish Academy for Sciences and Letters.

Markus KiderlenView profile

Associate Professor

• Integral Geometry
• Convex Geometry
• Stochastic Geometry
• +2 more

Markus Kiderlen is an Associate Professor at the Department of Mathematics, Aarhus University, specializing in the intersection of geometry, probability theory, and statistics. His research spans integral geometry, convex geometry, stochastic geometry, geometric tomography, and spatial statistics. Primary affiliation: Department of Mathematics, Aarhus University Academic rank: Associate Professor His work in stochastic geometry involves integral geometric relations, Minkowski tensors for quantifying random particles, and uniqueness/inference problems in geometric tomography (e.g., solving Hammer's X-ray problem). Another key focus is spatial sampling methods, including Wicksell's corpuscle problem and Cavalieri volume estimation improvements. Recent research trends include geometric reconstruction algorithms, stereological techniques for nonconvex particles, and rotational integral formulae for convex bodies. He has also explored polarizations in rearrangement processes and their applications. Markus Kiderlen has presented at international conferences such as MCQMC (2022), Stochastic Geometry Workshops (2019-2022), and the WIASABISS workshop (2018). His publications reflect collaborations with researchers like M. Stehr, R. Eriksen, and D. Hug.

Christophe BiscioView profile

Associate Professor

• Point Processes
• Statistics
• Mathematics
• +3 more

Christophe Biscio 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 topological data analysis, with applications in materials science and engineering. He leads interdisciplinary projects like 'Deciphering Nanoporosity of Amorphous Materials using Topological Data Analysis,' exploring medium-range order structures in glasses and metal-organic frameworks. Key research interests include statistical learning for point processes, functional central limit theorems, and adversarial machine learning. His work bridges theoretical mathematics with practical challenges in wireless communication, radar sensing, and urban system modeling. Collaborations span materials scientists, computer scientists, and data analysts. Biscio has advised one PhD student and contributed to datasets on topological summaries for spatial processes. He actively organizes conferences on data science and participates in public outreach events like Earth Day climate change discussions.

Søren HaubergView profile

Professor

• Machine Learning
• Bayesian Inference
• Geometric Deep Learning
• +11 more

Søren Hauberg is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the Cognitive Systems group. His research lies at the intersection of machine learning, geometry, and probabilistic modeling, with applications in life sciences and computer vision. Department: Applied Mathematics and Computer Science Research Group: Cognitive Systems Institution: Technical University of Denmark (DTU) His research interests center on geometric deep learning , Bayesian inference , and manifold-based modeling . He explores how stochastic and geometric structures can improve machine learning models, particularly in modeling complex data such as biological sequences and medical images. His work integrates Riemannian geometry, Gaussian processes, and energy-based models to build more robust and interpretable AI systems. The recent publications reveal a strong trend toward geometric representation learning , fairness in generative models , and computational methods for biological data . His team investigates latent space geometries, develops efficient GPU-based numerical algorithms, and applies foundational models to protein sequences. There is a clear emphasis on both theoretical rigor and practical implementation. Søren Hauberg actively supervises multiple PhD students and leads key projects in machine learning for life sciences. He is a project manager in the Center for Basic Machine Learning Research in Life Science , indicating leadership in interdisciplinary research. He also contributes to high-performance computing applications in statistical modeling. He is involved in several active research projects, including: Geometric Bayesian Deep Learning Stochastic Riemannian Geometry with Applications to Data Modelling The geometry of protein representations AI-driven Electron Tracking for High-Energy Radiation Detection

Søren Wengel MogensenView profile

Associate Professor

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

Troels PedersenView profile

Associate Professor

• Wireless Communication
• Radio Channel Modeling
• MIMO Systems
• +5 more

Troels Pedersen is an Associate Professor at the Department of Electronic Systems, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on stochastic radio channel modeling, MIMO systems, radar signal processing, and positioning technologies. Key projects include WHERE2 and NEWCOM++, addressing wireless hybrid estimators and network excellence in communications. Expertise in reverberant channel modeling, propagation graphs, and Bayesian inference methods Developed models for in-room radio channels using point processes and Galton-Watson trees Contributions to MIMO radar tracking, clutter mitigation, and drone swarm micro-Doppler analysis Active in wireless positioning, cooperative networks, and IoT interference modeling with 99+ publications in journals/conferences. Supervised 1 PhD candidate and contributed to EU-funded research initiatives.

• Functional Data Analysis
• Time Series Analysis
• Spectral Analysis
• +3 more

Vaidotas Characiejus is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark. His research focuses on functional data analysis, time series analysis, and spectral methods. He specializes in areas such as long-run variance estimation, periodogram analysis, and statistical inference for functional time series. Characiejus has contributed to developing nonparametric tests for stationarity and white noise detection in functional data contexts. His work often involves applications in nonlinear time series and asymptotic theory. He holds a Ph.D. in statistics and has published extensively in top-tier journals such as the Journal of the American Statistical Association. Characiejus's research interests span several key areas in statistics and mathematics. His primary focus is on functional data analysis, particularly in analyzing functional time series and their spectral properties. He investigates long memory processes, developing methodologies for estimating parameters such as the long-run variance and testing for stationarity. His work also addresses challenges in nonlinear time series, applying kernel-based and nonparametric techniques to improve statistical inference. Additionally, he explores applications of spectral analysis to real-world data, emphasizing the use of periodograms and Gaussian processes. His broader contributions include contributions to asymptotic theory, operator self-similar processes, and the central limit theorem in stochastic processes with varying memory structures. Characiejus has advised several research projects, though specific student names are not listed here. His research has been supported by grants focusing on statistical methodology and applications in functional data analysis. Specific grant details are not provided in the available materials. No lab or team affiliations are explicitly listed in the provided information.

Mark PodolskijView profile

Professor

• Mathematical Statistics
• Probability Theory
• Stochastic Processes
• +3 more

Mark Podolskij is an Honorary Professor at the Department of Mathematics, Aarhus University. Since 2006, he has contributed over 70 research outputs, focusing on mathematical statistics, probability theory, and stochastic processes. His work emphasizes asymptotic theory, statistical inference, and applications in financial markets. He holds editorial roles in journals like Bernoulli and Scandinavian Journal of Statistics . Research Interests: His primary areas include stochastic processes, limit theorems, and high-dimensional statistical methods. Notable contributions involve quadratic variation analysis, heavy-tailed distributions, and Lévy processes. Scientific Awards: He secured prestigious grants, including the ERC Consolidator Grant (2018) and Villum Fonden funding (2015). These grants supported projects on statistical methods for diffusions and probabilistic inference in ambit fields. Grants/Editorial Roles: Podolskij has led or co-investigated research projects totaling over €10M. His editorial work spans multiple journals, reflecting his influence in academic publishing.