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Jesper Fels BirkelundView profile

Assistant Professor

• Education Policy
• Ethnic Inequalities
• Social Mobility
• +4 more

Jesper Fels Birkelund is a Tenure Track Assistant Professor at the Department of Sociology, University of Copenhagen. His research focuses on education systems, ethnic inequalities, and social mobility, leveraging advanced statistical methods on register and survey data. He teaches courses such as Basic Statistics, Sociology in Danish Society, and Advanced Welfare, Inequality, and Mobility. His work has been published in journals like Social Forces and European Sociological Review . His research on education examines how schooling impacts cognitive and social-psychological skills, influencing long-term labor market outcomes. He has shown vocational training enhances conscientiousness, yielding earnings comparable to academic tracks. In ethnic inequality studies, he analyzes high aspirations among immigrant students despite poor academic performance, proposing counterfactual models to assess systemic challenges for minority students. In social mobility research, he explores how parental resources (human, cultural, social, economic capital) shape children’s career trajectories, particularly when parents and children share the same field of study. He uses firm linkage data to study mechanisms like parental networks and inherited family businesses. Awards: 2022 ECSR Prize for Best PhD Thesis Teaching: Basic Statistics (BA), Sociology in Danish Society (BA), Education and Social Inequality (BA/MA), Advanced Welfare, Inequality, and Mobility (MA) His work integrates micro-class approaches with intergenerational transmission theories, contributing to debates on educational policy and labor market equity. Office hours for Spring 2025: Monday 15:00–16:00 in room 16.0.57.

• Computational Statistics
• Causal Inference
• Machine Learning
• +3 more

Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects

• Statistical Inference
• Stochastic Processes
• Biomathematics
• +5 more

Susanne Ditlevsen is a Professor at the Department of Mathematical Sciences , University of Copenhagen. Her research focuses on statistical inference for stochastic processes , mathematical modeling of physiological systems , nonlinear dynamics , neuroscience , and biomathematics . Research : She develops statistical methods for diffusion processes, hidden Markov models, and stochastic differential equations, with applications in biomedical data and marine mammal behavior. Teaching : Covers basic statistics, probability, stochastic processes, regression, and generalized linear models. Publications highlight her work on climate tipping points (2023, Nature Communications ), nonlinear neuronal systems (2017), and statistical ecology (2020). Her collaborations span Denmark, France, and international institutions.

• Applied Statistics
• Statistical Consultancies
• Functional Data Analysis
• +5 more

Bo Markussen is a Professor at the University of Copenhagen within the Department of Mathematical Sciences . He is also a member of the Data Science Laboratory , where he contributes to statistical methodology and interdisciplinary collaborations. His academic journey began with a Cand.Scient (MSc) and PhD in Statistics from the University of Copenhagen, awarded in 1998 and 2002 respectively. 2012–present: Professor, Department of Mathematical Sciences, University of Copenhagen 2009–2012: Associate Professor, Department of Basic Sciences and Environment, University of Copenhagen 2006–2009: Assistant Professor, Department of Basic Sciences and Environment, University of Copenhagen Bo Markussen's research focuses on applied statistics , particularly in functional data analysis and multiple testing corrections in genetics . His work spans diverse domains including environmental science, agriculture, and public health. Recent research output highlights applications in Arctic climate data analysis, fire risk modeling, plant stress phenotyping, and nutritional biomarker prediction. His recent publications demonstrate a strong trend toward machine learning integration with statistical modeling , addressing challenges in high-dimensional data analysis and environmental risk assessment. Collaborations span institutions in Denmark and internationally, reflecting his engagement in pan-Arctic climate studies and tropical agricultural research. 2018–present: Associate Editor, Scandinavian Journal of Statistics 2017–2019: Chair, Danish Society for Theoretical Statistics 2015–2017: Board Member, Danish Society for Theoretical Statistics As a central figure in the Data Science Laboratory , Markussen leads statistical consultancy initiatives and contributes to methodological advancements. His expertise bridges theoretical statistics with real-world applications, particularly in handling complex datasets across biological and environmental domains.

Mogens FosgerauView profile

Professor

• Transport Economics
• Discrete Choice Modeling
• Travel Behavior
• +7 more

Mogens Fosgerau is a Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in transport policy and transportation science. His work spans econometrics, travel behavior modeling, and transport economics, contributing to sustainable urban mobility and policy design. Institution: Technical University of Denmark Department: Department of Technology, Management and Economics Email: mogens.fosgerau@econ.ku.dk ORCID: https://orcid.org/0000-0002-6452-5215 His research focuses on discrete choice modeling, travel time valuation, scheduling preferences, and congestion pricing. He develops theoretical and empirical models to understand how individuals make travel decisions under uncertainty and how these behaviors affect urban transport systems. His work integrates economic theory with data-driven methods, often using large-scale datasets and advanced econometric techniques. The recent trend in his publications highlights innovations in perturbed utility models for route choice, stochastic traffic assignment, and the analysis of induced demand for cycling. His research bridges transportation science, behavioral economics, and operations research, with applications in urban planning and policy evaluation. Scientific awards received include: The International Choice Modeling Conference (ICMC) award for Most Innovative Application (2022) Best Overall Paper Award, ITEA Conference (2015) Best Paper Awards from BIVEC-GIVET (2007), Kuhmo-Nectar (2008) Hedorfs Fonds Pris for Transportforskning (2011) Mogens Fosgerau has supervised PhD students such as Fentie Abegaz and has been involved in multiple externally funded research projects, including URBAN (Innovation Fund Denmark), IRUC (Danish Council for Strategic Research), and Horizon 2020 initiatives. He has also served on review panels, including for the Norwegian Research Council, and contributed to peer review and editorial duties. He is actively engaged in research networks and has presented his work at international conferences. His projects often involve interdisciplinary collaboration with researchers in economics, engineering, and urban planning.

Chenjuan GuoView profile

Associate Professor

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +4 more

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

Shuai ZhaoView profile

Assistant Professor

• Power Electronics
• Reliability Engineering
• Machine Learning
• +4 more

Shuai Zhao is an Assistant Professor at the AAU Energy Department, Faculty of Engineering and Science, Aalborg University. His research focuses on applying machine learning and artificial intelligence techniques to enhance reliability and condition monitoring in power electronic systems, with specific interests in lifetime estimation, fault diagnosis, and health management of critical components like capacitors and semiconductor devices. Institution: Aalborg University School: Faculty of Engineering and Science Department: AAU Energy Email: szh@energy.aau.dk His research spans multiple domains including: Physics-informed machine learning for power converter systems Remaining useful life prediction with hybrid Bayesian deep learning Thermal transient analysis and stress emulation methods IoT-enabled monitoring schemes for semiconductor devices Neural network applications in lithium-ion battery prognostics Recent publications show a strong trend toward integrating domain-specific physics with machine learning frameworks to address real-world challenges in: Power electronics reliability under operational stress Anomaly detection in multivariate time-series data Robust fault diagnosis for railway traction systems Temperature estimation in electric vehicle motors Imbalanced data handling in diagnostic systems Capacitance degradation modeling under environmental factors Current projects demonstrate collaboration with leading institutions on: AI-assisted long-term maintenance strategies Physics-informed neural network architectures Smart agricultural monitoring systems via IoT platforms Advanced particle filter methods for life prediction

• Functional data analysis
• Statistical inference for dependent data
• Statistical inference for stochastic processes
• +3 more

Helle Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. Her work bridges theoretical and applied statistics with interdisciplinary applications in biological and environmental sciences. Education : BSc (1993), MSc (1997), PhD (2000) in Statistics from University of Copenhagen. Employment : Professor (2018–present), Head of Data Science Lab (2018–2021), Professor MSO and head of Laboratory for Applied Statistics (2013–2018), Associate/Assistant Professor across multiple departments (2000–2013). Research Interests focus on: Functional data analysis Statistical inference for dependent data and stochastic processes Applications in biology, agriculture, and food science Her recent publications highlight statistical methodologies applied to: Enzymatic degradation of plant material Multivariate analysis in metabolic studies Random forest efficiency in metric spaces Quantile regression for longitudinal data Child food texture preferences and insect acceptance Teaching includes courses in basic probability, statistical theory, and applied statistics for bio/life sciences students. She supervises BSc, MSc, and PhD students in Statistics with co-supervision roles in interdisciplinary fields.

• Applied Probability
• Insurance Mathematics
• Finance
• +3 more

Mogens Bladt is a Professor of Applied Probability and Insurance Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He has held positions since 2018 after 24 years as Principal Researcher at the National University of Mexico (1994-2018), with visiting professorships at Technical University of Denmark and University of Copenhagen since 2001. Research: Focuses on time-inhomogeneous phase-type distributions, matrix-oriented life insurance models, heavy-tailed distributions, and diffusion bridge simulation Teaching: Offers graduate/undergraduate courses in Applied Probability, Stochastic Processes, Risk Theory, and Numerical Analysis Scientific Contributions: Developed R packages for Markov jump processes, phase-type distributions, and diffusion bridges. Holds grants from Mexico and Denmark, including Danish Research Council funding (2007–2008). Supervised 5 PhD, 8 Master’s, and 13 Bachelor’s theses Organized academic workshops and served as Associate Editor for Stochastic Models since 1997

Bekzod KhakimovView profile

Associate Professor

• Foodomics
• Metabolomics
• Multivariate Data Analysis
• +4 more

Bekzod Khakimov is an Associate Professor in the Department of Food Science at the University of Copenhagen, specializing in Food Analytics and Biotechnology. He has held this position since 2017, following his postdoctoral work (2013-2017) and PhD fellowship (2010-2013) at the same institution. His research group focuses on advanced analytical techniques for food quality assessment and nutritional value determination. His educational background includes: PhD in Plant Metabolomics, Department of Food Science, University of Copenhagen (2013) MSc in Chemical Research, Queen Mary, University of London, UK (2009) BSc in Chemistry, Faculty of Chemistry, National University of Uzbekistan (2006) Dr. Khakimov's research centers on food molecular composition (foodomics) and its health impacts through untargeted molecular screening of bio-fluids (metabolomics). His team works on optimizing standard operating procedures, developing efficient algorithms for processing raw instrumental data, and extracting information from complex omics data using multivariate data analysis (chemometrics). His analytical expertise spans high resolution liquid state NMR spectroscopy, GC-Tof-MS and LC-QTof-MS techniques, with applications ranging from plant foods to dairy products and human health studies. His publication record demonstrates consistent advancement in foodomics methodology and applications, with recent work focusing on metabolomics approaches to understand food quality, nutritional value, and health impacts across diverse food matrices including grapes, potatoes, dairy products, and botanical resources. His research increasingly incorporates advanced statistical modeling and deep learning approaches to overcome analytical challenges like the 'cage of covariance' in metabolomics data. His notable scientific achievements include: Nils Foss Talent Prize (2016) - International Award for ground-breaking science in advanced technologies for improved food quality and safety Best Young Investigator Award in Plant Metabolomics (2015) - 10th International Conference of the Metabolomics Society Dr. Khakimov currently serves as main supervisor for three postdocs, one PhD student, and two MSc students. He has successfully secured multiple competitive research grants as PI or Co-PI from sources including the Independent Research Fund Denmark, UCPH Funding, Danish Dairy Research Foundation, and the European Union. His laboratory maintains state-of-the-art analytical capabilities with responsibility for multiple GC-MS systems, LC-QTof-MS, and co-responsibility for NMR spectrometers. His research infrastructure includes four high-throughput GC-MS systems (Agilent GC-singleQ-MS, LECO Pegasus HT GC-TOF-MS and Bruker EVOQ QQQ systems), one LC-QTof-MS (Bruker Impact II), one LC-UV/Vis-FC system (Thermo Ultimate 3000), and co-responsibility for three NMR spectrometers (600, 500, and 400 MHz) from Bruker.

Yan ZhaoView profile

Associate Professor

• Anomaly Detection
• Autoencoder
• Attention (Machine Learning)
• +5 more

Yan Zhao is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, science, and systems, with a particular emphasis on anomaly detection, machine learning, and spatio-temporal data analysis. He holds a Ph.D. in Computer Science (specific education details not explicitly provided). Research Interests: Dr. Zhao's work spans anomaly detection, autoencoders, attention mechanisms, preference learning, multivariate time series, and computational efficiency. His research often integrates machine learning with real-world applications in spatial crowdsourcing, trajectory analysis, and data privacy. Publications: With 72+ publications, his recent work emphasizes spatio-temporal prediction frameworks, federated learning, and efficient time series analysis. Notable contributions include frameworks for continuous learning on streaming data and privacy-preserving clustering in spatial crowdsourcing. Grants & Supervision: He has supervised one Ph.D. student and actively contributes to research grants focusing on data engineering and smart systems. His work bridges theoretical advancements with practical applications in transportation, social networks, and IoT.

Lau Caspar ThygesenView profile

Professor

• Public Health Epidemiology
• Register-based Epidemiology
• Epidemiological Methods
• +12 more

Lau Caspar Thygesen is a Professor in Public Health Epidemiology at the National Institute of Public Health, University of Southern Denmark. He leads the Public Health Epidemiology research group and serves as Head of Studies for the BA and MA programs in Public Health Science. His work is centered on register-based epidemiology, utilizing large-scale surveys and national health registers to investigate both preventive and clinical health issues. Degree: MSc Public Health, PhD Institution: University of Southern Denmark Role: Professor and Research Group Leader Email: lct@sdu.dk ORCID: 0000-0001-8375-5211 His research interests include socio-economic health disparities, pharmacoepidemiology, clinical epidemiology, and advanced statistical methods. He has extensive experience in cohort studies and register-based research, with a focus on vulnerable populations such as those with intellectual disabilities and high-risk occupational groups. His work also addresses vaccination uptake, environmental health, and mental health during public health crises like the COVID-19 pandemic. The trends in his recent articles reflect a strong emphasis on large-scale, data-driven public health research. His publications frequently employ nationwide Danish register data to explore risk factors for disease, evaluate healthcare interventions, and assess health inequities. Common themes include cancer screening disparities, environmental exposures, mental health outcomes, and methodological rigor in epidemiological design. His collaborative network is extensive, involving multi-center and international studies. While no specific scientific awards are listed in the provided text, his research has been widely disseminated through 409 research outputs and featured in 63 media contributions, indicating significant public and academic impact. Lau Caspar Thygesen actively supervises bachelor’s, master’s, and PhD students and serves as principal or co-supervisor on multiple research projects. He is involved in key grants such as Danmarks mentale status (as PI) and Declining Response Proportions in the Danish National Health Survey (as co-supervisor), reflecting his leadership in national public health research initiatives. His teaching includes courses in epidemiology and theory of science. He is a central figure in several research teams and projects focused on health equity, including studies on people with developmental disabilities ( Sundhed og sygelighed blandt personer med udviklingshandicap ) and digital mental health interventions ( Mindhelper ). His work often involves interdisciplinary collaboration across public health, clinical medicine, and social science, leveraging register data for impactful policy-relevant findings.

Michael SørensenView profile

Professor

• Statistical Inference for Stochastic Processes
• Stochastic Differential Equations
• Financial Mathematics
• +3 more

Michael Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. His primary research focuses on statistical inference for stochastic processes, particularly stochastic differential equations and jump processes, with applications in finance, physics (e.g., wind-blown sand dynamics), and biology. He has authored/co-authored influential books such as Exponential Families of Stochastic Processes and edited volumes on empirical process techniques and statistical methods for stochastic differential equations. His work bridges theoretical statistics with applied problems in natural sciences and finance. Research interests include modeling turbulence, sand transport dynamics, and protein structure evolution. Collaborations with earth scientists like Keld Rømer Rasmussen have advanced understanding of aeolian processes. His methodologies emphasize likelihood-based inference and estimating functions, with contributions to high-frequency data analysis and diffusion bridge simulations. A comprehensive CV and full publication list are available on his profile. Key contributions span stochastic modeling in physics (e.g., sand dune dynamics), financial econometrics, and computational statistics. He has pioneered techniques for analyzing multi-modal diffusions and developed frameworks for mixed-effects stochastic differential equations. His work is widely cited in both theoretical and applied statistical literature.

Martin HansenView profile

Associate Professor

• Environmental Contamination & Chemicals
• Metabolomics
• Mass Spectrometry
• +6 more

Martin Hansen is an Associate Professor at the Department of Environmental and Resource Engineering , Technical University of Denmark (DTU). His research focuses on environmental contamination, metabolomics, and Greenland Ice Sheet biological darkening. He leads projects like PestNet and PharmaRisk , and supervises PhD students in non-target chemical analysis. Key Research Areas: Metabolomics, Mass Spectrometry, Ice Sheet Biogeochemistry, Environmental Risk Assessment Current Projects: Computational non-target chemical analysis, Drinking water quality monitoring, Pesticide risk evaluation His 2025 articles emphasize non-target chemical screening , Greenland Ice Sheet microbial ecology , and molecular networking . He collaborates with researchers across Europe and contributes to SDGs like Clean Water (6) and Climate Action (13).

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.