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
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.
Jun Yang is a Tenure Track Assistant Professor at the Department of Mathematical Sciences, University of Copenhagen. His research spans computational statistics and machine learning, with a focus on high-dimensional inference, time series analysis, and Monte Carlo methods. Current Position: Tenure Track Assistant Professor, University of Copenhagen (2023–present) Previous Role: Florence Nightingale Bicentennial Fellow, University of Oxford (2020–2023) Education: Ph.D. in Statistics, University of Toronto (2020), advised by Daniel M. Roy and Jeffrey S. Rosenthal Research Interests: Jun’s work addresses the intersection of computational statistics and machine learning, including: - High-dimensional Markov chain Monte Carlo (MCMC) algorithms - Bayesian variable selection in complex models - Spectral inference for nonlinear time series - Quantitative bounds and complexity analysis for MCMC Publications: His publications highlight advancements in high-dimensional sampling, time series analysis, and algorithm design. Key contributions include: - Dimension-free mixing results for Bayesian variable selection - Stereographic projection techniques for MCMC - State-domain change point detection in nonlinear regression Awards: Florence Nightingale Bicentennial Fellow, University of Oxford (2020–2023) Collaborations: Jun collaborates with researchers like K. Łatuszyński, G.O. Roberts, and J.S. Rosenthal, advancing statistical theory and applications in econometrics, machine learning, and stochastic processes.
Jes Frellsen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU) since 2016. Previously, he held academic positions at the IT University of Copenhagen (2016-2019), postdoctoral roles at University of Cambridge (2013-2016) and University of Copenhagen (2011-2013). Education: PhD in Bioinformatics (2011), University of Copenhagen MSc in Bioinformatics (2007), University of Copenhagen BSc in Mathematics and Computer Science (2005), University of Copenhagen EAP Exchange at University of California, Santa Cruz (2004-2005) Research Focus Jes Frellsen specializes in statistical machine learning , particularly generative AI and deep generative models with applications in bioinformatics . His work integrates Bayesian inference , directional statistics , and Markov chain Monte Carlo methods to address challenges in macromolecular structure prediction and missing data imputation . Recent efforts explore uncertainty quantification in image segmentation and generative modeling for materials science. Advising & Collaborations He actively supervises PhD students and postdoctoral researchers in projects spanning news recommendation systems , medical imaging , and 3D structure generation . Collaborations include work with Zoubin Ghahramani (Cambridge) and Thomas Hamelryck (Copenhagen), with contributions to protein structure prediction and statistical methods in structural bioinformatics .
Filipe Rodrigues is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in intelligent transportation systems and transportation science. His work integrates machine learning, artificial intelligence, and behavioral modeling to improve urban mobility and public transport systems. His research interests lie at the intersection of machine learning , transportation science , and behavioral modeling . He specializes in discrete choice modeling , reinforcement learning , graph neural networks , and smart card data analytics . His work contributes to sustainable urban mobility, leveraging big data and AI for proactive traffic control and public transport optimization. The recent publications highlight a strong trend toward integrating AI and behavioral science in transportation. Key themes include ride-sourcing driver behavior , public transport trip validation , autonomous fleet control , and causal machine learning . These works predominantly employ deep learning , Bayesian modeling , and offline reinforcement learning techniques, often applied to real-world datasets from Denmark and beyond. Scientific Contributions: Active contributor to journals like Transportation Research Part C and Journal of Choice Modelling . Supervises multiple PhD projects on AI in transportation and causal modeling. Regular presenter at major transportation and AI conferences. Advising and Grants: Filipe Rodrigues is the main or co-supervisor of several PhD students including O. B. Lassen, F. M. F. Santos, A. Nguyen, and X. Wu. He leads and participates in funded research projects such as 'Proactive traffic control through AI and Big Data' and 'Causal Graph Neural Networks for machine learning meta-modelling', indicating sustained grant support. His collaborative network spans institutions in Europe and beyond. Labs and Teams: He is part of the Intelligent Transportation Systems research group at DTU, collaborating closely with researchers like F. C. Pereira and C. M. L. Azevedo. The team focuses on data-driven mobility solutions, combining simulation, machine learning, and behavioral insights.
Svante Eriksen is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His research spans statistics, forensic genetics, and computational modeling, with a focus on Bayesian networks, graphical models, and statistical methods for forensic DNA analysis. He is actively involved in interdisciplinary research and software development for probabilistic genotyping and large-scale inference. Research Interests: Bayesian Networks and Graphical Models Statistical Methods in Forensic Genetics SNP and Y-STR Genotyping Data Mining and Knowledge Discovery Model Selection and Context-Specific Independence Software Development for Statistical Inference Recent Publication Trends (2024–2025): His recent work focuses on improving SNP genotyping accuracy using logistic regression models, developing efficient software (jti and sparta) for Bayesian network inference, and advancing forensic DNA analysis through dynamic SNP selection and probabilistic modeling of Y-STR databases. These contributions reflect a strong integration of statistical theory, computational efficiency, and real-world forensic applications. Scientific Contributions: Principal contributor to software packages for Bayesian network prediction. Developer of statistical models for forensic DNA data interpretation. Collaborator on projects involving digital learning analytics and student retention. Advising and Grants: While specific student names are not listed, the profile indicates involvement in PhD supervision (4 cases). He has participated in multiple externally funded research projects, including those supported by Novo Nordisk and Danish research councils, focusing on forensic DNA analysis, graphical models, and educational data mining. Research Groups and Collaborations: He is part of a strong research network in forensic genetics and statistical modeling at Aalborg University, collaborating with leading researchers such as N. Morling, M. M. Andersen, and T. Tvedebrink. His work is closely tied to the development and application of statistical software in both forensic and educational domains.
Jaron Skovsted Gundersen is a Research Assistant at the Department of Electronic Systems, within The Technical Faculty of IT and Design at Aalborg University, Denmark. He is actively involved in the Automation & Control group and the Learning and Decisions Lab, focusing on privacy-preserving distributed systems, quantum coding, and decentralized control for infrastructure resilience. His research centers on advanced topics in secure computation and machine learning, including privacy-preserving distributed consensus , secure multi-party computation using Shamir secret sharing , federated learning , and quantum stabilizer codes . His work integrates theoretical foundations with practical applications in critical systems such as water and power distribution networks. The trend in his publications shows a strong emphasis on data privacy in distributed machine learning , leveraging techniques like subspace perturbation and differential quantization. His recent articles span high-impact journals such as IEEE Transactions on Information Forensics and Security and IEEE Journal on Selected Areas in Information Theory, reflecting contributions to both theoretical and applied aspects of information security and control systems. He has been a project participant in the SWIFT research initiative (2019–2024), which investigates decentralized control solutions for electric and water distribution systems. His activities include multiple conference presentations, participation in academic workshops, and public engagement through events like the PDJF Grundfos Prize 'The Stars of Tomorrow' EXPO. He also delivered a lecture on technological solutions in water technology at a national climate meeting in 2022. PhD graduate (March 2021) Active researcher in privacy-preserving machine learning and quantum coding Contributor to resilient infrastructure control systems Regular participant in international conferences and workshops Gundersen is affiliated with the Learning and Decisions Lab at Aalborg University, where he collaborates on cutting-edge research in distributed intelligence, secure computation, and adaptive control systems. The lab fosters interdisciplinary work combining control theory, information theory, and machine learning for real-world applications.
Stephanie Thomas is an Adjunct Assistant Professor in the Department of Economics at Curtin University, with expertise in health economics, experimental economics, and environmental/resource economics. Her research focuses on healthcare policy, public finance, and behavioral economic modeling. Education: Ph.D., McMaster University (2016) M.A., University of Western Ontario (2010) B.A., McMaster University (2009) Her scholarly work bridges econometric methodologies with healthcare decision-making, including studies on EQ-5D-5L value sets and health policy analysis. Recent publications examine price prediction models under stress, healthcare financing systems, and behavioral taxation dynamics. Co-authors include prominent researchers such as Hurley, Buckley, and Cuff, reflecting collaborative interdisciplinary efforts. While no formal awards are documented in the provided data, her research has been widely shared across academic platforms.
Karsten Wedel Jacobsen is a Professor in the Department of Physics at the Technical University of Denmark (DTU), specializing in theoretical solid-state physics and computational materials design. His work focuses on quantum mechanical calculations for material design at the atomic scale, with applications in nanotechnology and energy-related materials. He has led major research centers like the Center for Atomic-scale Materials Design (CAMD) and contributed to open-source software like GPAW. He holds academic leadership roles, including directing CAMD and serving on DTU's educational committees. Education: PhD in Theoretical Physics (University of Copenhagen, 1987), M.Sc. in Physics (University of Copenhagen, 1984). Research Interests : Theoretical nanoscale physics, electronic structure methods, molecular electronics, and computational materials discovery. His research bridges quantum mechanics and practical applications, such as solar energy materials and catalyst design. Publications & Trends : Over 218 publications, with recent work emphasizing machine learning in materials design, high-throughput screening for 2D materials, and computational studies of catalytic interfaces. Key themes include atomic-scale simulations, defect engineering, and energy-related material discovery. Scientific Honors : Elected Member of The Danish Academy of Natural Sciences (DNA) (1994) Elected Member of Danish Academy of Technical Sciences (ATV) (2001) Reinholdt W. Jorch's Award (2004) Advising & Grants : Supervised 25 PhD students, including current advisees working on electrosynthesis, electrocatalysis, and machine learning in quantum materials. Active in research funding through grants like the Danish Research Councils and the Lundbeck Foundation. Leads interdisciplinary projects on computational modeling and open-source software development. Labs & Teams : Director of CAMD (2010–2012), a hub for atomic-scale materials design research. Collaborates globally through networks like CAMP and MIKA Advisory Board, advancing theoretical and computational methods in materials science.
Abderezak Lashab is an Assistant Professor at Aalborg University's Department of Electric Power Systems and Microgrids within the Faculty of Engineering and Science. He specializes in microgrid technologies, photovoltaic systems, and power electronics. His research focuses on enhancing the stability, efficiency, and resilience of energy systems, particularly in renewable energy integration, smart grids, and electric vehicle infrastructure. Key projects include the HECATE initiative exploring hybrid electric regional aircraft distribution technologies. Affiliations: AAU Energy, Microgrids Research Group Research Interests: Microgrid control strategies, photovoltaic system optimization, battery storage solutions, and cybersecurity in energy networks. His work emphasizes practical applications such as disaster-resilient mobile microgrids and EV charging infrastructure sustainability. Publications (82+): Focus on advanced control algorithms, power electronics, and renewable energy systems. Recent trends highlight grid stability under high PV penetration and smart grid resilience. Grants: HECATE project (2023-2025) funded by Horizon JU Innovation Action Labs/Teams: Active contributor to AAU's energy research groups, collaborating on projects involving hybrid electric systems and IoT-enabled cybersecurity.
Astrid Johannesson Hjelholt is a Physician Scientist at Aalborg University, affiliated with The Faculty of Medicine, Department of Clinical Pharmacology, and practicing as a physician at Aalborg University Hospital. Her research spans multiple disciplines at the intersection of clinical medicine, pharmacology, and statistical genetics. Dr. Hjelholt's research interests include: Genetic architecture and fine mapping of complex diseases Clinical trials methodology, particularly placebo-controlled studies Migraine treatment and neuropharmacology Type 1 diabetes interventions in pediatric populations Artificial intelligence applications in drug information systems Her recent publications reveal a strong methodological focus on Bayesian statistical approaches applied to genetic research, alongside clinical investigations in migraine treatment and diabetes management. She has developed advanced techniques for gene set prioritization and genetic fine mapping while maintaining active clinical research through multiple trials including the INVITED study for type 1 diabetes and a cabergoline migraine treatment pilot. Dr. Hjelholt's work has received notable attention across academic and public spheres, with publications picked up by news outlets and widely shared on social media platforms including X (formerly Twitter) where one study was posted by 14 users. Her research appears in high-impact journals including PLOS Genetics, PLOS ONE, BMJ Open, and the Danish Medical Journal. Her scientific contributions encompass: Development of Bayesian linear regression models for genetic analysis Design and execution of placebo-controlled clinical trials Evaluation of AI tools for clinical pharmacology applications Investigation of novel treatments for neurological and endocrine conditions
Wouter Boomsma is a Professor in the Machine Learning section of the Department of Computer Science at the University of Copenhagen. He leads the BioML research group, which focuses on developing machine learning techniques to address fundamental challenges in biology, particularly in the realm of protein science. His research interests center around understanding the relationship between protein sequence, structure, and function through advanced machine learning approaches. Key research areas include representation learning of protein sequences and structures, 3D structure prediction, and systematic protein optimization using techniques like Bayesian optimization. His work has significant applications in protein engineering, basic biological research on mutation effects in protein interaction networks, and health-related studies on human disease mutations. Boomsma's research program recently received substantial funding from the Novo Nordisk Foundation to establish the Center for Basic Machine Learning Research in Life Science (MLLS), a collaborative effort involving six machine learning research groups in the Copenhagen area. This initiative underscores the significance and potential impact of his work at the intersection of machine learning and life sciences. His publication record demonstrates a consistent focus on applying machine learning to protein science challenges, with recent work emphasizing protein structure prediction, variant effect analysis, and Bayesian optimization for protein engineering. The research output shows strong interdisciplinary connections between computer science, biology, and medicine. Boomsma maintains active collaborations with industry partners and research institutions, with his work gaining recognition through citations and media coverage. His group's research has been featured in high-impact journals including Nature Methods, Nature Communications, and PLOS Computational Biology. The BioML group operates within the Department of Computer Science's Machine Learning section at the University of Copenhagen, with physical location at Universitetsparken 1, Copenhagen Ø. The group maintains an active presence through their website (https://ku-bioml.github.io) and continues to expand its research portfolio in machine learning for biological applications.
Richard Michael is a PhD Fellow and Guest Researcher at the Machine Learning section of the Department of Computer Science , University of Copenhagen. His work focuses on bridging machine learning with computational biology, particularly in protein engineering and optimization of discrete sequences. Research Interests High-dimensional Bayesian optimization for discrete sequences Regression models in biomolecular design Continuous relaxation techniques for discrete problems Fitness landscape analysis in protein engineering Gaussian processes for predictive modeling Publication Trends Recent work highlights interdisciplinary approaches combining machine learning with computational biology. Key areas include optimization of discrete sequences (e.g., proteins or DNA), systematic benchmarking of regression models for biomolecular tasks, and development of novel computational frameworks for high-dimensional problems. His publications span both conference proceedings (e.g., NeurIPS) and peer-reviewed journals (e.g., PLOS Computational Biology). Contact Email: richard.michael@di.ku.dk
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.
Andreas Kryger Jensen is an Associate Professor in the Department of Public Health at the University of Copenhagen, Faculty of Health and Medical Sciences. His academic work is centered in the Section of Biostatistics, where he contributes to methodological and applied research in biostatistics and public health. He is actively engaged in research output, peer review, and academic collaboration. His primary research interests include: Functional Data Analysis Bioinformatics Design and analysis of clinical trials Manifold statistics Machine learning applications in health Environmental and pediatric epidemiology His recent publications demonstrate a strong focus on developing and applying advanced statistical methods to complex health datasets. Articles span topics such as deep learning for functional data alignment, air pollution mixtures and childhood asthma, hormonal profiles in pediatric diabetes, and predictive modeling in maternal and child health. These works appear in high-quality journals like Statistics and Computing , PLoS ONE , Environmetrics , and JACC: Advances , indicating interdisciplinary reach and methodological rigor. His research trends reflect a commitment to solving public health challenges through innovative statistical modeling, particularly in longitudinal and functional data contexts. He integrates machine learning with traditional biostatistical methods to improve prediction, causal inference, and data interpretation in clinical and population health settings. While no specific scientific awards are mentioned in the provided text, his sustained publication record, editorial roles, and active research profile suggest recognition within the academic community. Andreas Kryger Jensen is involved in academic service, including peer review for journals such as Biostatistics , Scandinavian Journal of Statistics , and Biometrical Journal , as well as participation in conferences and outreach lectures. He has also served on external committees, including at Statens Serum Institut, indicating broader institutional engagement. Though no specific students are named, his role as an associate professor and active researcher implies involvement in mentoring graduate students and early-career researchers in biostatistics and public health. His work is supported by collaborative networks across Denmark and internationally, particularly in environmental health and clinical research.
