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Per Christian HansenView profile

Professor

• Numerical Analysis
• Numerical Linear Algebra
• Iterative Reconstruction Methods
• +8 more

Per Christian Hansen is a Professor at the Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark (DTU), where he leads the Section for Scientific Computing. He is a VILLUM Investigator and heads the CUQI (Computational Uncertainty Quantification for Inverse Problems) research initiative, aiming to develop accessible computational platforms for uncertainty quantification in inverse problems. His expertise lies in numerical analysis, numerical linear algebra, iterative reconstruction methods, and computational inverse problems, with applications in tomography, signal analysis, and plasma physics. His research integrates theoretical analysis—such as perturbation and convergence analysis—with the development of robust, adaptive, and efficient computational methods. He has co-authored five books, over 100 scientific papers, and several widely used MATLAB software packages, including IR Tools and Regularization Tools. His recent work (2023–2025) emphasizes uncertainty quantification, Bayesian inversion, and high-dimensional tomography in fusion plasmas, reflecting a strong trend toward probabilistic and robust modeling in inverse problems. He is a SIAM Fellow (2015) for his contributions to computational methods for rank-deficient and discrete ill-posed problems and regularization techniques. His scientific leadership is evident in both theoretical advances and practical software implementations. He actively collaborates across disciplines, particularly in nuclear fusion and medical imaging, and continues to supervise PhD students and publish in top-tier journals such as Inverse Problems , SIAM Journal on Scientific Computing , and Nuclear Fusion . SIAM Fellow (2015) VILLUM Investigator He advises PhD and Master’s students in computational mathematics and inverse problems, and his research is supported by major grants, including the VILLUM Investigator award. He leads the CUQI team, which develops open-source tools for non-experts to apply uncertainty quantification in inverse problems. His lab focuses on creating modeling frameworks that bridge theory, computation, and real-world applications in materials science, imaging, and plasma diagnostics.

Carsten Hjort LangeView profile

Professor

• Roman History
• Civil War
• Political History
• +6 more

Carsten Hjort Lange is a Professor in the Department of Politics and Society at Aalborg University, Denmark. He specializes in Roman history, focusing on civil war, political/military history, and ancient historiography. His work extends to comparative studies of the American Civil War and contemporary US politics. He co-edits Brill's Historiography of Rome and Its Empire and De Gruyter's Studies in Ancient Civil War . Lange has authored three monographs, including From Hannibal to Sulla: The Birth of Civil War in Republican Rome (2024). He holds editorial roles for multiple journals and participates in international research networks like PortCityFutures. His awards include the 2017 Choice Outstanding Academic Title Award and Teacher of the Year accolades in 2021 and 2022. Education: Ph.d. in Classics/History. Research Interests: Late Roman Republic, civil war dynamics, Cassius Dio's historiography, naval history, and conceptual history. Key areas include stasis theory, Augustan propaganda, and the intersection of ancient and modern political conflict. Publications & Projects: Over 100 peer-reviewed outputs, including monographs, edited volumes, and journal articles. Active in projects such as Responses to Imperialism in Roman Historical Writing and International/European Network on Roman Republican History . Awards: Recognized for academic contributions and teaching excellence, including the prestigious Choice Award and two consecutive Teacher of the Year prizes. Media Engagement: Regular contributor to Danish media, analyzing parallels between ancient and modern political crises (e.g., Trump's presidency compared to Augustus).

• 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

Søren Lundbye-ChristensenView profile

Associate Professor

• Biostatistics
• Medical Statistics
• Cardiovascular Research
• +7 more

Søren Lundbye-Christensen is an Associate Professor and Biostatistician affiliated with the Clinical Institute at the Faculty of Health Sciences, Aalborg University, and Aalborg University Hospital in Denmark. He specializes in biostatistical support for medical research, with a strong emphasis on cardiovascular and epidemiological studies. His research interests include biostatistics, survival analysis, cohort studies, clinical epidemiology, and statistical modeling in public health. He has contributed to a wide array of healthcare research, particularly in cardiovascular diseases, cancer, maternal health, and infectious diseases. His methodological expertise spans time-to-event analysis, registry-based research, and interval-censored data modeling. The recent publications highlight a strong trend in applying advanced statistical methods to large-scale clinical and population-based datasets. His work often involves collaboration with medical researchers to derive prognostic models, validate clinical databases, and assess public health outcomes. Key themes include cardiovascular risk, fertility, cancer biomarkers, and implementation of medical training programs. Scientific Contributions and Recognition: Published over 320 research articles and datasets. Active contributor to methodological advancements in biostatistics. Regular peer reviewer, including for journals like the R Journal. Public engagement through media appearances on statistics and health. Academic Advising and Grants: Søren has supervised 31 student theses, formally serving as PhD supervisor for 14 theses and as a biostatistical advisor for 19 others, primarily in mathematics and statistics. He has participated in numerous research projects funded through institutional and national grants, including studies on seasonal disease trends, postoperative complications, and metabolic disease prediction. His work often involves interdisciplinary collaboration across medicine, public health, and data science. Labs and Research Teams: He is embedded in collaborative research networks at Aalborg University Hospital and Aalborg University, contributing statistical expertise to clinical research groups. He is involved in projects utilizing Danish national health registries and has contributed to the development and validation of clinical databases. His work supports both hypothesis-driven medical research and methodological innovation in biostatistics.

Philip BilleView profile

Professor

• Algorithms
• Data Structures
• String Processing
• +9 more

Philip Bille is a Professor and Head of the Algorithms, Logic and Graphs section at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), College of Engineering. His research centers on the design and analysis of efficient algorithms, particularly for string processing, compressed data, and data structures. His research interests lie at the intersection of theoretical computer science and practical applications. He focuses on algorithms , data structures , string indexing , pattern matching , and compressed computation . His work enables efficient querying and processing of large-scale, repetitive data, with applications in bioinformatics, intrusion detection, and green computing. The recent publications reflect a strong trend in developing space-efficient and fast algorithms for modern computational challenges. Key themes include compressed data structures , sliding window indexing , finite automata compression , and energy-aware matrix operations . These works demonstrate expertise in balancing theoretical rigor with practical performance. Philip Bille actively supervises multiple PhD students and leads several research projects. He contributes to advancing sustainable computing aligned with UN SDGs. His work integrates algorithmic theory with real-world efficiency. Supervises PhD projects on hierarchical compression, adaptive computation, and vector processor algorithms. Involved in research on green computing, compressed formats, and efficient data models. He is affiliated with the Algorithms, Logic and Graphs group at DTU, a hub for theoretical and applied algorithmic research. The team explores fundamental problems in data representation and processing, pushing the boundaries of what is computationally feasible in terms of time and space.

Romualdo Pastor-SatorrasView profile

Associate Professor

• Condensed Matter Physics
• Mathematical Physics
• Theoretical Physics
• +3 more

Romualdo Pastor-Satorras is an Associate Professor of Applied Physics at the Universitat Politècnica de Catalunya (UPC) since 2006. He earned his PhD in Condensed Matter Physics from the Universitat de Barcelona in 1995, followed by postdoctoral research at MIT (1996–1998) and The Abdus Salam International Centre for Theoretical Physics (1998–2000). His extensive international collaborations include visiting positions at Yale University, University of Notre Dame, Kavli Institute for Theoretical Physics, Helsinky University of Technology, Indiana University, and the ISI Foundation. Research Focus His interdisciplinary work spans statistical physics, network theory, and dynamical systems. Primary research areas include: Modeling epidemic spreading in complex networks Random walks and diffusion processes in temporal networks Social and biological applications of network science Glassy dynamics and energy landscapes His research combines mathematical rigor with data-driven approaches to address problems in public health, social dynamics, and complex system behavior. Publications Overview With over 100 peer-reviewed publications, Pastor-Satorras's work demonstrates consistent focus on network dynamics and epidemic modeling. Recent articles explore COVID-19 herd immunity thresholds (2020), echo chambers in political networks (2019), and non-Poissonian temporal networks (2019). His foundational 2015 review on epidemic processes in networks is highly influential. Publications frequently involve interdisciplinary collaborations across physics, data science, and computational biology. Awards and Distinctions Fellow of Universitat Politècnica de Catalunya ICREA Academia Prize (awarded twice by the Government of Catalonia) Research Infrastructure He maintains active collaborations through visiting positions at leading global institutions. His work involves theoretical modeling and computational analysis, though specific laboratory details are unspecified in the provided text.

Lisbeth Truelstrup HansenView profile

Professor

• Food Microbiology
• Microbial Food Safety
• Hygiene in Food Production
• +13 more

Lisbeth Truelstrup Hansen is a Professor and Head of the Research Group for Food Microbiology and Hygiene at the National Food Institute, Technical University of Denmark (DTU), where she leads research in microbial food safety, environmental hygiene, and pathogen survival mechanisms. She is also affiliated with the DTU Microbes Initiative and actively contributes to projects addressing food safety, Arctic sanitation, and public health. University: Technical University of Denmark School: National Food Institute Department: Research Group for Food Microbiology and Hygiene Role: Professor and Research Group Head Her research focuses on microbial food safety, with particular emphasis on Listeria monocytogenes , biofilm formation, hygiene in food production, and innovative preservation and detection methods. She also investigates water and environmental hygiene, including sewage management in Arctic regions under climate change. Her work supports UN Sustainable Development Goals related to clean water, sanitation, and good health. The recent publications highlight a strong trend in applying genomics, machine learning, and predictive modeling to food safety challenges. Her team investigates disinfectant resistance in Listeria , growth modeling of Bacillus cereus , and quality assessment in seafood and processed foods, demonstrating a multidisciplinary approach combining microbiology, environmental science, and data science. Scientific Contributions: Lead researcher in multiple national and international projects Active contributor to food safety policy and public discourse Regular media and conference speaker on hygiene and food safety topics Lisbeth Truelstrup Hansen supervises several PhD students and collaborates across disciplines on grants related to foodborne pathogens, climate impact on water quality, and sustainable food production. She has led or co-led projects such as 'ArcticSewlution' and 'ILLUQ. PERMAFROST – POLLUTION - HEALTH', which integrate environmental engineering with microbiology for public health outcomes. She is involved in key research labs and teams including the Research Group for Food Microbiology and Hygiene and the DTU Microbes Initiative, which focus on pathogen detection, biofilm dynamics, and hygiene solutions for the food industry.

Jes FrellsenView profile

Associate Professor

• Statistical Machine Learning
• Generative AI
• Deep Learning
• +5 more

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 .

Sadegh TalebiView profile

Assistant Professor

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +2 more

Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

Anders Rønn-NielsenView profile

Associate Professor

• Applied Probability Theory
• Lévy-Based Modelling
• Statistical Methods in Efficiency Analysis
• +3 more

Anders Rønn-Nielsen is an Associate Professor at the Department of Finance and Center Coordinator at the Center for Statistics at Copenhagen Business School (CBS). He holds a M.Sc. in Statistics from University of Copenhagen and a PhD in Statistics and Probability Theory from Aarhus University. His research focuses on applied probability theory, particularly Lévy-based spatial models, and statistical efficiency analysis. His academic credentials include: M.Sc. in Statistics, University of Copenhagen PhD in Statistics and Probability Theory, Aarhus University Research interests span: Lévy processes and spatial stochastic modeling Extreme value theory applications in finance and natural sciences Nonparametric production frontier analysis Efficiency measurement methodologies His publications (18+ articles) emphasize theoretical probability and statistical applications in efficiency analysis. He has served as external examiner at Aarhus University and Copenhagen University for master’s and PhD examinations (2017–2019). His teaching responsibilities include advanced probability theory courses and statistical methods training for economics students.

• Natural Language Processing
• Legal Informatics
• Multilingual NLP
• +3 more

Ilias Chalkidis is an Assistant Professor specializing in Natural Language Processing at the Department of Computer Science, University of Copenhagen. He is actively affiliated with the Natural Language Processing research section, contributing to both theoretical and applied advancements in the field. His research spans multiple high-impact domains with particular emphasis on: Legal natural language processing and multilingual legal reasoning Large language model applications in political and social contexts Fairness-explainability trade-offs in AI systems Innovative representation learning techniques for textual data Analysis of his recent publications reveals a strong focus on bridging legal informatics with cutting-edge NLP methodologies. His work on multilingual legal corpora (including the 689GB MultiLegalPile dataset) and legal decision influence prediction demonstrates practical applications for judicial systems. Simultaneously, his investigations into LLMs as voting assistants and European political spectrum analysis showcase innovative intersections between computational social science and language technology. His technical contributions to contrastive learning and hyperbolic embeddings provide foundational advances for document representation. Chalkidis actively participates in the research community through workshop organization (Natural Legal Language Processing Workshop 2023-2024) and conference presentations. His research has been published in top-tier venues including ACL, EMNLP, and ECAI, with significant citations reflecting community impact. While specific advising relationships aren't documented in the provided materials, his collaborative work patterns suggest active mentorship within the NLP research ecosystem.

Kirsten Gram-HanssenView profile

Professor

• Energy Consumption
• Sustainable Transition
• Everyday Life Practices
• +6 more

Kirsten Gram-Hanssen is a Professor at Aalborg University, specifically within the Department of Construction, Urban and Environmental Engineering at the Faculty of Engineering and Science. Her academic work is centered in the Section for City, Housing and Property, where she leads the Research Group for Sustainable Cities and Everyday Practice. She maintains an active research profile with numerous publications and projects focused on sustainable energy systems. Her educational background is not explicitly detailed in the provided text, but her PhD specialization in socio-technical analyses forms the foundation of her interdisciplinary approach connecting social sciences with engineering perspectives on sustainability. Gram-Hanssen's research interests focus on housing, everyday life, and consumption from a climate and energy perspective . She examines differences in household consumption practices through a practice-theoretical lens that emphasizes routines and technical infrastructures. Her work demonstrates that social organization of everyday life matters as much for home energy consumption as the technical efficiency of buildings and appliances. She also investigates how new technologies like private solar panels and smart home controls affect both energy consumption and daily life practices. Her research has significant implications for sustainable transitions and energy policy. Her recent publications show a clear trend toward addressing the energy crisis, sustainable consumption practices, and the intersection of technology with everyday life. The research spans domains including housing, food, and mobility, with a strong focus on practical solutions for energy transition and sustainability challenges. Her work increasingly addresses inequality, ethics, and policy implications of energy transitions. Active Principal Investigator on multiple major research projects including SGD: Shared Green Deal (2022-2027), FoMoHo: Food, Mobility and Housing in the Sustainable Transition of Everyday Life (2021-2025), and GECKO: Building greener societies (2021-2024) Frequent media commentator on energy and sustainability issues, with 197 media appearances documented Extensive research dataset creation including the eCAPE energy crisis interviews (2024) and smart home interviews (2022) Regular contributor to international conferences and workshops on sustainable consumption and energy practices Professor Gram-Hanssen has supervised numerous PhD students (9 documented) and actively participates in the academic community through peer review, conference organization, and editorial work. Her research group, the Research Group for Sustainable Cities and Everyday Practice, works at the intersection of social science and engineering to address sustainability challenges through understanding everyday practices and their relationship to energy systems.

• Differential Privacy
• Algorithms
• Theoretical Computer Science
• +3 more

Teresa Anna Steiner serves as an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, specializing in algorithmic research with emphasis on privacy-preserving computational methods and theoretical computer science. Her research centers on differential privacy mechanisms, where she investigates trade-offs between data utility and privacy guarantees through rigorous analysis of noise injection techniques like Laplace and Gaussian distributions. She extends this work to dynamic graph databases requiring real-time privacy protections and develops novel text indexing approaches for regular expression pattern matching, contributing to foundational advancements in algorithm design for sensitive data environments. Recent 2025 publications reveal a cohesive research trajectory focused on practical implementations of differential privacy across diverse data structures, with particular attention to variance optimization in noise mechanisms, edge-level privacy in evolving graphs, and efficient indexing for textual pattern recognition. These works collectively address critical challenges in balancing computational efficiency with robust privacy guarantees in modern data systems. No scientific awards were documented in the available information. Details regarding student advising or research grant funding were not specified in the provided materials.

Filipe RodriguesView profile

Associate Professor

• Machine Learning
• Intelligent Transportation Systems
• Transportation Science
• +7 more

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.

Martin Skovgaard AndersenView profile

Associate Professor

• Optimization
• Signal Processing
• Machine Learning
• +12 more

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.