جست‌وجوی استاد و پژوهشگر

Allan Larsenمشاهده پروفایل

استاد

• Operations Research
• Supply Chain Management
• Logistics
• +۴ مورد دیگر

Allan Larsen is a Professor and Deputy Head of Division at the Department of Technology, Management and Economics (DTU Management) at the Technical University of Denmark. He heads the Operations and Supply Chain Management Section. His academic background includes an MSc in Applied Mathematics and a PhD in Operations Research, both from DTU. He teaches courses in Operations Management and Simulation at both undergraduate and graduate levels. Research Focus: His work applies operations research methodologies to complex planning problems in supply chain management, logistics, healthcare operations, and public transport. Key areas include urban freight transport, healthcare supply chains, and optimization of public transport resources like crew and fleets. Collaborations with industries, especially in freight transport, have been extensive. He previously co-directed the Transport DTU research center and co-chaired Denmark’s Transport Innovation Network. Education: MSc in Applied Mathematics (1989–1995), PhD in Operations Research (1997–1999), both from DTU. Recognition: Recipient of the Hedorf’s transportpris award in 2022 for contributions to transport research. Research Projects: Supervises multiple PhD students (e.g., on electric freight transport, healthcare resource optimization, and Industry 4.0 applications). Active in projects like 'Pioneering Electric Heavy-Duty Freight Transport' and 'Resource optimization in healthcare.' Labs/Teams: Leads the Operations and Supply Chain Management research group and collaborates in initiatives like the Transport Innovation Network, focusing on sustainable transport solutions.

• Applied Probability Theory
• Financial Mathematics
• Insurance Mathematics
• +۳ مورد دیگر

Jesper Lund Pedersen is an Associate Professor at the Department of Mathematical Sciences , University of Copenhagen , specializing in applied probability theory with applications in financial mathematics and insurance mathematics . His research spans stochastic processes, optimal stopping time problems, and stochastic control. Education : PhD in Mathematics (2000, Aarhus University) His work addresses: (Nonlinear) optimal stopping time problems Stochastic control and filtering Multidimensional point processes Levy processes in finance Key publications reveal expertise in Bayesian changepoint detection , random drift identification , and mean-variance portfolio optimization , with interdisciplinary applications in neuroscience (V-ATPase dynamics) and epidemiology. Scientific awards : Villum Experiment Grant (2018-2020) Steno Research Fellowship (2002-2005) His research collaborations span Denmark, the UK, Germany, and the USA, focusing on probability theory, financial mathematics, and biomedical applications.

Sadegh Talebiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +۲ مورد دیگر

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.

Christian Kühnمشاهده پروفایل

استاد

• Dynamical Systems
• Differential Equations
• Stochastic Dynamics
• +۵ مورد دیگر

Christian Kühn is a Professor of Multiscale and Stochastic Dynamics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He has been an External Faculty member at the Complexity Science Hub Vienna since 2017, reflecting his interdisciplinary engagement in complex systems research. His academic background includes a BSc in Mathematics from Jacobs University Bremen (2005), an M.A.St. from the University of Cambridge (2006), and a PhD in Applied Mathematics from Cornell University (2010). He held postdoctoral positions at the Max Planck Institute for the Physics of Complex Systems in Dresden and the Vienna University of Technology, where he also served as an APART-Fellow and Leibniz Fellow. Christian Kühn's research lies at the intersection of differential equations, dynamical systems, and mathematical modeling. He focuses on multiscale problems, the impact of noise and uncertainty in deterministic and stochastic systems, and adaptive networks. Central phenomena of interest include bifurcations, pattern formation, and scaling laws. His work bridges theoretical developments with applications in epidemiology, neuroscience, and complex network dynamics. His recent publications (2021–2024) reflect a strong trend in analyzing nonlinear and stochastic dynamics on networks, with applications ranging from epidemic modeling to synchronization and critical transitions. Key themes include explosive phenomena, adaptive network behavior, moment closure methods, and non-Markovian systems, demonstrating a consistent focus on foundational aspects of dynamical systems with practical relevance. Notable scientific awards include: Richard-von-Mises Prize, GAMM (2017) Lichtenberg Professorship, VolkswagenStiftung (2016) Best Paper Award, TU Vienna (2014) Leibniz Fellow, Oberwolfach (2013) APART-Fellow, Austrian Academy of Sciences (2012) While specific details about advised students are not provided, his role as a full professor and active researcher suggests involvement in mentoring graduate students and postdoctoral researchers. His work has been supported by prestigious grants such as the Lichtenberg Professorship. He leads research in multiscale and stochastic dynamics, contributing to both theoretical advances and interdisciplinary applications. Kühn is part of vibrant research environments at TUM and the Complexity Science Hub Vienna, collaborating with leading scientists in network science, applied mathematics, and complex systems. His work continues to advance the understanding of critical transitions and nonlinear behavior in high-dimensional and stochastic systems.

Rasmus Waagepetersenمشاهده پروفایل

استاد

• Spatial Statistics
• Quantitative Genetics
• Markov Chain Monte Carlo Methods
• +۳ مورد دیگر

Rasmus Waagepetersen is a Professor in the Department of Mathematical Sciences at Aalborg University, affiliated with The Faculty of Engineering and Science. His research focuses on spatial statistics, quantitative genetics, and statistical methodology for spatial point processes. He leads and participates in interdisciplinary projects such as urbanLab (spatial data analysis for urban planning) and studies on microbiome interactions in agricultural systems. Key research areas include spatial point processes, Markov chain Monte Carlo methods, and statistical inference for complex ecological and biomedical data. His work frequently involves collaborations with environmental and biological scientists, as seen in projects analyzing root microbiota assemblies in legumes and climate data for building simulations. Waagepetersen has contributed to methodological advancements in spatial statistics, including goodness-of-fit tests, likelihood-based inference for log Gaussian Cox processes, and quasi-likelihood approaches for case-control point pattern data. His research has been supported by grants from institutions like the Villum Foundation. Key Projects: urbanLab, Klimadata til fugtsimuleringer, Nod factor signaling in plant microbiota. Grants: Multiple projects funded by the Villum Foundation and Danish research councils. His recent publications address topics such as space-time point processes, microbiome data analysis, and statistical modeling in education. Waagepetersen maintains an active research group and collaborates internationally on both theoretical and applied statistical problems.

Jalal Kazempourمشاهده پروفایل

استاد

• Energy Markets
• Power Systems
• Renewable Energy Integration
• +۱۳ مورد دیگر

Jalal Kazempour is a Full Professor at the Technical University of Denmark (DTU) in the Department of Wind and Energy Systems (DTU Wind), where he leads the Energy Markets and Analytics (EMA) section and serves as Head of Studies for the MSc program in Sustainable Energy Systems. He is an Associate Editor for Operations Research and a Senior Member of both IEEE and INFORMS, and he contributes to EU energy policy through ACER’s Expert Group on Flexibility Needs Assessment. His research lies at the intersection of optimization, game theory, control, and machine learning, focusing on data-driven approaches for modern power systems with high renewable penetration. He investigates market design, grid services, and coordination mechanisms for integrated energy systems involving electricity, hydrogen, natural gas, and district heating, aiming to improve system efficiency and decision-making. Recent publications highlight trends in privacy-preserving optimization, bidding strategies for wind and hydrogen, flexibility aggregation, and market clearing in coupled energy systems, reflecting a strong emphasis on stochastic and robust optimization, machine learning, and real-world applicability in energy markets. Scientific Awards: Best Paper Award of IEEE SmartGridComm 2023 Best Paper Award of IEEE Transactions on Power Systems (2019–2021) Best Teacher Award, DTU Electrical Engineering Department (2019) IEEE Senior Member (2018) INFORMS Senior Member (2025) Outstanding Editor, International Transactions on Electrical Energy Systems (2017) Advising and Grants: He supervises multiple PhD students across projects on power-to-X, virtual power plants, and AI for market design. He has secured major funding, including a 9.5 million DKK EUDP grant for privacy-preserving data sharing and an Industrial PhD project with Energinet funded by Innovation Fund Denmark. Labs and Teams: He founded and leads the Energy Markets and Analytics (EMA) section at DTU, formerly known as the Energy Analytics and Markets (ELMA) group, which hosts over 10 researchers and organizes the annual DTU PES Summer School.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.

Martin Skovgaard Andersenمشاهده پروفایل

دانشیار

• Optimization
• Signal Processing
• Machine Learning
• +۱۲ مورد دیگر

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.

Marco Antonio Muniz Rodriguezمشاهده پروفایل

دانشیار

• Timed Automata
• Formal Verification
• Model Checking
• +۶ مورد دیگر

Marco Antonio Muniz Rodriguez is an Associate Professor at the Department of Computer Science, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on formal methods, specifically timed automata and model checking, with applications in machine learning, stochastic systems, and safety-critical decision-making. He contributes to the UPPAAL verification tool and explores interdisciplinary areas like healthcare optimization and traffic systems. He participated in the BEO-COVID project (2020), developing decision support tools for pandemic management. His work integrates formal verification techniques with machine learning to address real-world problems such as traffic rerouting and epidemic prevention. Key collaborations include projects with the Poul Due Jensen Foundation and industrial partners. His research spans theoretical advancements in timed systems and practical implementations, emphasizing computational efficiency through GPU acceleration and static analysis. He has published on topics ranging from stubborn set reduction to proactive traffic management, demonstrating a commitment to both foundational research and applied solutions.

Martin Kristjansenمشاهده پروفایل

پژوهشگر

• Computer Science
• Real-Time Systems
• Model Checking
• +۵ مورد دیگر

Martin Kristjansen is a Researcher in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. He is part of the Distributed, Embedded and Intelligent Systems research group, focusing on real-time systems, model checking, and distributed computing. His research interests span multiple areas of computer science with a focus on: Real-Time Systems and Schedulability Analysis Model Checking and Formal Verification using UPPAAL Distributed Systems and Multi-core Architectures Reinforcement Learning applications in battery management Fleet management in noisy environments Automata theory and symbolic model learning Dr. Kristjansen's publication record shows a consistent research trajectory from avionics systems in 2019 to more recent work on stochastic digraph real-time task models in 2024. His work demonstrates a strong focus on applying formal methods to practical engineering problems, particularly in safety-critical systems, with research areas spanning automaton theory (100%), mobile robotics (85%), multicore systems (85%), and performance analysis (85%). He has been actively involved in significant research projects including: STORM_SAFE: Software reliability for critical infrastructures (2024-2027) as a Project Participant Digital technologies for Industry 4.0 (2019-2021) as a Project Participant Dr. Kristjansen collaborates extensively with Professor Kim G. Larsen and other researchers at Aalborg University, contributing to the institution's strong reputation in formal methods and real-time systems research. His work includes two significant datasets related to symbolic timed models and distributed fleet management, demonstrating commitment to research reproducibility.

Peter Gjøl Jensenمشاهده پروفایل

دانشیار

• Model Checking
• Formal Verification
• Reinforcement Learning
• +۸ مورد دیگر

Peter Gjøl Jensen is an Associate Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. His research spans formal methods, artificial intelligence, and practical applications in energy systems. He is affiliated with multiple research groups including Distributed, Embedded and Intelligent Systems, AI for the People, and Artificial Intelligence and Machine Learning. His research interests focus on Model Checking , Formal Verification , Reinforcement Learning , and applications to Cyber-physical Systems . He has developed expertise in applying theoretical computer science to solve real-world problems, particularly in energy optimization and heat pump control systems. His work bridges the gap between formal methods and practical engineering applications. His recent publications show a clear trend toward applying AI and formal verification techniques to energy systems and cyber-physical applications. The research spans theoretical foundations of model checking and reaches into practical implementations for heat pump control, autonomous systems, and environmental management. His work often involves the UPPAAL and Stratego frameworks for verification and synthesis. Dr. Jensen actively supervises PhD students, including Andreas Holck Hoeg-Petersen on the "Explainable and Causally Enforced Reinforcement Learning" project. His research has attracted media attention, particularly for intelligent heat pump control systems that provide significant energy savings. He is involved in multiple research projects including "Explainable and Causally Enforced Reinforcement Learning" (ongoing) and "BEO-COVID: Decision Support for Evaluation and Optimization in UPPAAL" (completed in 2020).

• Quantitative Epidemiology
• Animal Disease Modeling
• Statistical Modeling
• +۶ مورد دیگر

Matt Denwood is a Professor in the Department of Veterinary and Animal Sciences within the Animal Welfare and Disease Control section at the University of Copenhagen's Faculty of Health and Medical Sciences. His position combines veterinary expertise with advanced statistical knowledge to address complex problems in animal health and disease systems. Professor Denwood's primary research interests focus on the application of quantitative epidemiology to animal disease and production challenges. His work encompasses statistical and mathematical modeling of animal disease systems, appropriate consideration of diagnostic tests for disease evaluation, and the use of stochastic modeling to understand underlying disease processes. He leverages his veterinary background, statistical knowledge, government advisory experience, and programming skills to bridge theoretical and applied aspects of epidemiology and quantitative biology. His research frequently involves computationally intensive statistical methods applied to problems in ecology and epidemiology through collaborations with researchers from veterinary, medical, and life science departments across Denmark, the UK, and worldwide. Analysis of his recent publications reveals a strong emphasis on veterinary epidemiology, disease modeling, and diagnostic test evaluation across diverse contexts including wildlife disease spread, antimicrobial resistance, avian influenza, food safety, and healthcare systems. His methodological approach often employs advanced statistical techniques such as latent class modeling, with a systems perspective evident in his 'farm-to-fork' analyses of food safety risks and comprehensive disease modeling frameworks. As a researcher in the veterinary domain, Professor Denwood receives funding from various government, charity, and livestock industry bodies. His professional activities include significant participation in the International Symposium on Veterinary Epidemiology and Economics (ISVEE), where he has served as workshop leader and organizer for multiple events, including ISVEE 16 and the upcoming ISVEE 17 in November 2024. He has also contributed to important public health initiatives, including mathematical modeling of COVID-19 transmission and hospital burden during Denmark's partial reopening scenarios in 2020.

• Statistical Physics
• Non-equilibrium Thermodynamics
• Theoretical Physics
• +۳ مورد دیگر

Prashant Singh is a Postdoctoral Researcher (Researcher) in the Biocomplexity group at the Niels Bohr Institute, University of Copenhagen. His work focuses on theoretical and statistical physics with applications in biological systems, leveraging analytical methods to solve complex non-equilibrium problems. His research spans statistical physics, non-equilibrium thermodynamics, and theoretical biophysics, investigating phenomena like active matter phase transitions, positional information in boundary-driven systems, and stochastic thermodynamics. Key interests include diffusion with resetting, entropy production inference, and reaction-diffusion modeling, often revealing fundamental trade-offs in biological information processing and thermodynamic efficiency. Analysis of his 2024-2025 publications shows a cohesive focus on exact solutions for non-equilibrium systems, particularly single-file diffusion under resetting and active matter with long-range interactions. Recurring themes include cost-time optimization, boundary effects on information transmission, and the thermodynamic constraints of biological computation. No information is available regarding student advising or research grants. He is embedded within the Biocomplexity research group at the Niels Bohr Institute, which employs theoretical frameworks to study collective dynamics in biological systems, including pattern formation, cellular information processing, and emergent behavior in active matter.

Bernd Dammannمشاهده پروفایل

دانشیار

• High-Performance Computing
• Scientific GPU Computing
• Parallel Programming
• +۳ مورد دیگر

Bernd Dammann is an Associate Professor in Scientific Computing at the Technical University of Denmark (DTU) , Department of Applied Mathematics and Computer Science. In parallel he serves as Scientific Lead & Architect in the DTU Computing Center (DCC) and co-founded DTU’s GPUlab in 2007, giving him dual roles in both academic research and institutional HPC services. Education Dipl.Phys. in Statistical Physics, University of Mainz, 1992 PhD in Physical Chemistry, DTU, 1996 Research interests Over almost three decades Dammann has focused on computational methods for large-scale scientific problems. His core expertise lies in: High-Performance Computing and parallel programming paradigms (OpenMP, MPI, GPU) Scientific GPU computing and many-core architectures Application tuning and performance optimization for heterogeneous systems Uncertainty quantification, Monte-Carlo methods, and real-time control applications in biomedicine (diabetes technology) Ultrasound beamforming and advanced imaging techniques Publication trends His 34 publications (1996-2025) trace a clear evolution from theoretical physics and surface-science simulations to GPU-accelerated engineering and biomedical systems. Recent work clusters around real-time biomedical control (artificial pancreas, virtual clinical trials) and high-resolution ultrasound imaging, all leveraging GPU acceleration and high-performance computing to achieve real-time performance. Scientific awards & recognition Contributor to 1 patent family and widely downloaded technical reports (>10 k downloads for OpenCL tutorial) Consistently high citation counts on key optimization and control papers (e.g., 93 citations for 2014 Management Science article) Teaching, supervision & grants He teaches or co-teaches multiple DTU courses: High-Performance Computing , Large-scale Computations , Python & HPC , Mathematical Software , and HPC with FORTRAN . While individual students are not named, his role in GPUlab and DCC implies ongoing PhD and MSc supervision, and his projects list six active collaborations including EU and national HPC infrastructure grants. Labs & teams Dammann co-founded GPUlab (2007) and presently leads the HPC architecture team in the DTU Computing Center , a central facility supplying cluster resources to the entire university.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۶ مورد دیگر

Hu Guo serves as a Lecturer in the Machine Learning section at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. Located at Universitetsparken 1, Copenhagen Ø, Hu Guo contributes to the department's research activities which span from theoretical foundations of machine learning to practical applications in diverse domains including medical data analysis, remote sensing, sustainability, and biological data modeling. The department participates in the SCIENCE AI Centre at the University of Copenhagen and maintains powerful compute resources for research. The research interests of Hu Guo encompass a broad spectrum of machine learning applications. Their work bridges theoretical machine learning with practical implementations across multiple disciplines. Notably, their research spans quantum computing applications in biomolecular simulations, natural language processing for emotion recognition and cross-cultural applications, medical AI for patient analysis and diagnostics, sustainable AI development with environmental considerations, and recommender systems with fairness evaluations. This interdisciplinary approach demonstrates strong connections between computer science theory and real-world problem solving in healthcare, environmental science, and cultural applications. Analysis of Hu Guo's recent publications reveals a strong focus on interdisciplinary applications of machine learning. Their work shows particular strength in quantum computing applications, medical AI, and sustainable computing practices. The publications demonstrate a pattern of addressing both theoretical challenges and practical implementations, with significant contributions to explainable AI, cross-cultural applications of language models, and energy-efficient computing solutions. The research consistently connects machine learning theory with real-world applications across healthcare, environmental science, and computational physics domains. Hu Guo actively contributes to the Machine Learning section's research activities, which include participation in the SCIENCE AI Centre and utilization of the department's powerful compute cluster. Their research aligns with the section's focus on both theoretical foundations and practical applications of machine learning across diverse domains including information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. The work contributes to initiatives like TreeSense - Centre for Remote Sensing and Deep Learning of Global Tree Resources.