جست‌وجوی استاد و پژوهشگر

• History of Mathematics
• History of Analysis
• History of Mechanics
• +۲ مورد دیگر

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

Nicki Skafte Detlefsenمشاهده پروفایل

دانشیار

• Deep Learning
• Representation Learning
• Machine Learning
• +۵ مورد دیگر

Nicki Skafte Detlefsen serves as an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing within the Cognitive Systems research section. His institutional affiliation includes the Richard Petersens Plads campus in Kgs. Lyngby, Denmark, with primary research activities centered on advanced machine learning methodologies. Dr. Detlefsen's research program focuses on deep learning architectures for representation learning, particularly emphasizing invariant representations through inductive bias incorporation. His work spans protein sequence analysis using generative models, temporal alignment networks for time-series data, and stochastic representation frameworks via Laplacian autoencoders. Key contributions include developing methods for predictive uncertainty estimation and addressing suboptimal performance in biological sequence modeling, with strong connections to computational biology and neural network theory. Analysis of his 2019-2025 publications reveals an evolving research trajectory from foundational representation learning techniques toward broader AI ecosystem development. Early work centered on protein sequence modeling and temporal alignment algorithms, culminating in high-impact publications like the Nature Communications paper on protein representations. His most recent 2025 publication demonstrates strategic expansion into European HPC/AI infrastructure development, indicating a shift toward large-scale collaborative AI initiatives while maintaining core expertise in representation learning. No formal scientific awards or fellowships are documented in the source material. Dr. Detlefsen completed his PhD at DTU in 2020 under Søren Hauberg's supervision through the "Deep Metric Learning" project (2017-2021), which received significant academic attention with 328 Orbit downloads and coverage by 6 news outlets. His current research appears institutionally funded through DTU's Cognitive Systems section, with emerging involvement in pan-European AI infrastructure projects as evidenced by the 2025 HPC/AI ecosystem publication. He operates within DTU's Cognitive Systems research environment, collaborating extensively with Søren Hauberg, Ole Winther, and international researchers. His work shows strong interdisciplinary connections between computer science, bioinformatics, and neural engineering, with recent expansion into AI policy and infrastructure development through European collaborative networks.

Fritzi Grevstadمشاهده پروفایل

استادیار

• Population Ecology
• Invasive Species Ecology
• Plant-Insect Interactions
• +۷ مورد دیگر

Fritzi Grevstad is an Assistant Professor (Senior Research) in the Department of Botany and Plant Pathology at Oregon State University, affiliated with the College of Agricultural Sciences. Her research is centered at the Richardson Hall Quarantine Facility, where she leads studies on classical biological control of invasive weeds such as knotweeds (Fallopia spp.) and gorse (Ulex europaeus). She works with insect agents collected from native ranges to assess host specificity and efficacy for safe field release. Her research interests include: Population and invasion ecology Plant-herbivore interactions Biological control of invasive plants Phenology and photoperiodism in introduced insects Modeling climatic suitability and phenological mismatch Optimization of biocontrol release strategies The trends in her recent publications show a strong focus on the integration of ecological modeling with empirical studies in biological control. Her work emphasizes the importance of phenological alignment between biocontrol agents and host plants, particularly under climate change. She investigates genetic variation among biocontrol populations, host specificity testing, and long-term impacts of agent releases. Much of her research involves collaborative modeling tools like DDRP for real-time pest forecasting. Although no specific scientific awards are mentioned in the provided text, her extensive publication record in high-impact journals such as Ecological Applications , Biological Control , and Global Change Biology reflects significant scholarly contributions. She has authored technical petitions for field release of biocontrol agents, extension publications for practitioners, and book chapters summarizing national biocontrol programs. Dr. Grevstad advises on the biology and implementation of biocontrol programs, with a history of collaborative grants and research projects involving agencies like the USDA Forest Service and Department of Defense. Her work includes developing decision-support tools and models for weed biocontrol on federal lands. She has co-authored multiple technical reports and petitions submitted to the Technical Advisory Group on Biological Control of Weeds, indicating active grant-funded and regulatory-engaged research. She leads a research team focused on invasive species management, working closely with entomologists, modelers, and quarantine specialists. The team conducts both laboratory and field studies, particularly within the Richardson Hall Quarantine Facility at OSU, to ensure safe and effective development of biological control programs.

Ju Fengمشاهده پروفایل

پژوهشگر

• Wind Energy Systems
• Floating Offshore Wind Farms
• Wind Farm Optimization
• +۶ مورد دیگر

Ju Feng is a Senior Researcher at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), specializing in Wind Energy Systems , Wind Farm Optimization , and Control Strategies . He contributes to research in Floating Offshore Wind Farms , Multi-Disciplinary Optimization , and Noise Propagation . His work includes developing simulation tools, wake models, and control frameworks for wind farms in complex environments. Research Interests: Optimization of wind farm design and operation Advanced wake and flow modeling AI-driven wind energy solutions Noise control and acoustic propagation Multi-fidelity/multi-disciplinary modeling Cost-effective wind farm technologies Publication Trends: Ju Feng's recent work (2022-2024) focuses on floating offshore wind farms , noise constraints , and multi-disciplinary integration . Older articles (2012-2017) emphasize control algorithms , wake modeling , and optimization frameworks . Advising: Supervising PhD Student Wang R. (AI-Augmented Wind Farm Design Optimization, 2025-2028) Co-supervising Nyborg C. M. (Noise Control in Wind Farms, 2019-2023) Collaboration Network: Active in international projects like DecoWind, ModFarm, and FarmConners. Collaborates with institutions such as DTU Wind Energy and researchers including Shen W. Z. , Réthoré P.-E. , and Bredmose H. .

• Business economics
• Statistics
• Probability theory
• +۱ مورد دیگر

Fioralba Ajazi is a Research Fellow at the Danish Institute for IT Program Management, specializing in Software Engineering. She is a Co-Investigator on the Villum Kann Rasmussen Professorship project (2022–2027), focusing on IT Program Management and Program Management methodologies. Her work involves interdisciplinary research across Business economics, Statistics, Probability theory, and Applied mathematics. Research Interests Her research explores the intersection of stochastic processes and network modeling, particularly in neuronal connectivity and spatial network structures. This includes applications of random geometric graphs, phase transitions in complex systems, and the analysis of 2D network growth dynamics. Project Highlights Villum Foundation Project: Villum Kann Rasmussen Professorship (2022–2027), contributing to IT and program management frameworks. Awards No scientific awards explicitly mentioned, though she participates in a prestigious Villum Foundation-funded initiative. Advising & Grants While no students are listed, she collaborates on a multi-year, multi-disciplinary grant focusing on program management methodologies. Funding: Villum Foundation (~DKK 100M).

Ariya Sangwongwanichمشاهده پروفایل

دانشیار

• Power Electronics
• Photovoltaic Systems
• Grid-Connected Converters
• +۵ مورد دیگر

Ariya Sangwongwanich is an Associate Professor at the Department of Energy Technology, Aalborg University, within the Faculty of Engineering and Science. He leads the Sustainable Power Electronics and Electrification (SPELL) research program and has conducted visiting research at RWTH Aachen (2017) and the University of Cambridge (2023). Education: B.Eng. in Electrical Engineering, Chulalongkorn University (2013) M.Sc. in Energy Engineering, Aalborg University (2015) Ph.D., Aalborg University (2018) His research focuses on power electronics, grid-connected converters, photovoltaic systems, and reliability/sustainability in energy systems. Recent work emphasizes fault diagnosis, digital twin technology, and battery-PV system interactions. Notable scientific awards include the Danish Academy of Natural Sciences' Ph.D. Prize (2019), Spar Nord Foundation Research Award (2019), IEEE IPEMC-ECCE Asia Best Paper Award (2020), Best Presentation Awards at IEEE APEC (2016, 2021), and a DFF Sapere Aude Starting Grant (2024). Key projects include: GreenMag: Green High-Power Planar Magnetic Components (2025-2029, Innovation Fund Denmark) CirclePE: Circular Power Electronics for Green Transition (2025-2028, Independent Research Fund Denmark) OMRES: Operation & Digital Twin Solutions for Hybrid Renewable Energy (2025-2027, Horizon Europe) TEAMING: Predictive Maintenance for e-Powertrains (2024-2027, Horizon Europe) ALL2GaN: GaN IC Solutions for Greener Applications (2023-2026, Horizon Chips)

• Stochastic Control Theory
• Nonparametric Estimation
• Diffusion Processes
• +۳ مورد دیگر

Claudia Strauch is an Associate Professor at the Department of Mathematics, Aarhus University . She is currently on leave and can be reached via email at strauch@math.au.dk. Her research focuses on nonparametric methods for stochastic processes, particularly in diffusion dynamics and control theory. Research Interests: Stochastic control, nonparametric estimation of diffusions, Hamiltonian systems, and machine learning applications to stochastic dynamics. Selected Projects: Exploring nonparametric modeling via stochastic PDEs (2021–2024) Learning diffusion dynamics for optimal control (2021–2024)

• Bisimulation
• Probabilistic Automata
• Mobile Networks
• +۷ مورد دیگر

Jens Christian Godskesen is a Lecturer in Theoretical Computer Science at the IT University of Copenhagen. He has maintained an active research career with 19 publications and involvement in 20 research projects spanning from 1999 to the present. His academic work focuses on formal methods and theoretical computer science with particular emphasis on bisimulation techniques and probabilistic systems. Godskesen's research interests center around bisimulation theory, probabilistic automata, mobile networks, and stochastic calculus. His work explores behavioral equivalence in computational systems, logical characterizations of probabilistic models, and formal verification techniques. He has made significant contributions to understanding probabilistic bisimulation for realistic schedulers and developing abstraction refinement techniques for complex systems. His research bridges theoretical computer science with practical applications in security and networking. Analysis of his publication trends shows a consistent focus on bisimulation theory and probabilistic systems from 2012-2015, with recent expansion into blockchain technology and urban data analytics. His work demonstrates strong theoretical foundations while addressing practical challenges in mobile security and network modeling. The interdisciplinary nature of his research connects computer science theory with applications in security, networking, and fintech. Godskesen has served as Principal Investigator for numerous research projects including the ongoing Urban Insights Data Lab (2023-present), the Velux Visiting Professor Program (2011-2012, 2015-2016), CMS: Calculi for Mobile Security (2006-2010), and the 6th International Blockchain School (2023). He has also contributed to academic governance as chair of the hiring committee for a full professor in Digital Play (2022-2023). With 92 media contributions documented through 2024, Godskesen actively engages with public discourse on IT education, blockchain technology, and digital leadership. His recent media appearances include commentary on the IT University's new vice-chancellor at the University of Copenhagen (November 2024), DigitalLead's new board of directors (August 2024), and concerns about declining enrollment in Danish IT programs (July 2022).

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.

• Cancer Research
• Breast Cancer
• Triple-Negative Breast Cancer
• +۸ مورد دیگر

Odd Lilleng Gammelgaard is an Associate Professor in the Department of Molecular Medicine at the Faculty of Health Sciences, University of Southern Denmark. His research is centered on cancer biology, with a particular emphasis on breast cancer, metastasis, tumor immunology, and epigenetic regulation. His research interests span triple-negative breast cancer , myeloid-derived suppressor cells , immunotherapy , epigenetic mechanisms such as DNA methylation, and age-related differences in cancer biology . He investigates how tumor microenvironments evolve and how therapeutic interventions can be optimized through targeting immune evasion and epigenetic dysregulation. The recent publication trends indicate a strong focus on immunomodulatory strategies , including adoptive cell transfer and anti-PD-L1 therapy, epigenetic drug responses , and preclinical modeling using PDX and murine systems. His work integrates molecular oncology with translational immunology to identify novel therapeutic vulnerabilities. Scientific Awards: Årets bedste foredag (2016) Metastasis as a systemic disease (2017) Dr. Gammelgaard has actively participated in immunology training programs such as the AAI Advanced Course in Immunology and presented at key conferences. His collaborative network includes prominent researchers in oncology and immunology, and he contributes to both experimental and clinical cancer research. While no formal advising roles or grants are detailed in the text, his co-authorship on multiple high-impact studies suggests significant involvement in research supervision and team science. He is involved in developing novel therapeutic strategies such as bispecific antibodies and CD73-targeting agents, and his work on liquid biopsy and biomarker discovery aims to improve early detection and treatment monitoring. His research continues to bridge molecular mechanisms with clinical applications in oncology.

• Operational Research
• Healthcare Optimization
• Stochastic Optimization
• +۷ مورد دیگر

Troels Martin Range is an Associate Professor at the Department of Clinical Research, University of Southern Denmark, affiliated with Odense University Hospital (OUH) and the Research Unit of Emergency Medicine in Odense. His work bridges operational research and clinical medicine, focusing on optimizing healthcare delivery systems. His research interests lie primarily in operational research , stochastic optimization , and healthcare analytics . He investigates emergency department patient flow , surgery scheduling , and resource allocation using advanced mathematical models such as column generation , simulation modeling , and machine learning techniques . His work often integrates real-world data including meteorological and calendar variables to improve forecasting accuracy. The analysis of his recent publications reveals a consistent trend toward applying rigorous mathematical and computational methods to solve complex problems in healthcare operations. His work spans both theoretical contributions in Mathematical Programming and practical implementations in hospital settings, particularly in emergency and surgical care. He frequently employs stochastic modeling , decision forests , and neural networks to address uncertainty in medical systems. Editor, International Journal of Advanced Operations Management Member, Dansk Selskab for Operationsanalyse (DORS) Guest Lecturer on hospital staff rostering and resource planning He has taught courses in operations analysis and has supervised academic work, though specific advisees are not listed. His research has been supported through academic collaborations and implementation projects in hospital environments. He is actively involved in knowledge transfer between academia and healthcare institutions. Dr. Range is part of a research network focused on emergency medicine and operational efficiency, contributing to both national and international collaborations. His lab or team context centers on the Research Unit of Emergency Medicine at OUH, where data-driven approaches are developed to enhance clinical decision-making and system performance.

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.

Dolores Romero Moralesمشاهده پروفایل

استاد

• Data Science
• Supply Chain Optimization
• Revenue Management
• +۳ مورد دیگر

Dolores Romero Morales is a Professor of Operations Research at the Department of Economics, Copenhagen Business School (CBS). She holds editorial roles as Editor-in-Chief of TOP (Spanish Society of Statistics and Operations Research journal) and Associate Editor of Journal of the Operational Research Society and INFORMS Journal on Data Science . Prior roles include Full Professor at the University of Oxford (2003–2014) and Assistant Professor at Maastricht University (2000–2014). Education: BSc and MSc in Mathematics, Universidad de Sevilla PhD in Operations Research, Erasmus University Rotterdam Research Interests: Her work focuses on Data Science (explainability, fairness, visualization), Supply Chain Optimization (environmental sustainability, robustness), and Revenue Management (large-scale network models). Recent contributions include counterfactual analysis frameworks for machine learning models and optimization-driven solutions for decision-making under uncertainty. Grants & Projects: Leading the EU NeEDS project (€1M+ budget) to advance Data-Driven Decision Making through intersectoral collaboration. Recipient of Otto Mønsted Visiting Professorship funding for conference participation. Teaching: Current courses include Analytics and Big Data (MBA), Data Science: Data Driven Decision Making (MSc), and Mathematical Optimization (MSc). Supervised ten PhD dissertations with one ongoing. Awards & Recognition: Honorary SAS Fellow (SAS Institute) Member of SAS Academic Advisory Board

Line Reinhardtمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Operations Research
• Maritime Logistics
• +۳ مورد دیگر

Line Reinhardt is an Associate Professor at Roskilde University's Department of People and Technology Programming, Logic and Intelligent Systems, and leads the Centre for Maritime and Marine Research. His primary research focuses on combinatorial optimization and operations research, with applications in maritime logistics, rail systems, healthcare operations, and IoT-enabled transportation solutions. He collaborates extensively with industry partners to ensure practical relevance of his work. Key research areas include container stowage planning, route optimization for maritime networks, and AI-driven solutions for energy-efficient liner shipping. His work bridges theoretical optimization with real-world implementation, addressing challenges in port operations, hospital resource allocation, and sustainable transportation systems. Reinhardt has led or participated in multiple research projects, including the Innovation Fund-backed 'Stowage Algorithms and AI for Energy Efficient Liner Shipping' (2022–2025) and the EU-funded 'Game For Green' project (2023–2026). He was awarded a prize for cargo transport optimization in 2023 and has been featured in media for advancing green maritime solutions. Recent Projects: Game For Green (2023–2026): Developing green transition strategies for the blue economy. ROROGREEN (2020–2024): Digital innovation for sustainable roll-on/roll-off shipping. His academic contributions span over 25 peer-reviewed publications, with a focus on algorithmic innovation and systems optimization. Reinhardt also engages in academic outreach, organizing PhD courses and serving as an examiner in doctoral evaluations.