جستجوی استاد راهنمای دانشگاهی

Renaud Lambiotteمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Temporal Networks
• +۷ مورد دیگر

Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

Christian Pascal Hirschمشاهده پروفایل

دانشیار

• Topological data analysis
• Large deviations theory
• Spatial random networks
• +۳ مورد دیگر

Christian Pascal Hirsch is an Associate Professor for Data Science and Statistics at the Department of Mathematics, Aarhus University. His research focuses on random networks inspired by biology and health sciences, utilizing techniques from topological data analysis and stochastic geometry. He is affiliated with the Stochastics group, AU DIGIT Centre, and AU Quantum Campus. Research Interests: Topological data analysis, large deviations theory, spatial random networks, and stochastic geometry. His work includes studies on percolation theory, Gibbs measures, and applications to neural networks and geometric functionals. Publications span journals such as the Journal of Applied and Computational Topology, Journal of Statistical Physics, and Stochastic Processes and Their Applications, covering topics from network topology to Poisson approximation.

Jes Frellsenمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Generative AI
• Deep Learning
• +۵ مورد دیگر

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 .

Thomas Ebelمشاهده پروفایل

استاد

• Power Electronics
• High-Voltage Electrical Engineering
• Dielectric Materials
• +۳ مورد دیگر

Thomas Ebel is Professor and Head of the Centre for Industrial Electronics at the University of Southern Denmark (SDU) , Institute of Mechanical and Electrical Engineering. A leading expert in power electronics, high-voltage engineering and capacitor technology, he directs large, multi-partner research projects and teaches/supervises at both graduate and PhD levels. Education & Career Path Prof. Ebel holds the academic title Dr. rer. nat. and has been appointed full Professor at SDU. He concurrently serves as Head of Section at the Centre for Industrial Electronics, orchestrating cross-disciplinary research teams and infrastructure. Research Interests Power Electronics & Power Conversion: advanced converter topologies, WBG devices (GaN, SiC), high-frequency magnetics, grid-forming control. Dielectric Materials & Capacitors: polymer and hybrid nanocomposite dielectrics, self-healing metallized film capacitors, aluminium electrolytic capacitors, lifetime modelling and reliability. High-Voltage Engineering & Breakdown Physics: breakdown mechanisms in nanocomposites, corona and partial discharge, insulation coordination. IoT & Data-Driven Monitoring: real-time condition monitoring, digital twins, data-driven RUL estimation for power components. Publication Trends Across 133 research outputs (2018-2025) the dominant themes are (i) construction and reliability of 700 V-class aluminium polymer electrolytic capacitors, (ii) GaN-based power converter optimisation, (iii) hybrid AC/DC microgrid control and harmonic mitigation, and (iv) nanocomposite dielectrics for next-generation capacitors. The 15 most recent articles (2025) reinforce these directions while adding socio-technical energy analytics and green-vehicle powertrains. Scientific Awards Tek Innovation Prize 2023 – awarded for outstanding contributions to power electronics research and industrial innovation. Advising & Funding Prof. Ebel currently supervises ~10 PhD candidates and post-docs including L. Tavares, M. A. Khan, R. Maheshwari, S. Mateen, A. N. Pinky and others. He is Principal Investigator or Head Coordinator of six active projects (2024-2027) valued at >€8 M, spanning ultra-high-efficiency drives, hydrogen-PtX converters, self-healing capacitors and hybrid power-plant concepts. Laboratory & Teams He heads the High-Voltage Power Electronics Laboratory at SDU, equipped with 700 V/200 A capacitor test rigs, GaN/SiC converter prototyping benches, and environmental chambers for accelerated ageing studies. The centre collaborates with 20+ industrial partners and coordinates the international IEA Wind Task 50 on hybrid power plants.

• Machine Learning
• Remote Sensing
• Geospatial Representation Learning
• +۳ مورد دیگر

Stefan Oehmcke is an Assistant Professor at the Machine Learning Section of the Department of Computer Science , University of Copenhagen. His research focuses on applying machine learning techniques to environmental and geospatial analysis, particularly in forest ecology, tree monitoring, and climate impact studies. Research Trends: His recent publications emphasize deep learning for LiDAR data processing, multi-modal geospatial representation, and sustainable AI practices. Key Collaborations: Frequently collaborates with researchers in environmental science, remote sensing, and climate change (e.g., Martin Brandt, Christian Igel). Applications: Develops tools for forest biomass estimation, tree mortality mapping, and urban safety analysis using satellite imagery. While no specific educational background or scientific awards are mentioned in the provided texts, Oehmcke's work demonstrates technical innovation in AI explainability and environmental monitoring, with significant contributions to journals like Remote Sensing of Environment and Nature Communications .

Kasper Green Larsenمشاهده پروفایل

استاد

• Machine Learning
• Theoretical Computer Science
• Algorithms and Data Structures
• +۳ مورد دیگر

Kasper Green Larsen is a Professor in the Department of Computer Science at Aarhus University. His research focuses on theoretical computer science, machine learning, algorithms, and data structures. He has made significant contributions to boosting algorithms, PAC learning theory, and computational geometry. His work often bridges algorithm design with complexity theory, addressing challenges in optimization, memory efficiency, and lower bounds analysis. Key research areas include: Algorithmic Learning Theory (e.g., boosting, bagging, and PAC learners) Data Structure Design (e.g., invertible Bloom tables, succinct representations) Computational Complexity (e.g., lower bounds for dynamic and oblivious algorithms) Geometric Algorithms (e.g., hierarchical searching, range queries) Recent publications emphasize foundational advancements in learning theory (e.g., optimal weak-to-strong learning) and data efficiency (e.g., memory-reduced Bloom filters). His work frequently appears in top conferences like IJCAI, ICALP, and SODA, reflecting rigorous theoretical contributions with practical implications.

Chenjuan Guoمشاهده پروفایل

دانشیار

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +۴ مورد دیگر

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

Haley Kujawaمشاهده پروفایل

پژوهشگر

• Environmental Modeling
• Water Quality Management
• Climate Change Impacts
• +۴ مورد دیگر

Haley Kujawa is a Postdoctoral Researcher at the Department of Biology, University of Southern Denmark (SDU). She is affiliated with the Aqua-NbS research center, focusing on environmental modeling to address water quality and carbon storage challenges under climate change. Her work emphasizes stakeholder engagement to ensure model results are practically applicable for policy and decision-making. Her research employs tools like the Soil and Water Assessment Tool (SWAT) to quantify nutrient runoff from agricultural landscapes and design management strategies. She has collaborated with governmental organizations, NGOs, and landowners to enhance model relevance. Current projects involve developing locally adaptable environmental models to evaluate aquatic nature-based solutions (NbS) across climate scenarios. Key research areas include watershed modeling, climate adaptation strategies, and interdisciplinary approaches to integrate models into policy frameworks. Her publications span hydrology, environmental management, and uncertainty analysis in climate-sensitive systems. Dr. Kujawa’s work bridges scientific rigor and real-world application, aiming to provide actionable insights for sustainable land and water management. She contributes to the Aqua-NbS center’s interdisciplinary efforts to create land-management scenarios tailored for policy contexts.

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.

Claus Brøndgaard Madsenمشاهده پروفایل

دانشیار

• Computer Vision
• Virtual Reality
• Augmented Reality
• +۷ مورد دیگر

Claus Brøndgaard Madsen is an Associate Professor in the Department of Architecture, Design and Media Technology at Aalborg University, under The Technical Faculty of IT and Design. He is based at Campus Aalborg and is affiliated with the Computer Graphics Group and VR Light research environment. His work bridges computer vision, virtual reality, and architectural design, with a strong focus on real-time visualization and human interaction in digital environments. Research Interests: His primary research areas include Computer Vision , Virtual Reality (VR) , Augmented Reality (AR) , 3D Reconstruction , Structure-from-Motion (SfM) , and Image-Based Lighting . These are applied in industrial VR, urban design evaluation, and sensory augmentation systems. His research fingerprint highlights strong engagement with Virtual Reality (100%), Augmented Reality (71%), Illumination Conditions (41%), and Wind Turbine Engineering (28%). The recent publications (2023–2025) reflect a consistent trend in enhancing user experience in VR/AR environments, with emphasis on spatial cognition, locomotion techniques, exposure control, and machine learning integration for lighting classification and part direction prediction in industrial contexts. These works are published in top-tier conferences such as EG, GRAPP, WACV, and CAADRIA. Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: Madsen has supervised PhD students and served as a supervisor in research projects like AAIVR. He is Principal Investigator (PI) in multiple funded projects, including BUMUS: Sustainable Expansion of Mussel Production (2024–2027), CityVR: Evaluating Parametric City Designs in VR (2020–2022), and V-BRT: Virtual Bus Rapid Transit (2019). These projects reflect substantial grant acquisition and leadership in applied VR research. Labs and Research Groups: He is a key member of the Computer Graphics Group and VR Light at Aalborg University, which focus on real-time visualization, lighting techniques, and immersive technologies for industrial and architectural applications.

Tom Kirchmaierمشاهده پروفایل

استاد

• Law and Economics
• Policing and Crime
• Banks and Money Laundering
• +۳ مورد دیگر

Tom Kirchmaier is Professor of Governance, Risk, Regulation and Compliance at Copenhagen Business School's (CBS) Department of Accounting and Centre for Corporate Governance. He concurrently serves as a Professorial Fellow and Director of Policing and Crime at the London School of Economics' Centre for Economic Performance. He is also a Faculty Affiliate at LSE's Data Science Institute and member of the UK Home Office Scientific Advisory Council. Research Interests: Kirchmaier's work empirically analyzes organizational governance using large datasets. Primary domains include: Law/economics intersections Policing systems and crime patterns Anti-money laundering (AML) and organised crime networks Corporate boards with emphasis on gender dynamics Risk management and compliance frameworks Publication Trends: Recent articles (2021-2025) demonstrate strong focus on empirical crime analysis (e.g., terrorism impacts, domestic abuse recidivism) and corporate governance in banking. Methodologies leverage big data to study spatial crime patterns, behavioral economics, and organizational efficiency across financial and public sectors. Awards: 2024 LSE Innovation Challenge Winner Teaching & Advising: Supervises 1 PhD and 1 Master's student. Teaches graduate/executive courses: Corporate governance and finance MBA compliance/risk management Board training programs

• Spatial data analysis
• Time series data
• Machine learning
• +۴ مورد دیگر

Maria Sinziiana Astefanoaei is an Assistant Professor at the IT University of Copenhagen , affiliated with the Data, Systems, and Robotics department. Her research focuses on spatiotemporal data analysis, urban computing, and graph algorithms using machine learning techniques. Research Interests: Spatial data analysis, Time series data processing, Large-scale visualizations, Machine learning, Human mobility modeling, and Embeddings. Projects: Principal Investigator for CCAI: Towards greener last-mile operations (2022-2023), contributing to cargo-bike logistics optimization, and Co-Investigator for the Pilot Hub project (2020-2022) funded by the Danish Agency for Research and Education. Publications: 2021 conference paper at CIKM '21 on spatiotemporal signal processing frameworks with neural machine learning models. Her work intersects computer science , urban logistics , and environmental sustainability , with applications in smart city technologies and multi-modal transportation systems.

Lise Justesenمشاهده پروفایل

دانشیار

• Digitalization
• Invisible Work
• Artificial Intelligence
• +۴ مورد دیگر

Lise Justesen is an Associate Professor at the Department of Organization, Copenhagen Business School . She holds a PhD from CBS and a Master's in Philosophy from the University of Copenhagen. Her research focuses on digitalization , invisible work , and the interplay between accounting and organizing , grounded in Actor-Network Theory and qualitative methods . Co-author of Digitalization and Invisible Work (2025), a critical examination of unforeseen consequences of AI implementation in public sector organizations Supervisor for PhD candidates across CBS departments and the Vienna University of Economics Collaborator with Ursula Plesner on Visibility Management and Digitalization in Public Sector projects Their work reveals recurring themes: digitalization's hidden labor demands , algorithmic accountability challenges , and managerial adaptation to AI systems . They analyze these through ethnographic studies of public sector transformations and critical assessments of predictive algorithms in governance. Justesen's scientific contributions examine how digital systems create new accountability frameworks and redefine professional roles . Their research demonstrates that digitalization produces unanticipated labor patterns , reconfigures human-machine relationships , and generates complex valuation practices in organizational contexts.

• Computational social science
• Intergroup relations
• Immigration
• +۳ مورد دیگر

Friedolin Merhout is an Assistant Professor in the Department of Sociology at the University of Copenhagen, holding a tenured-track position. He serves as Director of Graduate Studies for the interdisciplinary MSc in Social Data Science and coordinates the Welfare, Inequality, and Mobility research group. His affiliations include the Copenhagen Center for Social Data Science (SODAS). Education: B.A. from Freie Universität Berlin (Germany), Ph.D. from Duke University (USA) Roles: Director of Graduate Studies, Research Group Coordinator, Faculty Member His research focuses on computational and experimental methods to study intergroup relations using digital trace, text, survey, and administrative data. Key themes include political polarization, radicalization, and the impact of digital media. Recent work combines Google search data with military records to explore discrimination-radicalization links and uses online experiments to address polarization. His publications span computational sociology, political behavior, and research methodology, with notable contributions on Russian IRA influence on U.S. voters and the efficacy of mobile platforms to reduce polarization. He leads the Crowdsourced Replication Initiative, examining variability in social science research outcomes. Office hours are Tuesdays 15:00–16:00 in room 16.1.49.

Jamal Jokar Arsanjaniمشاهده پروفایل

استاد

• Remote Sensing
• Climate Change
• Land Use Change
• +۵ مورد دیگر

Jamal Jokar Arsanjani is a full professor in Geoinformatics and Earth Observation at Aalborg University (Denmark), leading the Geoinformatics & Earth Observation group. He holds a PhD from the University of Vienna (2011), with postdoctoral research at Heidelberg University supported by an Alexander von Humboldt fellowship (2012–2015). His academic career includes roles as a UN consultant in Vienna and senior scientist at Heidelberg's GIScience group. Research focuses on integrating geospatial data with computational models to address complex environmental challenges, including land use change, climate impacts, and natural hazards. Key methodologies include agent-based modeling, machine learning, and remote sensing. He contributes to UN Sustainable Development Goals, particularly SDGs related to climate action, sustainable cities, and responsible consumption. Recent projects include 'PROMISE' (micro-mobility integration in urban transport) and 'AI4Covid' (AI-driven pandemic monitoring). Awards include the Springer Outstanding PhD Dissertation (2012) and an Earth Observation award (2020). He serves on the European Environment Agency board and edits the journal Land . Research outputs span 105+ publications since 2008, emphasizing urban sustainability, disaster resilience, and geospatial technologies. Active in 7 ongoing projects and 45+ academic activities, he advises on policy and innovation in smart cities and climate adaptation.