جست‌وجوی استاد و پژوهشگر

Jesper Rindom Jensenمشاهده پروفایل

دانشیار

• Audio Signal Processing
• Artificial Intelligence
• Speech Enhancement
• +۱۲ مورد دیگر

Jesper Rindom Jensen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark, under the Technical Faculty of IT and Design. He is the Head of the Audio Analysis Lab, a leading research group in audio signal processing, since 2023. His work bridges theoretical signal processing and practical applications in artificial intelligence and audio systems. Full Name: Jesper Rindom Jensen Institution: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Audio Analysis Lab Email: jrj@es.aau.dk Office: Fredrik Bajers Vej 7B, B5-206, 9220 Aalborg Øst, Denmark Education: M.Sc. in Electronic Systems, Aalborg University (cum laude, 2009) Ph.D. in Signal Processing, Aalborg University (2012) Research Interests: Jesper Rindom Jensen's research centers on audio signal processing, with a strong emphasis on artificial intelligence, speech enhancement, noise reduction, beamforming, and multichannel systems. His work applies to diverse domains including robot and drone audition, spatial audio, and active noise control. He develops novel filtering techniques, including variable span linear filters and harmonic beamformers, to improve speech quality and intelligibility in noisy and reverberant environments. Publication Trends: His recent publications (2023–2025) show a strong trend toward integrating deep learning with classical signal processing, particularly in direction-of-arrival estimation, underwater acoustics, and robust multichannel systems. There is a clear focus on real-world applications, including sound zone control, active noise control, and limited-data scenarios using knowledge distillation. His work consistently emphasizes robustness, efficiency, and practical deployment. Scientific Awards and Recognition: AAU Talent for emerging research leaders Recipient of a competitive postdoc grant from the Danish Independent Research Council Advising and Grants: Jesper has supervised multiple PhD and master’s students, including Nørholm, Karimian-Azari, Zhang, and Wang. He has led significant research projects such as 'Sound Processing for Robots and Drones' (2018–2020) and participated in others related to joint audio-visual tracking and speech enhancement. His research has been supported by national funding bodies, reflecting its innovation and impact. Labs and Teams: He is a founding and core member of the Audio Analysis Lab at Aalborg University, which focuses on cutting-edge audio signal processing and AI-driven solutions. The lab fosters interdisciplinary collaboration and has produced numerous publications, datasets, and real-world applications. Jensen’s leadership since 2023 underscores his pivotal role in shaping the lab’s research direction.

• Systems Biology
• Stress Response
• Stem Cell Differentiation
• +۳ مورد دیگر

Ala Trusina is an Associate Professor at the Niels Bohr Institute , University of Copenhagen , specializing in Biocomplexity and Biophysics . Her research integrates coarse-grained modeling to study complex biological systems. Key Research Areas Stress Response Systems (diabetes, aging, cancer, inflammation) Stem Cell Differentiation (cell fate coordination, reversibility of states) Complex Systems (species coexistence, epidemics, CRISPR-phage interactions) Methodologies Theoretical: Agent-based modeling, in-silico simulations Experimental: Quantitative single-cell imaging, RNA/protein profiling Collaborations Joshua Brickman (ES cells), Anne Grapin-Botton (pancreas), Feroz Papa (diabetes), Else Kai Hoffman (p53 dynamics) Thomas Mandrup-Poulsen (inflammation), Savas Tay (spatio-temporal regulation) Teaching Physics of Molecular Diseases Numerical Methods in Physics

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.

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.

Clara Marika Velteمشاهده پروفایل

دانشیار

• Fluid Mechanics
• Turbulence
• Turbulence Theory
• +۱۴ مورد دیگر

Clara Marika Velte is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), where she serves as Head of the Turbulence Centre of Excellence. Her research is centered on fluid mechanics, particularly turbulence, with a focus on theoretical modeling, experimental design, and vortex dynamics. She is actively involved in major research initiatives funded by the European Research Council and the Poul Due Jensen Foundation. PhD in Experimental and Theoretical Fluid Mechanics, DTU (2006–2009) MSc in Turbulence, Chalmers University of Technology (2004–2005) Her research interests include turbulence theory, boundary layers, flow control, vortex dynamics, and the development of theoretical models for turbulent flows. She employs advanced techniques such as Proper Orthogonal Decomposition, Reynolds decomposition, and modal analysis to study non-equilibrium turbulence and jet dynamics. Her work bridges fundamental physics with practical applications in energy and environmental engineering. Recent publications explore triad interactions, spatio-temporal structure functions, and Lumley decomposition in turbulent round jets, indicating a strong trend toward mathematical and physical modeling of turbulence dynamics. These works are targeted at high-impact journals such as Physical Review Fluids and Journal of Fluid Mechanics , reflecting her leadership in advancing theoretical frameworks in fluid dynamics. Best paper award, SEMA’18 (2018) Kalundborg Refinery Prisen (2023) Selected for special issue in Experiments in Fluids (2010) Publication in Journal of Systemics, Cybernetics and Informatics (2009) Clara Velte supervises multiple PhD students and leads several active research projects, including the PDJF Turbulence Centre of Excellence and UniEqTURB, which challenge classical paradigms like the Richardson-Kolmogorov theory. She also contributes to academic service as a member of the Editorial Board for Philosophical Transactions of the Royal Society A . Her laboratory, the Turbulence Research Laboratory at DTU, fosters interdisciplinary collaboration and innovation in turbulence science. The Turbulence Centre of Excellence at DTU, under her leadership, operates as a state-of-the-art research hub integrating experimental, theoretical, and computational approaches. The center collaborates internationally and engages in public outreach, including media appearances on turbulence in aviation and climate systems.

Yan Zhaoمشاهده پروفایل

دانشیار

• Anomaly Detection
• Autoencoder
• Attention (Machine Learning)
• +۵ مورد دیگر

Yan Zhao is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, science, and systems, with a particular emphasis on anomaly detection, machine learning, and spatio-temporal data analysis. He holds a Ph.D. in Computer Science (specific education details not explicitly provided). Research Interests: Dr. Zhao's work spans anomaly detection, autoencoders, attention mechanisms, preference learning, multivariate time series, and computational efficiency. His research often integrates machine learning with real-world applications in spatial crowdsourcing, trajectory analysis, and data privacy. Publications: With 72+ publications, his recent work emphasizes spatio-temporal prediction frameworks, federated learning, and efficient time series analysis. Notable contributions include frameworks for continuous learning on streaming data and privacy-preserving clustering in spatial crowdsourcing. Grants & Supervision: He has supervised one Ph.D. student and actively contributes to research grants focusing on data engineering and smart systems. His work bridges theoretical advancements with practical applications in transportation, social networks, and IoT.

Maxi San Miguelمشاهده پروفایل

استاد

• Statistical Physics
• Nonlinear Physics
• Complex Systems
• +۸ مورد دیگر

Maxi San Miguel is Professor of Physics at the University of the Balearic Islands (UIB) and Director of IFISC (Institute for Cross-Disciplinary Physics and Complex Systems), a joint research institute of CSIC and UIB in Palma de Mallorca, Spain. He has held academic positions at the University of Barcelona, Temple University, Università di Roma La Sapienza, University of Arizona, University of Strathclyde, and Helsinki University of Technology. His research spans statistical and nonlinear physics , including stochastic processes, phase transitions, pattern formation, spatio-temporal complexity, and complex networks, with interdisciplinary applications in computational social science , laser physics , and photonics . He has authored over 400 papers in top-tier journals across physics, engineering, ecology, and multidisciplinary sciences. The recent publication trend reflects a strong focus on foundational and interdisciplinary aspects of complex systems, particularly in the context of the 2021 Nobel Prize in Physics, highlighting advances in understanding emergent behavior, network dynamics, and cross-disciplinary modeling. Scientific Awards: Medal of the Spanish Physical Society-Fundacion BBVA (2010) Senior Scientific Award 2015 of the Complex Systems Society Maxi San Miguel has led major research initiatives through IFISC, securing substantial grants in complex systems science. He advises a broad research group in nonlinear dynamics and interdisciplinary physics, though specific student names are not listed. His leadership includes directing IFISC and fostering international collaborations across Europe and the United States. He is a key figure in the complex systems community, contributing to major collaborative projects and shaping the future of cross-disciplinary physics. His work continues to influence both theoretical developments and real-world applications in social, ecological, and technological systems.

Rolf Lyneborg Lundمشاهده پروفایل

دانشیار

• Social Inequality
• Urban Studies
• Sociological Data Modeling
• +۵ مورد دیگر

Rolf Lyneborg Lund is an Associate Professor in the Department of Sociology and Social Work at Aalborg University, affiliated with The Faculty of Social Sciences and Humanities. He is also an External Lecturer at Aarhus University. His research focuses on social inequality, sociological data modeling, and integrating artificial intelligence into sociological research. He explores how sociology can intersect with computer science to enhance understanding of social systems and automate data analysis. Key research themes include urban-rural disparities, neighborhood effects on youth wellbeing, and the long-term impacts of Danish 'ghetto' areas on educational outcomes. Lund leads projects like 'MASSHINE' (Mission: Improved Wellbeing Among Children And Youth In Denmark) and 'SocMap - Research Group in Sociological Mapping'. Projects span sociogeographical analyses of vulnerable children, pediatric cancer treatment proximity, and rural-urban quality of life comparisons. Publications (2016-2025) address topics like neighborhood composition's impact on immigrant employment, psychosocial consequences of healthcare access distance, and algorithmic approaches to spatial sociology. He has received multiple awards including the 'Årets underviser' (Best Teacher) from 2018-2021 and the Spar Nord Research Prize (2020). His work contributes to UN Sustainable Development Goals related to reducing inequalities and promoting wellbeing. Lund supervises PhD students such as Cecilie Høymark (geographical analysis of vulnerable youth) and Kirsten Kjelmann (immigrant job attainment via co-ethnic networks). He collaborates across disciplines with institutions like the North Denmark Region and Aalborg University Hospital. Labs/Groups: SocMap Research Group, MASSHINE initiative.

Anders Nielsenمشاهده پروفایل

استاد

• Biostatistics
• Stock Assessment
• Fish Population Dynamics
• +۵ مورد دیگر

Anders Nielsen is a Professor at the National Institute of Aquatic Resources (DTU Aqua), Technical University of Denmark, working within the Section for Marine Living Resources. His research focuses on the intersection of biostatistics, fisheries science, and marine ecology, with particular emphasis on developing statistically sound approaches for fish stock assessment and management. His research interests span biostatistics with a focus on modeling dynamical systems and state-space models, fish stock assessment methodologies, fish movement patterns, and statistical software development. Nielsen specializes in improving stock assessment models through better statistical frameworks, optimized sampling designs, and incorporation of spatial structure. His work on automatic differentiation in statistical tools has contributed significantly to computational approaches in fisheries science. Analysis of Nielsen's recent publications reveals strong trends in environmental DNA (eDNA) applications for fisheries monitoring, advanced statistical modeling for stock assessments, and integration of climate variables into fish population dynamics. His work bridges theoretical statistical development with practical fisheries management applications, particularly in North Sea and Baltic Sea ecosystems. The recurring themes include uncertainty quantification, retrospective analysis of assessment models, and development of robust frameworks for sustainable fisheries management. Nielsen actively participates in numerous research projects including MAKE-IT (sustainable mackerel management), FISHMAP (fish distribution and fisheries management), REVIVAL (herring stock assessment), CanMac (genetic approaches to mackerel assessment), and LenSAM (length-based assessment models). He serves as project coordinator for CanMac and LenSAM, demonstrating leadership in developing innovative assessment methodologies. His collaborative network spans multiple institutions and countries, reflecting the international nature of fisheries science. As a member of the International Council for the Exploration of the Sea (ICES), Nielsen contributes to international fisheries science and advice. His work directly supports UN Sustainable Development Goals related to sustainable marine resource management. While specific details about laboratory facilities aren't provided in the available information, his research clearly involves advanced computational modeling and analysis of fisheries data, likely utilizing high-performance computing resources at DTU.

Torben Bach Pedersenمشاهده پروفایل

استاد

• Data Engineering
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Professor Torben Bach Pedersen at Aalborg University's Department of Computer Science within The Technical Faculty of IT and Design is a leading expert in Data Engineering, Artificial Intelligence, and Energy Systems. With over 394 publications and 19 completed projects, he directs research at the Daisy – Center for Data-intensive Systems and leads innovations in energy flexibility and smart grid technologies. Key research areas: Data Warehousing, AI/ML, Energy Systems, Smart Grids Major projects: domOS (Smart Building OS), FEVER (Virtual Power Plants), DiCyPS (Cyber-Physical Systems) His research spans data-intensive systems, AI applications in energy management, and smart infrastructure development. Recent work focuses on transformer-based network AI and energy flexibility metrics. Scientific recognition includes: Æresdoktor (Honorary Doctor) at TU Dresden (2021) Best Paper Award Runner-Up (2019) WWW 2017 Best Demo Award Best Poster Award World Smart Grid Forum (2013) Member of Danish Academy of Technical Sciences (2013) As principal investigator and supervisor in 17 PhD projects, he advances AI-driven solutions for 6G wireless systems, smart buildings, and energy market optimization.

• Quantum Physics
• Superconductivity
• Nanowire Devices
• +۳ مورد دیگر

Yu Liu is a researcher at the Center for Quantum Devices within the Niels Bohr Institute at the University of Copenhagen. The Center for Quantum Devices focuses on cutting-edge research in quantum physics, particularly in areas related to spin qubits, topological quantum systems, novel devices, superconducting qubits, and quantum materials. Dr. Liu's research spans multiple disciplines within quantum physics and condensed matter. His primary focus appears to be on quantum devices, particularly investigating superconductivity in hybrid nanowire systems with ferromagnetic components. His work explores spin-split superconductivity, supercurrent transport, and spin-polarized bound states in semiconductor-superconductor-ferromagnetic-insulator hybrid structures. Beyond quantum physics, Dr. Liu has also published work in computer vision, mathematics, particle physics, transportation studies, and medical imaging, demonstrating remarkable interdisciplinary breadth. His recent publications (2021-2025) show a strong focus on quantum transport phenomena in hybrid nanostructures, particularly examining the interplay between superconductivity, ferromagnetism, and spin-orbit coupling in nanowire systems. These investigations have important implications for the development of topological quantum computing platforms and novel quantum devices. The observed phenomena include supercurrent reversal near coercive fields of ferromagnetic insulators, spin-splitting exceeding induced superconducting gaps, and percolative supercurrent flow in bilayer systems. Dr. Liu has collaborated extensively with researchers at the Center for Quantum Devices, including P. Krogstrup, S. Vaitiekėnas, C. M. Marcus, M. Leijnse, and R. Seoane Souto. His work often involves experimental measurements combined with theoretical modeling to explain observed phenomena in quantum transport systems, bridging the gap between fundamental physics and potential quantum technology applications.

Søren Wengel Mogensenمشاهده پروفایل

دانشیار

• Causal Inference
• Stochastic Processes
• Survival Analysis
• +۳ مورد دیگر

Søren Wengel Mogensen is an Associate Professor at the Department of Finance, Copenhagen Business School, Denmark. His research focuses on developing advanced statistical and machine learning methodologies for complex systems analysis. Research Interests: Dr. Mogensen's work spans causal inference, stochastic processes, survival analysis, and time-series modeling. Key themes include: Causal discovery algorithms for industrial and biological systems Graphical representations of dependencies in high-dimensional data Time-varying mediation in survival contexts Bayesian networks for cascade modeling Publication Trends: His recent articles (2021-2025) demonstrate a strong emphasis on theoretical-statistical innovation with applications in healthcare, industrial monitoring, and computational finance. Dominant methodologies include kernel-based independence tests, continuous-time Bayesian networks, and constrained stochastic process modeling.

Mads Stehrمشاهده پروفایل

دانشیار

• Applied Probability Theory
• Lévy-based Spatio-temporal Modeling
• Extreme Value Theory
• +۳ مورد دیگر

Mads Stehr is an Associate Professor in Statistics at the Department of Finance, Copenhagen Business School (CBS), and a member of the Center for Statistics. He holds a PhD in Statistics and Probability Theory from Aarhus University. His primary research focuses on applied probability theory, including Lévy-based modeling, extreme value theory, and numerical integration methods. Education: PhD in Statistics and Probability Theory (Aarhus University) His research interests span Lévy processes, spatial and spatio-temporal modeling, and the application of extreme value theory to stochastic systems. He has contributed significantly to improving numerical integration techniques like the Cavalieri estimator and Newton–Cotes quadratures under non-equidistant sampling conditions. Key publications include studies on extremal clustering in spatial random fields and tail asymptotics of Lévy-driven models. While no scientific awards are explicitly mentioned, his work demonstrates impactful contributions to statistical methodology and stochastic analysis. Teaching responsibilities include courses such as Stokastiske processer og deres statistiske analyse (Stochastic Processes and Their Statistical Analysis) and Matematisk statistisk (Mathematical Statistics). His doctoral thesis, Stereology and Spatio-Temporal Models , further underscores his expertise in integrating statistical methods with complex spatial-temporal systems.

• Bayesian Statistics
• Gaussian Processes
• Variational Inference
• +۵ مورد دیگر

Michael Riis Andersen is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute , Technical University of Denmark. His research focuses on Bayesian statistics, Gaussian processes, variational inference, and probabilistic programming, with applications in computer vision, natural language processing, and spatio-temporal modeling. His recent work includes: (1) Bayesian optimization with uncertainty-aware frameworks, (2) recommender systems balancing accuracy with editorial constraints, (3) geometry-aware transformers for geospatial analysis, and (4) scalable inference methods for neural networks and Gaussian processes. Many articles explore variational inference and probabilistic programming to enhance computational reliability and efficiency. Research keywords : Bayesian Statistics, Variational Inference, Machine Learning, Computer Vision, Probabilistic Programming, Spatio-Temporal Modeling.

Ute Hahnمشاهده پروفایل

دانشیار

• Statistics
• Biostatistics
• Neurodegenerative Diseases
• +۵ مورد دیگر

Ute Hahn is an Associate Professor at the Department of Mathematics, Aarhus University . Her research bridges Statistics, Biostatistics, and Neuroscience , with a focus on neurodegenerative diseases like ALS and advanced microscopy techniques . Research Interests: Statistical modeling in ALS and genetic research Stochastic geometry and spatio-temporal analysis Super-resolution microscopy (PALM, STED) Medical statistics and clinical epidemiology Functional data analysis and methodological innovations Selected Publications highlight her interdisciplinary work, including studies on ALS mouse models , microscopy artifacts , and spatio-temporal patterns in medical and environmental contexts . Collaborative Projects: Ute is actively involved in the MechanoGeometry research initiative, which began in 2022 and continues into 2023.