جستجوی استاد راهنمای دانشگاهی

Jesper Møller is a Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, specializing in Statistics and Mathematical Economics. His research focuses on advanced statistical methodologies with applications across various scientific domains. His educational background includes extensive training in mathematical sciences, though specific degree details aren't provided in the current materials. His research interests span: Applied probability theory Markov chain Monte Carlo methods (MCMC) Spatial statistics Stochastic geometry Stochastic simulation Point process modeling Professor Møller's recent publication record shows consistent productivity with 239 research outputs including journal articles, reports, and book chapters. His work demonstrates strong focus on spatial point processes, Bayesian inference methods, and applications of stochastic geometry. The research trends indicate increasing sophistication in modeling complex spatial patterns and developing computational methods for statistical inference. His scientific contributions have been supported by numerous research projects, with 28 projects documented including the current "Peculiar Distribution Functions and Interesting Stochastic Processes" (2022-2026). His work has generated significant scholarly impact with citations across multiple disciplines. Professor Møller has supervised 8 PhD students and maintains active collaborations across international research networks. His current projects suggest continued research activity in developing novel statistical methodologies for complex spatial data analysis with applications in materials science, neuroscience, and environmental statistics.

• Applied probability theory
• Markov chain Monte Carlo methods
• Spatial statistics
• +۴ مورد دیگر

Shuai Zhao is an Assistant Professor at the AAU Energy Department, Faculty of Engineering and Science, Aalborg University. His research focuses on applying machine learning and artificial intelligence techniques to enhance reliability and condition monitoring in power electronic systems, with specific interests in lifetime estimation, fault diagnosis, and health management of critical components like capacitors and semiconductor devices. Institution: Aalborg University School: Faculty of Engineering and Science Department: AAU Energy Email: szh@energy.aau.dk His research spans multiple domains including: Physics-informed machine learning for power converter systems Remaining useful life prediction with hybrid Bayesian deep learning Thermal transient analysis and stress emulation methods IoT-enabled monitoring schemes for semiconductor devices Neural network applications in lithium-ion battery prognostics Recent publications show a strong trend toward integrating domain-specific physics with machine learning frameworks to address real-world challenges in: Power electronics reliability under operational stress Anomaly detection in multivariate time-series data Robust fault diagnosis for railway traction systems Temperature estimation in electric vehicle motors Imbalanced data handling in diagnostic systems Capacitance degradation modeling under environmental factors Current projects demonstrate collaboration with leading institutions on: AI-assisted long-term maintenance strategies Physics-informed neural network architectures Smart agricultural monitoring systems via IoT platforms Advanced particle filter methods for life prediction

• Power Electronics
• Reliability Engineering
• Machine Learning
• +۴ مورد دیگر

Torben Hansen is a Professor at the University of Copenhagen's Faculty of Health and Medical Sciences, leading the Hansen Group at the Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR). His research focuses on genetic and molecular mechanisms underlying metabolic diseases, particularly type 2 diabetes and obesity. Professor Hansen's research encompasses several critical areas: Genetic determinants of type 2 diabetes and metabolic traits Genome-wide association studies of metabolic phenotypes Statistical methods for multi-omics data integration Early life determinants of metabolic disease risk Cardiometabolic complications in diabetes With 779 research outputs, his work demonstrates significant contributions to metabolic disease research. Recent publications highlight advanced computational approaches to multi-omics datasets for identifying causal relationships in type 2 diabetes pathogenesis. His research group actively participates in major international collaborations including the IMI DIRECT consortium, which integrates diverse data types for diabetes research advancement. Professor Hansen's work has substantial translational potential with implications for prevention strategies and therapeutic approaches for metabolic disorders. His publications in high-impact journals including PLOS Genetics, Nature Microbiology, Diabetologia, and the Journal of Clinical Endocrinology and Metabolism reflect the significance and quality of his research contributions.

• Genetics of diabetes
• Metabolic disorders
• Obesity research
• +۴ 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.

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

Henrik Madsen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His work focuses on energy systems, stochastic processes, and mathematical modeling. He leads research in areas such as demand response in district heating, probabilistic forecasting, and integration of renewable energy sources. Madsen has supervised multiple PhD students and contributes to projects like the IEA DHC Annex TS9 and SEEDS initiative. His expertise includes statistical analysis, time series modeling, and data-driven approaches for energy systems. He actively engages in collaborative research on smart grids, thermal energy storage, and sustainable energy solutions. Education: Academic qualifications from DTU, though specific details are not provided in the text. Research Interests: Mathematical modeling of energy systems Probabilistic forecasting methodologies Integration of wind and solar power Dynamic modeling for district heating Data assimilation in hydrological systems Optimization of energy flexibility Publications & Trends: Recent works emphasize demand response strategies, district heating optimization, and machine learning tools like the nabqr Python package and evalprob4cast R package. His research bridges theoretical stochastic methods with practical applications in energy infrastructure. Grants & Projects: IEA DHC Annex TS9: Digitalization of district heating SEEDS: RES-integrated electrified heating systems Data-Driven Methods for Demand-Side Flexibility Labs/Teams: Collaborates with DTU's Dynamical Systems group and participates in interdisciplinary initiatives like the Frigg 2.0 energy system analysis framework.

• Energy Systems
• Stochastic Processes
• Demand Response
• +۷ مورد دیگر

Ulrik Dam Nielsen is an Associate Professor in the Section for Fluid Mechanics, Coastal and Maritime Engineering at the Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU). He also held an external position as Associate Professor II at the Norwegian University of Science and Technology (NTNU) from 2014 to 2023, reflecting strong international collaboration. His work contributes to UN Sustainable Development Goals related to sustainable maritime operations and clean energy. His research focuses on naval architecture and ship motion dynamics , particularly in the context of sea state estimation , added resistance in waves , and real-time prediction of vessel responses . He integrates data analytics , estimation theory , and machine learning to develop methods for monitoring hydrodynamic performance and enhancing maritime safety and energy efficiency. A central theme of his work is using ships as mobile wave sensors—transforming operational vessels into 'sailing wave buoys' for environmental monitoring. His recent publications show a clear shift toward data-driven methodologies, especially machine learning applications in sea state estimation, added resistance modeling, and performance monitoring. These works span journals like Ship Technology Research and Journal of Offshore Mechanics and Arctic Engineering , and conferences such as IEEE MetroSea, highlighting interdisciplinary innovation at the intersection of classical marine engineering and modern AI. Best Paper Presented by a Young Researcher Award (First Classified), 2024 (jointly awarded) He actively supervises PhD students—such as R. E. G. Mounet, M. Mittendorf, J. P. Tomy, and A. Oikonomakis—on projects funded by DTU and collaborative initiatives. His leadership in projects like WEFOSWAB (Wave Estimation and Forecasting Using Ships as Buoys) and data-driven added resistance modeling underscores his role in advancing smart maritime technologies. He also contributes to open science through the public release of datasets such as NetSSE . He teaches core courses including Introduction to Ships and Floating Structures , Marine and Ocean Engineering , and Ship Operations , shaping the next generation of maritime engineers.

• Naval Architecture
• Ship Motion Dynamics
• Sea State Estimation
• +۷ مورد دیگر

Søren Rosendahl is a Professor in Ecology and Evolution at the Department of Biology, Faculty of Science, University of Copenhagen. He holds a PhD and has been recognized as a Knight (Ridder af Dannebrog) for his contributions to science. His academic career spans over three decades at the University of Copenhagen, where he served as Assistant Professor (1991), Associate Professor (1994), and Professor (2000) in the Department of Mycology, which later became part of the Department of Biology. From 2010-2019, he served as Deputy Head of the Department of Biology. Education: 1984: Master of Science (Cand.scient.) in biology, University of Copenhagen 1985: Practical and theoretical training as high school teacher in biology 1989: Ph.D. (Lic.scient) at Institut for Sporeplanter Professor Rosendahl specializes in molecular ecology, population genetics, and evolution of fungi, with particular emphasis on biotrophic symbionts and parasites. His research encompasses Fusarium, Penicillium, Oomycota (Peronosporamycota), arbuscular mycorrhizal fungi (AMF) in Glomales, and fungi associated with insects. He employs advanced genomic techniques including rad-sequencing, microsatellites, SNPs, and DNA sequencing to study fungal diversity and distribution patterns. His BioMark project applies population genomics to predict emerging epidemics of Phytophthora infestans (Potato late blight). He also investigates molecular diversity of AMF fungi in agricultural and natural environments using evolutionary and demographic models based on coalescence and MCMC Bayesian statistics. Analysis of Professor Rosendahl's recent publications reveals a strong interdisciplinary focus spanning fungal ecology, medical microbiology, and agricultural science. His work demonstrates consistent application of molecular genetic approaches to understanding fungal community dynamics across diverse ecosystems - from soil microbial communities to human gut microbiomes. The trend shows increasing integration of computational approaches including hierarchical joint species modeling and co-occurrence networks for microbial community data analysis. His research bridges fundamental fungal biology with practical applications in agriculture, medicine, and environmental science. Scientific Recognition: Knight (Ridder af Dannebrog) Throughout his career, Professor Rosendahl has held numerous leadership and advisory positions. He served as Head of the Department of Mycology (1998-2001), Coordinator for the Bachelor degree in Industrial Science (2002-2005), and Head of the Study Board for Biology (2005-2007). He has represented Danish higher education on the Biology/Biotechnology board in the Ministry of Education since 2010 and has consulted for international organizations including the EC Commission, Norwegian Ministry for Education, and European Baccalaureate Unit. His external collaborations include significant work with Novozymes Biologicals and Thomsen BioScience A/S. Professor Rosendahl maintains active research collaborations through his membership in the European culture collection (BEG - Bank of European Glomales) and has served as Danish representative in multiple COST actions. His research group appears to focus on fungal population genetics with applications spanning agricultural systems, natural ecosystems, and medical contexts.

• Molecular Ecology
• Population Genetics
• Evolution of Fungi
• +۴ مورد دیگر

Poul G. Hjorth is an Associate Professor in the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark (DTU). He specializes in dynamical systems, mathematical modeling, and industrial applied mathematics, with strong interdisciplinary ties to neuroscience and industrial problem-solving. He has held visiting positions at the University of Southern Denmark and maintains affiliations with the University of Copenhagen as an External Lecturer. Ph.D. in Mathematical Physics, University of California, San Diego (1988) cand.scient in Mathematics, University of Copenhagen (1982) His research focuses on mathematical physics, classical mechanics, dynamical systems, and geometry , particularly in real-world industrial applications. He has been instrumental in organizing the European Study Groups with Industry (ESGI) in Denmark and is Executive Director of the European Consortium for Mathematics in Industry (ECMI). His recent work includes modeling the glymphatic system and cerebrospinal fluid dynamics in the brain, contributing to neuroscience and medical research. The most recent publications reflect a shift toward interdisciplinary applications, especially in neuroscience, fluid dynamics, and environmental modeling , while maintaining a strong foundation in pure and applied mathematics. Themes include brain efflux, phytoplankton dynamics, and philosophical explorations of probability and cosmology. DTU Teaching Award (2010) Hjorth has supervised students and led multiple long-term research projects since the 1990s. He serves as Editor for the Danish Mathematical Society Newsletter Matilde and the Fields Institute journal Mathematics in Industry Case Studies . His collaborative network spans institutions in Denmark, Israel, the UK, and the USA, reflecting his international engagement in both research and academic service.

• Dynamical Systems
• Mathematical Modelling
• Industrial Applied Mathematics
• +۶ مورد دیگر

Michael Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. His primary research focuses on statistical inference for stochastic processes, particularly stochastic differential equations and jump processes, with applications in finance, physics (e.g., wind-blown sand dynamics), and biology. He has authored/co-authored influential books such as Exponential Families of Stochastic Processes and edited volumes on empirical process techniques and statistical methods for stochastic differential equations. His work bridges theoretical statistics with applied problems in natural sciences and finance. Research interests include modeling turbulence, sand transport dynamics, and protein structure evolution. Collaborations with earth scientists like Keld Rømer Rasmussen have advanced understanding of aeolian processes. His methodologies emphasize likelihood-based inference and estimating functions, with contributions to high-frequency data analysis and diffusion bridge simulations. A comprehensive CV and full publication list are available on his profile. Key contributions span stochastic modeling in physics (e.g., sand dune dynamics), financial econometrics, and computational statistics. He has pioneered techniques for analyzing multi-modal diffusions and developed frameworks for mixed-effects stochastic differential equations. His work is widely cited in both theoretical and applied statistical literature.

• Statistical Inference for Stochastic Processes
• Stochastic Differential Equations
• Financial Mathematics
• +۳ مورد دیگر

Jørgen Beck Hansen is an Associate Professor at the Niels Bohr Institute , University of Copenhagen, specializing in Experimental Subatomic Physics . His career spans roles at CERN and NBI, focusing on particle physics detectors, high-energy collisions, and computational methods. Education: Ph.D. in Particle Physics (1996) and M.Sc. in Physics (1993) from the University of Copenhagen. Research interests include two-boson physics at the ATLAS detector, precision measurements within the Standard Model, effective Lagrangian densities, and searches for new physics beyond the Standard Model (e.g., Higgsless theories, extra dimensions). He also works on GRID computing and distributed data analysis. Recent trends in his research involve Higgs boson studies, vectorlike top quarks, photonuclear collisions, and detector trigger optimization. Collaborative efforts with the ATLAS Collaboration and Danish NORDUGRID team highlight his interdisciplinary approach. Scientific awards : Skou Stipend (Assistant Professor) from the Danish Natural Science Council Teaching and supervision include mentoring 5 summer students, 2 Ph.D., 4 Master's, and 10 Bachelor's students. He has contributed to popular science through Danish Cosmic Rays at Schools and public lectures. Labs and teams : Actively involved in the ATLAS Collaboration and the Danish NORDUGRID team for distributed computing infrastructure.

• Particle physics
• High-energy physics
• Beyond Standard Model
• +۷ مورد دیگر

Efren Fernandez Grande is an Associate Professor at the Technical University of Denmark , specializing in Acoustic Technology within the Department of Electrical and Photonics Engineering. His research focuses on advanced acoustic modeling and signal processing techniques. Key Research Areas: Sound Field Engineering, Room Acoustics, Acoustic Holography, Beamforming, Neural Networks for Acoustic Modeling Recent publications highlight his work on sound field reconstruction, acoustic rainbows, and physics-informed neural networks, emphasizing spatial and frequency limitations in acoustic modeling. He supervises multiple PhD students in projects related to noise control and acoustic space reproduction. His contact details include efgr@dtu.dk and links to his ORCID and research website .

• Sound Field Engineering
• Acoustic Field Engineering
• Microphone Array Engineering
• +۷ مورد دیگر

Andreas Asp Bock is a postdoctoral researcher at the Department of Applied Mathematics and Computer Science within the Technical University of Denmark . His work focuses on scientific computing and numerical linear algebra, with particular emphasis on matrix approximation techniques, preconditioning strategies, and optimization methods. Current affiliation: Technical University of Denmark (College of Engineering) Academic role: Researcher (postdoctoral level) Research interests include: Matrix factorization and truncation Bregman divergence applications Baysian inversion frameworks Curve registration algorithms High-dimensional data analysis Preconditioning for iterative solvers Recent publications demonstrate expertise in improving approximate factorization preconditioners, geometric curve registration, and divergence-based preconditioner design. Collaborations with Martin S. Andersen and others indicate ongoing contributions to sparse linear algebra and computational statistics. Supervision : Currently mentoring J. V. Galvão da Mata in a PhD project focused on Optimization methods for data-sparse models , active from 2022-2025.

• Scientific Computing
• Numerical Linear Algebra
• Bayesian Inversion
• +۳ مورد دیگر

Leon Eyrich Jessen is an Associate Professor and Groupleader at the Department of Health Technology, Technical University of Denmark (DTU). He leads the Computational Autoimmunity research group, focusing on computational immunology with a particular emphasis on understanding disruptions in central tolerance and the mechanisms that allow the immune system to differentiate between self and non-self. His work aims to uncover immuno-cellular processes associated with autoimmune diseases through single-cell technology and machine learning-based bioinformatics tools. Dr. Jessen's research spans several key areas including Bioinformatics, Immunology, Machine Learning/AI, Data Science, and T-cell Biology. His work specifically addresses Computational Autoimmunity, Bayesian Modeling, and Molecular Evolution. He employs single-cell technology to investigate autoimmune disease mechanisms, with applications in psoriatic arthritis, diabetes, and other autoimmune conditions. His group develops computational methods for T cell receptor analysis, epitope prediction, and spatial transcriptomics. His recent publications demonstrate a strong focus on computational immunology, with key contributions in single-cell RNA sequencing, T cell receptor analysis, and spatial transcriptomics. His work bridges computational methods with immunological applications, particularly in autoimmune disease research. The research shows a consistent pattern of developing and applying machine learning algorithms to complex immunological data, with significant contributions to understanding T cell biology and autoimmune mechanisms. Dr. Jessen actively supervises PhD students including Drachmann, C., Montemurro, A., Povlsen, H. R., and Schaap-Johansen, A.-L. on projects related to single-cell analysis, immunoinformatics, and deep learning methods for immunotherapy. He is also the creator and instructor of "R for Bio Data Science," which is the third largest course at his department, demonstrating his commitment to education and training the next generation of bioinformaticians. He leads the Computational Autoimmunity group which focuses on developing, applying, and serving machine learning-based bioinformatics tools. The group works at the intersection of computational methods and immunological research, with particular emphasis on single-cell technologies and their application to autoimmune disease mechanisms.

• Bioinformatics
• Immunology
• Machine Learning
• +۶ مورد دیگر

Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +۵ مورد دیگر

Svante Eriksen is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His research spans statistics, forensic genetics, and computational modeling, with a focus on Bayesian networks, graphical models, and statistical methods for forensic DNA analysis. He is actively involved in interdisciplinary research and software development for probabilistic genotyping and large-scale inference. Research Interests: Bayesian Networks and Graphical Models Statistical Methods in Forensic Genetics SNP and Y-STR Genotyping Data Mining and Knowledge Discovery Model Selection and Context-Specific Independence Software Development for Statistical Inference Recent Publication Trends (2024–2025): His recent work focuses on improving SNP genotyping accuracy using logistic regression models, developing efficient software (jti and sparta) for Bayesian network inference, and advancing forensic DNA analysis through dynamic SNP selection and probabilistic modeling of Y-STR databases. These contributions reflect a strong integration of statistical theory, computational efficiency, and real-world forensic applications. Scientific Contributions: Principal contributor to software packages for Bayesian network prediction. Developer of statistical models for forensic DNA data interpretation. Collaborator on projects involving digital learning analytics and student retention. Advising and Grants: While specific student names are not listed, the profile indicates involvement in PhD supervision (4 cases). He has participated in multiple externally funded research projects, including those supported by Novo Nordisk and Danish research councils, focusing on forensic DNA analysis, graphical models, and educational data mining. Research Groups and Collaborations: He is part of a strong research network in forensic genetics and statistical modeling at Aalborg University, collaborating with leading researchers such as N. Morling, M. M. Andersen, and T. Tvedebrink. His work is closely tied to the development and application of statistical software in both forensic and educational domains.

• Statistics
• Bayesian Networks
• Graphical Models
• +۷ مورد دیگر