جست‌وجوی استاد و پژوهشگر

Hao Liمشاهده پروفایل

پژوهشگر ارشد

• Topology Optimization
• Mechanical Engineering
• Thermal Systems
• +۲ مورد دیگر

Hao Li is a MSCA Postdoc Fellow and Visiting Scholar at LIP6 , affiliated with the University of Southern Denmark in the Department of Mechanical Engineering . His research focuses on advanced computational methods for topology optimization in thermal, fluid, and structural engineering systems. Education: Not explicitly stated in the provided text. Current Projects: Leading EU-funded research on heat exchanger design using multiscale models and machine learning. Dr. Li's work spans multiscale topology optimization, level-set methods, and fluid-structure interaction, with applications in microchannel cooling, compliant mechanisms, and biodegradable composites. His recent publications highlight advancements in 3D conjugate heat transfer, adaptive meshing, and eigenfrequency maximization. The trends in his research output (2017–2025) emphasize thermal-fluid systems , high-resolution structural optimization , and manufacturable composite designs . Notable subfields include triply periodic minimal surfaces for cooling channels, nonlinear buckling analysis, and phasor-based dehomogenization techniques. Teaching & Supervision: Currently supervising projects on topology optimization frameworks for heat sinks and high heat flux cooling. His past projects (2018–2023) include research on piezoelectric transducers and thermal-fluid system design. Labs & Collaborations: Collaborates with institutions in Japan and France, focusing on experimental validation and industrial applications. His network includes partnerships with researchers in structural mechanics, computational fluid dynamics, and additive manufacturing.

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.

• Probabilistic Graphical Models
• Machine Learning
• Deep Generative Models
• +۷ مورد دیگر

Thomas Dyhre Nielsen is a Professor (MSO) in the Department of Computer Science at Aalborg University . He is a member of the Distributed, Embedded and Intelligent Systems (DEIS) research group and contributes to the Artificial Intelligence and Machine Learning team at the university. His primary research interests lie in the theoretical and applied aspects of probabilistic graphical models , machine learning , and deep generative models . His work spans from foundational methods for learning and inference to the development of frameworks for solving complex decision problems. He is the co-author of the authoritative textbook Bayesian Networks and Decision Graphs . His recent research output reveals a strong trend towards interdisciplinary applications. His 15 most recent publications demonstrate significant work in bioinformatics , using graph neural networks and variational autoencoders for metagenomic binning, and in urban infrastructure , applying reinforcement learning and probabilistic models to optimize stormwater and wastewater management systems. He also has impactful research in healthcare , developing Bayesian network models for clinical risk prediction. Senior area editor for the International Journal of Approximate Reasoning . Principal or Co-Investigator on research projects such as the AMIDST project (developing a Java toolbox for scalable probabilistic machine learning) and PGM 2020 (organizing the International Conference on Probabilistic Graphical Models). Actively supervising research, as evidenced by a current opening for a PostDoc position in probabilistic machine learning. Professor Nielsen leads a research lab focused on probabilistic machine learning, which is part of the larger DEIS and AI/ML teams at Aalborg University. His group develops and applies advanced models to real-world problems in environmental science, healthcare, and intelligent systems.

Heidi Hautoppمشاهده پروفایل

دانشیار

• Visual Methods
• Design Approaches
• Hybrid Learning Formats
• +۵ مورد دیگر

Heidi Hautopp is an Associate Professor at the Department of Culture and Learning, Faculty of Humanities and Social Sciences, Aalborg University, Copenhagen. Her work integrates visual methods and design approaches in higher education and organizational contexts, with a focus on hybrid teaching formats through the Nordic Visual Studies and Art Education (NoVA) master's program. PhD from Aalborg University (2018-2022) on graphic facilitation Teaches IT, learning and organizational change (ILOO), Learning and change processes (LFP), and Communication and digital media programs Co-founder of the Danish Play Think Tank promoting creative approaches in education Her research examines how visual tools like graphic facilitation and video prototyping can drive educational innovation. Recent work explores AI image generation in design and hybrid PBL models for arts education. Publications span both empirical studies and methodological frameworks. Principal investigator in projects like Emergency AI and Sustainable Development Lab Active in conferences on networked learning and arts-based research Collaborates across disciplines including organizational development and sustainability

Kristian G. Olesenمشاهده پروفایل

دانشیار

• Bayesian Networks
• Artificial Intelligence in Medicine
• Medical Decision Making
• +۳ مورد دیگر

Kristian G. Olesen is an Associate Professor in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. His work focuses on probabilistic graphical models, medical decision support systems, and Bayesian network applications in healthcare. Research Interests : Olesen specializes in Bayesian networks and their applications in medicine, including temporal reasoning for diagnoses (e.g., Lyme disease), home care delivery optimization, and emergency department patient flow prediction. His methodologies emphasize online updating algorithms and modularization of complex tasks. Professional Activities : He serves on the board of Hugin Expert A/S since 1989 and Folkeuniversitetet i Aalborg from 2018–2022. His projects include collaborations on medical decision support systems and normative systems with Hewlett Packard (1998–2003). Media Engagement : Olesen has been featured in media discussions on AI ethics (e.g., robot regulation in 2016) and institutional achievements, including the recognition of Aalborg University’s computer science institute as world-class in 2017.

Anders Nymark Christensenمشاهده پروفایل

دانشیار

• Image Analysis
• Computer Vision
• Medical Imaging
• +۷ مورد دیگر

Anders Nymark Christensen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Visual Computing. His work bridges computer vision, medical imaging, and materials science, with a strong focus on explainable AI in clinical applications and structural analysis of complex materials. Institution: Technical University of Denmark (DTU) School: Department of Applied Mathematics and Computer Science Department: Visual Computing Role: Associate Professor Christensen’s research interests include image analysis, computer vision, explainable AI, ultrasound imaging, fetal medicine, and fiber orientation in composites. He applies advanced computational techniques to solve real-world problems in healthcare and materials engineering, particularly through structure tensor analysis and deep learning. His recent publications highlight a strong trend in developing AI tools for clinical decision support in fetal medicine, structural analysis of food and composite materials, and ultrasound imaging. These works span journals such as Scientific Reports , Food Structure , and IEEE Access , reflecting interdisciplinary innovation. Clinical validation of explainable AI for fetal growth scans Characterization of anisotropy in mozzarella cheese Determining fetal orientations from ultrasound video Structure tensor analysis in composites He supervises several PhD students in projects related to AI in skin lesion analysis, cancer detection, and ultrasound imaging. His collaborative network includes key researchers at DTU such as Anders Bjorholm Dahl, Vedrana Andersen Dahl, and Mads Nielsen. He has been involved in significant research grants and is active in both medical and engineering domains, contributing to open datasets and reproducible research. Christensen leads and contributes to numerous active projects, including: Decision Support AI for Skin Lesions (2024–2027) Fighting Cancer with Generative AI (2024–2027) SONAI: Explainable AI in the Clinic (2022–2026) Deep learning for identifying biomarkers in medical images (2022–2025) His work supports UN Sustainable Development Goals related to health, innovation, and responsible consumption, particularly through advancements in medical diagnostics and sustainable materials.

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.

Vedrana Andersen Dahlمشاهده پروفایل

دانشیار

• Geometry Processing
• Image Analysis
• Computer Vision
• +۳ مورد دیگر

Vedrana Andersen Dahl is an Associate Professor at the Technical University of Denmark (DTU), Department of Applied Mathematics and Computer Science. Her research focuses on image analysis and geometry processing, including geometric texture analysis, deformable models, and tomographic data processing. She holds a PhD in Image Analysis and Computer Graphics from DTU (2007–2011), an MSc in IT from the IT University of Copenhagen (2005–2007), and a Diploma in Mathematics and Physics from the University of Zagreb (1992–1999). PhD: Technical University of Denmark (2007–2011) MSc: IT University of Copenhagen (2005–2007) Diploma: University of Zagreb (1992–1999) Her research interests span geometric texture analysis, deformable models, OCT data processing, and medical imaging applications. She has contributed to projects such as the Villum Experiment Grant (2022) and collaborates internationally on topics like placental tomography and fibrous composite analysis. Dahl supervises multiple PhD students in areas like medical image segmentation and X-ray imaging techniques. She has authored over 100 publications, with recent work focusing on 3D tomography, medical imaging, and machine learning applications. Her datasets, including BugNIST and STXAE, are widely used in academic and industrial research. Dahl also serves on review committees at institutions like the University of Copenhagen and University College Copenhagen.

Christoph Seidlمشاهده پروفایل

دانشیار

• Software Engineering
• Virtual Reality
• Software Visualization
• +۵ مورد دیگر

Christoph Seidl is an Associate Professor in the Department of Software Engineering at the IT University of Copenhagen. His research focuses on software visualization, software archaeology, and the application of virtual reality in software engineering tasks. He leads the Immersive Software Archaeology project, exploring collaborative systems and legacy system analysis in virtual environments. Key research areas include software product lines, feature modeling, and model-driven development. Seidl has received awards such as the SoSyM Best Reviewer Award (2018) and SPLC'19 Distinguished Reviewer Award (2019). His work emphasizes innovative tools for software exploration, including immersive 3D visualization and collaborative note-taking systems in VR. He has published extensively on topics like variability management, configuration testing, and domain-specific languages. Research trends in his articles highlight advancements in using VR for software archaeology, collaborative software exploration, and methodologies for managing software evolution. His projects address challenges in legacy system understanding, feature model synthesis, and automated testing for configurable systems. Seidl collaborates internationally, with projects funded by the Independent Research Fund Denmark. His contributions span academic publications, datasets (e.g., VaMoS 2022 artifacts), and tool development for software product line engineering.

• Data Science
• Machine Learning
• Forensic Science
• +۳ مورد دیگر

Therese Graversen is an Associate Professor of Data Science and Head of the BSc in Data Science Programme at the IT University of Copenhagen. Her research focuses on forensic science , DNA analysis , and computing education . She leads two major research projects funded by the Independent Research Fund Denmark: Statistical and Computational Aspects of Forensic Identification using DNA (2019–2020) A Unified Statistical Framework for Complex Forensic DNA Evidence (2022–2027) Her work bridges Bayesian networks , graphical models , and educational strategies , with notable contributions to improving gender representation in computing education. Recent collaborations include predictive maintenance in pharmaceutical industries and analysis of high-profile criminal cases via advanced DNA methodologies. Media engagements highlight her role as an expert in forensic DNA advancements.

Mark Philip Philipsenمشاهده پروفایل

استادیار

• Computer Vision
• Robotics
• Machine Learning
• +۳ مورد دیگر

Mark Philip Philipsen is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University. He holds a PhD (ErhvervsPhD) from 2017 to 2019. His research focuses on computer vision, robotics, and machine learning applications in environmental science, infrastructure, and industrial automation. He leads projects such as ReDoCO2 (2020–2025) addressing peatland CO2 emissions and Applications of Vision and Robotics in Meat Production (2017–2020), exploring automation in food processing. His work spans diverse areas, including semantic segmentation of underground utilities using 3D point clouds, peat mapping with graph neural networks, and anomaly detection in agricultural vehicles. Philipsen has contributed datasets like OpenTrench3D and LISA Traffic Light Dataset, advancing research in infrastructure analysis and vision systems. His publications emphasize practical applications of AI in environmental monitoring, robotics, and data-driven solutions for industry challenges. Key achievements include developing synthetic thermal human scenarios (ThermalSynth) and semi-supervised learning models for sewer systems. His research often bridges theory and real-world problems, with a focus on sustainability and automation.

Neelu Madanمشاهده پروفایل

پژوهشگر ارشد

• Anomaly Detection
• Deep Learning
• Computer Vision
• +۴ مورد دیگر

Neelu Madan is a Postdoctoral Researcher at the Department of Architecture, Design and Media Technology, Section for Media Technology, within The Technical Faculty of IT and Design at Aalborg University, Denmark. Her research focuses on computer vision and machine learning with applications in video surveillance and anomaly detection. Dr. Madan completed her PhD in Computer Science at Aalborg University in 2023. Her doctoral thesis, "Towards Limited Label Learning for Visual Surveillance", established her expertise in limited-label learning paradigms for surveillance systems. Her research spans anomaly detection , deep learning , and computer vision , with significant contributions in self-supervised learning for video analysis. She specializes in transformer architectures, masked autoencoders, and synthetic data generation, addressing challenges in multi-object tracking and event detection under limited supervision. Recent publications (2023-2024) reveal a strong trend toward foundation models for video understanding and self-supervised anomaly detection. Her work bridges theoretical advances in representation learning with practical applications in thermal imaging, fashion technology, and harbor safety systems. Scientific recognition includes: Spar Nord Fondens Forskningspris (2024) Dr. Madan is actively involved in externally funded research as Co-Investigator on three major projects: AICF: AI Color Fashion (2023-2026), Generic Object Tracking (2020), and Self-Supervised Learning For Video Anomaly Detection (2020-2023). While no formal advisees are documented, her postdoctoral role likely involves mentoring junior researchers. She operates within Aalborg University's Visual Analysis and Perception research group, maintaining strong industry collaborations with Milestone Systems on video surveillance applications and harbor safety technology.

Jevgenijs Ivanovsمشاهده پروفایل

دانشیار

• Stochastic Processes
• Probability Theory
• Mathematical Statistics
• +۳ مورد دیگر

Jevgenijs Ivanovs is an Associate Professor in the Department of Mathematics at Aarhus University . His research focuses on stochastic processes , particularly Lévy processes , extreme value theory , and probability theory , with applications in queueing systems , statistical inference , and multivariate extremes .

Samir Bhattمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Public Health
• +۵ مورد دیگر

Samir Bhatt is a Professor of Machine Learning and Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences, Department of Public Health, Section for Health Data Science and AI. He also holds a position as Professor of Statistics and Public Health at Imperial College London since 2016. His work focuses on developing mathematical, statistical, and computer science tools to address critical questions in human health. His educational background includes a DPhil in Statistical Genetics from the University of Oxford (2010), an MPhil in Computational Biology from the University of Cambridge (2006), and a BEng in Chemical and Bioprocess Engineering from the University of Bath (2005). Professor Bhatt's research spans the intersection of statistics, machine learning, and public health with particular emphasis on infectious disease modeling. His primary research areas include Bayesian inference, genomic epidemiology, and kernel methods applied to health data. His work bridges theoretical statistical approaches with practical public health applications, particularly in disease surveillance and outbreak response. The integration of AI with traditional epidemiological methods represents a key innovation in his research program. Analysis of his recent publications reveals a strong focus on applying advanced computational methods to pressing public health challenges. His work demonstrates consistent innovation in developing AI-driven approaches for infectious disease modeling, genomic surveillance, and survival analysis. Notable themes include the application of graph neural networks to epidemiological data, development of interpretable AI tools for public health decision-making, and sophisticated modeling of disease transmission dynamics across multiple pathogens including malaria, cholera, and respiratory viruses. Professor Bhatt has published extensively with over 107 research outputs to date. His work has received significant attention, with multiple publications covered by news outlets, referenced on social media platforms, and read by researchers on academic platforms like Mendeley. His research on AI for infectious disease modeling published in Nature demonstrates the high impact of his work in both academic and policy spheres. His research group at the University of Copenhagen appears to focus on developing computational tools for health data science, with particular emphasis on creating analytical frameworks that can be rapidly deployed during disease outbreaks. The development of GRAPEVNE (Graphical Analytical Pipeline Development Environment for Infectious Diseases) represents one such effort to create accessible tools for public health practitioners.

Morten Rieger Hannemoseمشاهده پروفایل

استادیار

• Computer Vision
• Medical Imaging
• 3D Reconstruction
• +۳ مورد دیگر

Morten Rieger Hannemose is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computer vision, medical imaging, 3D reconstruction, and AI-driven healthcare solutions. He is actively involved in projects like 'Fighting Cancer with Generative AI' and 'Decision support AI for Skin Lesions,' supervising multiple PhD students in these domains. His work contributes to UN Sustainable Development Goals related to health and innovation. Research interests include neural networks, medical image analysis, and computational imaging. Notable achievements include developing methods for digitizing material appearances and estimating diagnostic difficulty in skin lesion diagnosis. He collaborates internationally on topics such as monocular 3D pose estimation and multimodal data fusion. Morten has supervised PhD students in areas like crowd counting through remote sensing, generative AI for cancer diagnostics, and diffusion models for image segmentation. His projects often bridge theory and application, such as creating the 'Sportspose' 3D sports pose dataset. His email is mohan@dtu.dk , and his website is mortenhannemose.github.io .