جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Quantum Machine Learning
• Neural Networks
• +۶ مورد دیگر

Sebastian Bugge Loeschcke is a PhD Fellow at the Machine Learning Section of the Department of Computer Science (DIKU), University of Copenhagen . His research spans theoretical and applied machine learning with focus on quantum machine learning, language modeling, and sustainability. Current affiliation: Machine Learning Section, DIKU Key research areas: Quantum-classical hybrid models, neural language processing, geospatial analysis Collaborative initiatives: SCIENCE AI Centre, TreeSense Centre Loeschcke's recent work includes Coarse-To-Fine Tensor Trains for compact representations and LoQT: Low-Rank Adapters for Quantized Pretraining , reflecting his focus on efficient neural architectures and quantum-inspired methods. His publications address cross-disciplinary challenges in climate modeling, healthcare, and quantum computing. Scientific contributions include: 2024: Tensor train compression methods for visual representations 2024: Low-rank adapter techniques for quantized models 2025: Quantum computing applications in molecular binding energy calculation 2025: Ethical frameworks for sustainable AI development 2025: Quantum dot array simulation tools (QDarts) Loeschcke contributes to interdisciplinary projects involving: TreeSense (remote sensing of global tree resources) Quantum computing optimization with Danish research consortia

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +۳ مورد دیگر

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

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.

• Statistical Modeling
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Katrine Harder Clemmensen is a Professor at the Department of Mathematical Sciences , University of Copenhagen . She specializes in statistical modeling , machine learning , and statistical evaluation of AI systems , with particular emphasis on low-resource domains , explainable modeling , and fair health/life science applications . External Employment: Co-founder & Chief Science Officer at Interhuman AI Research Trends (2024-2025): Focus on health applications of ML/AI Developing fair/explainable algorithms for clinical use Interdisciplinary work in psychiatry , microbiome analysis , and neuroimaging Methodological contributions to clustering , self-explainable models , and emotion recognition Selected Publications (2025): Gene co-expression networks in scRNAseq data Computational psychiatry applications in OCD Remote sensing for crop health microbiome studies Neurostimulation techniques beyond RGB light LLM bias analysis in STEM education

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۳ مورد دیگر

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.

Eleni Tzirita Zacharatouمشاهده پروفایل

استادیار

• Spatial Data Management
• Geospatial Analytics
• Query Processing
• +۱ مورد دیگر

Eleni Tzirita Zacharatou is Assistant Professor in the Department of Computer Science at the IT University of Copenhagen. Her research develops efficient data management tools for modern analytics workloads, with specialization in spatial data processing and geospatial applications. She leads projects on query optimization, spatial indexing, and distributed geocomputation. Her research focuses on improving the efficiency of spatial data analytics through novel indexing structures, approximation techniques, and hardware acceleration. Key areas include spatial join optimizations, geospatial stream processing, and scalable earth observation data systems. She develops specialized algorithms for polygon processing, raster operations, and spatiotemporal indexing. Dr. Zacharatou's publications demonstrate innovations in geospatial query processing, including raster-based approximations for spatial joins, distributed execution of zonal statistics, and FPGA-accelerated sketching techniques. Her work bridges database systems research with practical applications in earth observation, IoT monitoring, and transportation analytics. Awards & Recognition: Distinguished Reviewer (VLDB 2022) SIGMOD Best Demonstration Award (2018) She leads the GeoSpatial Systems Lab, developing open-source tools for processing massive geospatial datasets. Current projects include distributed spatial aggregation frameworks and learned indexing techniques for earth observation platforms.

Harrison Bo Hua Zhuمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Deep Learning
• Infectious Disease Modeling
• +۷ مورد دیگر

Harrison Bo Hua Zhu is an Assistant Professor at the Section for Health Data Science and AI within the Department of Public Health at the University of Copenhagen. He joined the university in November 2024 and is also a member of the Machine Learning and Global Health Network and part of the research group led by Samir Bhatt and David Duchêne. His educational background includes: PhD in Modern Statistics and Statistical Machine Learning from Imperial College London (2019-2023), supervised by Seth Flaxman and Yingzhen Li MSci in Mathematics from Imperial College London (2015-2019), with an exchange year at École Polytechnique Fédérale de Lausanne Dr. Zhu's research focuses on developing probabilistic machine learning methods for public health applications, particularly in infectious disease modeling and phylogenetics. His work bridges theoretical advances in Gaussian processes and Bayesian deep learning with practical epidemiological challenges. He specializes in creating scalable models that can handle multimodal data sources including time series, satellite imagery, and genomic sequences. His publication record shows a clear progression from foundational machine learning methodology to impactful public health applications, with significant contributions during the COVID-19 pandemic as part of the Imperial College London Response Team. Recent work demonstrates expertise in Markovian Gaussian Process Variational Autoencoders and multimodal learning approaches for health data science. Dr. Zhu actively supervises students at multiple levels: Current PhD students: Mathilde Marie Brünnich Sloth (life-course epidemiology) and Mariya Pavlova (Climate Change AI) Previous students: Alexander Pondaven (MEng thesis on diffusion models), Qing Pan (MSc on meta-learning), and Gengjian Hu (MSc on climate data compression) He maintains strong industry connections through past work at Fano (2023-2024) and an internship at Amazon, demonstrating the practical applicability of his research. His GitHub activity shows active development of machine learning tools, including implementations of neural processes and Gaussian process models.

• Machine Learning
• Quantum Computing
• AI Fairness
• +۲ مورد دیگر

Simon Krogh Anderson serves as a Lecturer at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. He is an active member of the Machine Learning section which focuses on theoretical foundations and applications across domains including natural language processing, medical image analysis, and biological data modeling. The department participates in the SCIENCE AI Centre and maintains powerful compute resources including the TreeSense platform for remote sensing. His research spans multiple cutting-edge areas in artificial intelligence with particular emphasis on machine learning, quantum computing applications, and algorithmic fairness. Key interests include sustainable AI development, reproducibility in recommender systems, and cross-cultural adaptation frameworks. His work often bridges theoretical computer science with practical applications in environmental monitoring, healthcare analytics, and quantum information processing. Recent publications demonstrate strong interdisciplinary connections across quantum computing, sustainable AI, and fairness metrics. Trends show increasing focus on environmentally conscious AI development, integration of quantum methods with classical machine learning, and ethical considerations in recommendation systems. His work frequently leverages Denmark's extensive health registries and environmental data resources. Anderson actively contributes to the department's research ecosystem through teaching and collaboration within the Machine Learning section. While specific grants aren't detailed in available materials, his publications indicate involvement in projects related to quantum computing infrastructure, environmental monitoring systems, and AI ethics frameworks. The department provides significant computational resources including a dedicated cluster and specialized labs like TreeSense for remote sensing applications. His work appears connected to the SCIENCE AI Centre's initiatives in sustainable computing and quantum information processing.

Niklas Elmqvistمشاهده پروفایل

استاد

• Data Visualization
• Immersive Analytics
• Human-Computer Interaction
• +۳ مورد دیگر

Niklas Elmqvist is a Professor in the Department of Computer Science at Aarhus University. His research focuses on immersive analytics, data visualization, and human-computer interaction, emphasizing user-centered design and interdisciplinary approaches. He leads projects on interactive visualization systems, collaborative platforms, and accessibility in data analysis. Key research areas include agentic visualization systems, attention-aware interfaces, and ubiquitous analytics environments. He explores novel interaction techniques, such as bimanual gestures and multimodal feedback, to enhance data exploration experiences. His work bridges computer science with domains like epidemiology, cybersecurity, and creative writing. Elmqvist has contributed to influential platforms like DashSpace (collaborative immersive analytics) and Datamancer (gesture-driven analytics). He investigates challenges in accessible visualization for visually impaired users and the use of large language models for automated design feedback. His recent work addresses human-centered AI integration in visualization tools and the ethics of automated decision-making systems. He collaborates internationally on projects such as Riverside (cybersecurity visualization) and Lodestar (data science workflow recommendations). His research has been published in top journals like IEEE Transactions on Visualization and Computer Graphics, emphasizing both theoretical advancements and practical system implementations.

• Satellite Remote Sensing
• Geographic Information Systems (GIS)
• Urban Studies
• +۴ مورد دیگر

Thomas Theis Nielsen is an Associate Professor at the Department of People and Technology, Roskilde University. His work focuses on satellite remote sensing and GIS applications, contributing to UN Sustainable Development Goals. He has participated in 13 research projects including the Femern Fyrtårn infrastructure impact study and the SEAFACTS maritime history platform. His research explores rural migration patterns, sustainable tourism in nature parks, and socio-economic impacts of large infrastructure projects. Education & Professional Background: While specific academic credentials aren’t detailed, his extensive publication record (64+ outputs) and project leadership indicate advanced training in geography and spatial sciences. Research Interests: Combines technical expertise (remote sensing, GIS) with socio-spatial analysis. Key themes include: Quantitative migration modeling Infrastructure development impacts Sustainable tourism governance Crowdsourced spatial data applications GIS-based education innovation Awards: Won RUC's 2018 Teaching Prize for educational contributions. Grants & Activities: Manages projects like Urban Tech Lab (Carlsberg Foundation) and Innotech TaskForce. Engages in 45+ activities including consultancy on urban housing trends and workshops on historical GIS. Labs/Teams: Collaborates with the Centre for Maritime and Marine Research and leads the MOSPUS research group.

Marina Georgatiمشاهده پروفایل

استادیار

• Sustainable Energy Planning
• Spatial Analysis
• Urban Demography
• +۴ مورد دیگر

Marina Georgati is an Assistant Professor at Aalborg University, affiliated with the Technical Faculty of IT and Design and the Department of Sustainability and Planning . Her research focuses on sustainable energy systems, spatial analysis, and migration patterns, with a strong emphasis on machine learning applications in urban demography and environmental planning. She leads the Sustainable Energy Planning Research Group and collaborates on interdisciplinary projects such as the Heat Roadmap Europe 5 and iDesignRES initiatives. Key Projects: iDesignRES: Open-source tools for renewable energy system design HRE5: Strategic heat planning across Europe FUME: Migration scenarios analysis for Europe BalticRIM: Maritime cultural heritage management Research Interests: Georgati’s work bridges energy sustainability, urban demography, and computational geography. She develops machine learning models for spatial disaggregation of population data, analyzes migration dynamics using gridded datasets, and evaluates heat potential in district heating systems. Her methodologies include gradient boosting, deep learning, and compositional data analysis. Grants & Activities: Principal Investigator/Participant in 5 EU-funded projects (2017–2027) Peer reviewer for Environmetrics , International Journal of GIS , and others Organizer of academic workshops on data science applications Labs/Teams: She contributes to the Data Science Lab x Academia collaboration and leads the Sustainable Energy Planning Research Group, fostering interdisciplinary innovation in energy and urban systems.

Patrick Westphalمشاهده پروفایل

پژوهشگر

• Structured Machine Learning
• Semantic Web
• Big Data Analytics
• +۲ مورد دیگر

Dr. Patrick Westphal is a Researcher at the University of Leipzig's AKSW (Agile Knowledge Engineering and Semantic Web) Research Group, where he specializes in structured machine learning for big data applications. As a core member of the MOLE (Machine Learning and Ontology Engineering) group, he develops scalable frameworks like DL-Learner for semantic web technologies and life science use cases. His research focuses on: Structured machine learning algorithms for knowledge graphs Parallelization of learning systems for distributed architectures Geospatial data inference and ontology engineering Hybrid human-AI workflows for smart city analytics He leads contributions to multiple EU-funded projects including SANSA-Stack (distributed semantic analytics), GeoKnow (geospatial linked data), and QROWD (crowd-sourced data integration).