جست‌وجوی استاد و پژوهشگر

Yiyi Chenمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Language Model Security
• Large Language Models
• +۳ مورد دیگر

Yiyi Chen is a Research Fellow at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. Specializing in Natural Language Processing (NLP), their work focuses on language model security, multilingual modeling, and computational linguistics. They hold a Master’s in Computational Linguistics and a Bachelor’s in Computer Science from Ludwig Maximilian University of Munich. Chen has contributed to projects like the Carlsberg Foundation-funded 'Multilingual Modelling for Resource-Poor Languages,' exploring marginalized languages through transfer learning and multitask benchmarks such as CreoleVal. Research interests include adversarial attacks on text embeddings, security metrics for large language models (LLMs), and linguistic typology. Notable achievements include the 2024 EliteForsk Research Travel Grant and conference participations like ACL 2024. Their work bridges ethical AI concerns with technical innovations, addressing vulnerabilities in multilingual systems and proposing solutions for memorization and inversion attacks. Education: Master’s in Computational Linguistics, LMU Munich (2018-2020) Bachelor’s in Computer Science, LMU Munich (2015-2018) Publications span embedding inversion security, cross-lingual attacks (e.g., LAGO framework), and typological analysis. Collaborations include Macquarie University visits and contributions to ACL and TACL journals. Current projects emphasize multilingual model robustness and ethical NLP practices.

René Bødker Christensenمشاهده پروفایل

استادیار

• Quantum Codes
• Quantum Error Correction
• Entanglement Engineering
• +۳ مورد دیگر

René Bødker Christensen is an Assistant Professor at the Department of Mathematical Sciences, Aalborg University, within the Faculty of Engineering and Science. His research focuses on quantum codes, quantum error correction, entanglement engineering, and their applications in secure communication and computing. He holds a PhD in Mathematics (2020) from Aalborg University, specializing in quantum codes and multiparty computation. Education: PhD in Mathematics: Quantum Codes and Multiparty Computation (2020) Research Interests: Quantum coding theory, entanglement-based protocols, quantum error correction, and educational applications of virtual reality in mathematics. His work bridges theoretical mathematics and practical implementations in quantum networks and secure computation. Projects: myPBL-VRMath: Integrates virtual reality into engineering mathematics education (2024) Quantum codes research collaboration (2017-2019) Activities: Active in international conferences, including presentations on quantum entanglement applications and satellite-aided quantum networks. Participated in workshops on future communication technologies and quantum engineering. Labs/Teams: Collaborates with interdisciplinary teams in quantum information science and mathematics education. Engaged in projects combining theoretical research with practical implementations in emerging technologies.

Zheng-Hua Tanمشاهده پروفایل

استاد

• Machine Learning
• Speech Processing
• Audio Signal Processing
• +۷ مورد دیگر

Zheng-Hua Tan is a Full Professor of Machine Learning and Speech Processing at the Department of Electronic Systems, Aalborg University, Denmark. His research focuses on advanced signal processing techniques, including speech enhancement, audio representation learning, and applications in hearing aid technology. He leads the Artificial Intelligence and Sound group and has authored over 284 publications. His work spans machine learning, state-space models, and deep learning applications in audio-visual speech processing. Notable contributions include pioneering methods for noise-robust keyword spotting, diffusion-based speech enhancement, and adversarial attack defense in ASR systems. He also explores cross-modal audio captioning and generative models. His research extends to 6G radio sensing and energy optimization in CubeSats. Tan holds an ORCID identifier (0000-0001-6856-8928) and maintains active collaborations through international conferences like ICASSP and INTERSPEECH. His lab develops practical solutions for real-world audio challenges, including hearing assistance systems and low-latency voice activity detection. He supervised 15+ PhD students and has secured multiple grants for projects in AI-driven audio innovation. Current work emphasizes self-supervised learning, PAC-Bayesian theory for dynamical systems, and bi-level optimization in pretraining frameworks.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۲ مورد دیگر

Philip Kroon Enevoldsen serves as an Instructor in the Department of Mathematical Sciences at the University of Copenhagen, based at Universitetsparken 5, 2100 København Ø. His academic work bridges theoretical mathematics with applied machine learning security. His research centers on Adversarial Machine Learning and Open-Set Recognition , specifically developing frameworks to identify unknown classes under malicious input perturbations. This work addresses critical vulnerabilities in deep learning systems where traditional closed-set assumptions fail under attack scenarios, enhancing model reliability in security-sensitive applications. His 2025 publication analyzes familiarity metrics for robust open-set classification during adversarial attacks, contributing to Northern Lights Deep Learning Conference proceedings. The research demonstrates intersections between cybersecurity protocols and computer vision architectures, emphasizing practical defenses for real-world AI deployment.

• Cardiometabolic Research
• Epidemiology
• Machine Learning
• +۵ مورد دیگر

Jonas L. Isaksen is a Postdoctoral Researcher at the Department of Biomedical Sciences within the Faculty of Health and Medical Sciences at the University of Copenhagen. His research focuses on the physiology of circulation, kidney and lung, with particular emphasis on cardiometabolic research using advanced data analysis techniques. Dr. Isaksen's research interests span several interconnected domains: Application of machine learning and deep learning models for electrocardiogram (ECG) analysis Development of explainable AI approaches in cardiology Epidemiological studies using large Danish registers and population data Identification of novel associations in the cardiometabolic domain Creation of synthetic ECG data for open science in medical AI His publication record demonstrates a strong focus on applying artificial intelligence to cardiac diagnostics, with recent work spanning ECG analysis for dementia prediction, atrial fibrillation management, and mortality risk assessment. His 2025 publications particularly highlight methodological advances in ECG reconstruction and validation frameworks for AI-enabled medical tests. Dr. Isaksen has received recognition for his work, including the EliteForsk Rejsestipendium in 2021. As an educator, Dr. Isaksen teaches at all academic levels (BSc, MSc, and PhD), welcoming students interested in his research areas. His work is situated within the research group focused on 'Physiology of circulation, kidney and lung' at the University of Copenhagen.

Bin Yangمشاهده پروفایل

استاد

• Computer Science
• Data Management
• Machine Learning
• +۹ مورد دیگر

Bin Yang is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, artificial intelligence, and machine learning, with emphasis on spatiotemporal data analysis, representation learning, and time series forecasting. He leads projects like aSTEP (spatio-temporal data analytics) and Light-AI for Cognitive Power Electronics. Key contributions include advancements in trajectory data processing, anomaly detection, and lightweight neural architectures. Research interests span machine learning, data mining, intelligent transport systems, and material science applications. Notable awards include the IJCAI 2019 Distinguished PC Member and Sapere Aude Research Leader (2018). He has supervised six PhD students and contributed to over 119 publications. His work is supported by grants from Villum Foundation and EU initiatives. Yang is involved in interdisciplinary collaborations, including data-driven decision-making frameworks and environmental monitoring systems. Labs and teams include the Daisy Center for Data-intensive Systems and AI for the People initiatives. Projects emphasize real-world applications in smart cities, traffic forecasting, and sustainable technologies. His research bridges theoretical foundations with practical implementations in domains like oceanography and crowdsourcing systems.

• Inverse Problems
• Geostatistics
• Seismology
• +۴ مورد دیگر

Klaus Mosegaard is a Professor at the University of Copenhagen's Niels Bohr Institute within the Physics of Ice, Climate and Earth department. His academic affiliation spans decades of research in computational geophysics and inverse problems methodology. His research focuses on computational aspects of inverse problems and geostatistics, with applications across seismology, planetary geophysics, geothermal investigations, and geomagnetism. Key contributions include development of algorithms for nonlinear inversion, particularly through Markov Chain Monte Carlo methods. His work bridges theoretical advances with practical applications in planetary seismology (including Mars interior studies), deep seismic lithosphere analysis, resource exploration, and thermal studies in Greenland ice shelves. Analysis of his 110+ publications reveals consistent innovation in probabilistic inversion techniques, with recent work integrating machine learning (particularly generative adversarial networks) with traditional geostatistical methods for reservoir characterization and facies modeling. His 2023-2025 publications demonstrate ongoing leadership in addressing dimensionality challenges in large-scale inverse problems. Best Paper Award Computers and Geosciences 2008 The Ole Rømer Grant for outstanding research in geophysics (1994) Teaching Award, Faculty of Science, University of Copenhagen (2000) Honorable Mention for Best Papers in Geophysics journal (2011) Mosegaard has delivered 25+ invited lectures on Bayesian inversion paradigms, Monte Carlo methods, and seismic inversion since 2020, including presentations at major international conferences. His research profile shows sustained external collaborations across planetary science and petroleum geophysics fields, with significant contributions to Mars interior modeling through InSight mission data analysis.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

Ervin Dervishaj is a PhD Fellow at the Machine Learning Section of the Department of Computer Science, University of Copenhagen. His research spans theoretical and applied machine learning, with a focus on recommendation systems, medical imaging, and computational modeling. The Machine Learning Section engages in interdisciplinary work across Information retrieval Medical data analysis Remote sensing Sustainability Biological data modeling Recent publications highlight expertise in Recommendation system interpretability (2025) GAN-based collaborative filtering (2022) Linguistic typology through language embeddings (2018) Medical imaging applications in osteoarthritis and neurodegenerative diseases (2016-2018) Optimization algorithms for adversarial learning (2016-2017) He contributes to projects involving the SCIENCE AI Centre and the TreeSense Centre for remote sensing applications.

• Fair Machine Learning
• Federated Learning
• Graph Neural Networks
• +۳ مورد دیگر

Yijun Bian is a Postdoctoral Research Fellow at the Department of Computer Science (DIKU), University of Copenhagen, specializing in machine learning research. They are affiliated with the Machine Learning section and the SCIENCE AI Centre, focusing on theoretical and applied aspects of fair machine learning, federated learning, and adversarial robustness. Their research spans multiple domains including: Fairness-aware algorithms (HL-HGAT, FairSHAP) Federated learning security Heterogeneous graph modeling Adversarial attack analysis Ensemble learning optimization Biomedical data modeling Recent publications highlight their work on fairness evaluation, topological graph attention networks, and robustness in vision-based tasks. They actively contribute to advancing trustworthy AI systems through theoretical foundations and practical implementations.

• Machine Learning
• Natural Language Processing
• Sustainable Artificial Intelligence
• +۳ مورد دیگر

Casper Dorph-Jensen serves as a Lecturer in the Department of Computer Science at the University of Copenhagen, where he is an active member of the Machine Learning section. His academic work bridges theoretical machine learning foundations with practical applications across diverse domains, contributing to both research and educational initiatives within Denmark's premier computing institution. His research spans multiple critical frontiers in artificial intelligence. Key interests include: Natural Language Processing with emphasis on emotion-aware dialogue systems and cross-cultural adaptation frameworks Sustainable AI development addressing environmental impacts of large models Quantum machine learning applications for biomolecular simulations Medical image analysis techniques for clinical diagnostics Fairness-aware information retrieval systems and recommender algorithms Analysis of his 2024-2025 publications reveals strong interdisciplinary trends combining machine learning with quantum physics, healthcare informatics, and sustainability science. Notable patterns include the application of large language models to clinical contexts (particularly nursing values evaluation), energy efficiency concerns in AI infrastructure, and novel quantum-classical hybrid approaches for scientific computing. His work frequently addresses real-world implementation challenges in noisy environments and resource-constrained settings. No scientific awards were documented in available sources. Information regarding student supervision or specific research grants remains unavailable in current public records, though his Machine Learning section affiliation suggests participation in broader departmental initiatives like the SCIENCE AI Centre and TreeSense remote sensing project. He operates within the Department of Computer Science's Machine Learning section, which maintains dedicated high-performance computing resources and participates in the university-wide SCIENCE AI Centre. This section focuses on both theoretical ML foundations and applications in medical imaging, biological data modeling, and sustainability-focused computing, with recent projects including quantum computing initiatives and environmental monitoring systems.

Anca-Simona Horvathمشاهده پروفایل

پژوهشگر

• Computational Design
• AI in Architecture
• Bio-technologies in Creative Practices
• +۵ مورد دیگر

Anca-Simona Horvath is a Guest Researcher at Aalborg University's Department of Communication and Psychology, affiliated with The Faculty of Social Sciences and Humanities. She leads the Art, Aesthetics & Health Research Laboratory for Art and Technology, focusing on intersections between emerging technologies (AI, bio-technologies) and creative practices. Her work addresses broader cultural contexts through design-based research, spanning scales from footwear to urban planning. Education: PhD in Architecture, with professional experience as an architect and computational designer in Romania and Denmark. Research contributions span architectural humanities, computational design, and digital art, with publications in journals like Frontiers of Architectural Research and International Journal of Architectural Computing . Research interests include AI-driven conceptual architecture, bio-crafting with mycelium, and STEAM education for sustainability. Key projects include Environmental Knowledge in the Digital Anthropocene (EKoDA) and Agency for Resilience (ActRes), addressing climate resilience and interdisciplinary collaboration. She has received awards such as the Teacher of the Year (2019) and Young Researcher Grant (2013). Her recent articles explore AI tool adoption in architecture, generative adversarial networks in form generation, and fungal bio-technologies in design. She actively participates in peer review for journals like Buildings and conferences like CAADRIA, and advises on EU Horizon Europe energy projects. Labs/Teams: Leads the Research Laboratory for Art and Technology , collaborating with initiatives like MASSHINE and Green Societies . Her work intersects environmental humanities, digital art, and sustainable design, contributing to UN SDGs related to climate action and sustainable cities.

Daniel Ortiz Arroyoمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Daniel Ortiz Arroyo is an Associate Professor at Aalborg University's Faculty of Engineering and Science, affiliated with the Intelligent Energy Systems and Flexible Markets department. His research focuses on AI-driven solutions for energy systems, wastewater treatment optimization, robotics, and deep learning applications in industrial systems. He leads and collaborates on projects such as AITEKS (AI for Tech Support), OPTIMIZE (Deep Reinforcement Learning in Fermentation), and amphibious drone development for infrastructure inspection. Key research areas include reinforcement learning for environmental control systems, synthetic data generation for defect detection, and predictive modeling in wastewater treatment. His work integrates machine learning with physical systems to enhance efficiency and sustainability. Notable contributions include grey-box models for N₂O dynamics and amphibious quad-rotor designs for pipeline inspection. Dr. Arroyo has contributed to over 97 publications, including peer-reviewed journal articles and conference proceedings, focusing on AI applications in energy, environment, and robotics. He actively participates in editorial peer review for journals like Evolutionary Intelligence and CRC Press . His projects involve collaborations with industry partners like Novo Nordisk Foundation and Innovation Fund Denmark, addressing challenges in microbial fermentation, fiber rope inspection, and wind turbine maintenance. He maintains a strong emphasis on data-driven innovation and interdisciplinary research.

Dalin Zhangمشاهده پروفایل

دانشیار

• EEG-based Emotion Recognition
• Time Series Forecasting
• Graph Neural Networks
• +۴ مورد دیگر

Dalin Zhang is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He holds a position within the Technical Faculty of IT and Design. His research focuses on machine learning, sensor data analysis, and their applications in healthcare, smart cities, and human-computer interaction. Key areas include EEG-based emotion recognition, time series forecasting, and graph neural networks. He leads the PREDICTION-ENABLED DYNAMIC SCHEDULING OF HOUSEHOLD ELECTRICITY USAGE project (2023–2024), demonstrating expertise in energy systems optimization. His work bridges theoretical advancements with practical applications, such as developing lightweight models for real-time traffic flow prediction and creating benchmarks for emotion recognition systems like LibEER. Research interests span: EEG signal processing for emotion and intention recognition Efficient time series analysis and forecasting Graph neural networks and adversarial defense mechanisms Data privacy in wearable sensor systems Recent publications highlight innovations like Pure-GNN (graph neural networks against adversarial attacks), LightCast (traffic forecasting), and Self-Supervised EEG Representation Learning. He advises two PhD students but their names are not listed in the provided texts. His work often emphasizes computational efficiency and real-world deployment across healthcare, smart infrastructure, and consumer electronics.

Jes Frellsenمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Computational Biology
• +۴ مورد دیگر

Jes Frellsen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), affiliated with the Cognitive Systems research group. His office is located at Richard Petersens Plads, Building 321, 2800 Kongens Lyngby. Research Focus: Dr. Frellsen's work spans machine learning theory and applications, with core expertise in: Probabilistic modeling (latent variable models, Gaussian processes) Computer vision (image segmentation, geometry-aware algorithms) Generative AI (energy-based models, neural representations) Scientific applications (protein structure prediction, news recommendation systems) His research emphasizes uncertainty quantification and handling incomplete data. Advising & Projects: Currently supervising 5 PhD students on projects including: Machine learning for single-cell sequencing analysis AI-driven natural product discovery Digital twins for 3D concrete printing Inverse catalyst design via deep generative models Implicit neural representations for incomplete data

Anton Danholt Lautrupمشاهده پروفایل

پژوهشگر

• Synthetic Data
• Feature Selection
• Artificial Intelligence
• +۷ مورد دیگر

Anton Danholt Lautrup is a PhD student in Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on synthetic data generation, feature selection, and privacy-preserving techniques in machine learning. Education Master of Science in Physics, Quantum generative adversarial network models on a simulated photonic platform, Aarhus University (2022) Bachelor of Science in Physics, Aarhus University (2020) Research Trends His work spans computer science, artificial intelligence, and data mining, with a focus on synthetic data applications, evaluation metrics for machine learning, and quantum computing-inspired models. Publications from 2025 highlight collaborations on healthcare data privacy, benchmarking frameworks, and inter-dataset similarity analysis.