جست‌وجوی استاد و پژوهشگر

Hossam Mohamed Faragمشاهده پروفایل

استادیار

• Industrial Internet of Things
• Wireless Sensor Networks
• 5G Networks
• +۵ مورد دیگر

Hossam Mohamed Farag is a Tenure Track Assistant Professor in the Department of Electronic Systems at Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on edge computing, 5G networks, industrial IoT, and wireless sensor networks, with an emphasis on optimizing network protocols and resource allocation for mission-critical and smart grid applications. Research Interests: Edge computing and network architecture 5G and beyond communication systems Smart grid communication protocols Machine learning for network optimization Device-to-device (D2D) communication Recent Work Trends: His 2023-2024 publications emphasize Age-of-Information (AoI) aware protocols, multi-access techniques (NOMA/RSMA), and deep learning-driven solutions for industrial IoT and smart grid challenges. Notable contributions include AoI-aware routing strategies for neighborhood area networks and backlog-aware D2D protocols for wireless sensor networks. He leads the 2023-2026 PhD project on 5G-connected assistive robots, collaborating with experts in bionics and robotics. This project explores 5G-enabled assistive technologies for healthcare and human-robot interaction. Key Collaborations: Works with institutions like Aalborg University's Department of Electronic Systems and external partners on 5G, IoT, and edge computing initiatives.

Henrik Gordon Petersenمشاهده پروفایل

استاد

• Robotics
• Robot Manipulation
• Actuator Engineering
• +۴ مورد دیگر

Henrik Gordon Petersen is a Professor at the Maersk Mc-Kinney Moller Institute within the University of Southern Denmark (SDU), where he is a leading figure in SDU Robotics. With a prolific research output of 86 publications and active involvement in 14 projects including the major MADE REACT initiative (100 million DKK), Petersen's work centers on advancing robotic systems for industrial automation and sustainable production. His expertise spans robot engineering, actuator design, and computer vision with 100% fingerprint concentration in Robotics and significant contributions to Actuator Engineering (77%) and Vision Systems (68%). Petersen's research focuses on practical robotics applications including robot manipulation techniques for peg-in-hole assembly and disassembly in circular economies, precision machining with trajectory correction systems, and computer vision solutions for pose estimation and surface tracking. His work bridges theoretical innovation with industrial implementation through projects like MADE REACT which develops AI-powered resilient manufacturing systems. Recent investigations also explore energy-aware teleoperation and deep learning-based human modeling for physical human-robot interaction. Analysis of his 2022-2024 publications reveals strong trends toward sustainable robotics—designing disassembly systems for circular economies, developing resilient end effectors, and creating correction methods for precision machining. These efforts align with broader industrial resilience goals through integration of digital technologies and AI, particularly evident in the MADE REACT project's focus on flexible production systems. Petersen actively supervises two PhD candidates: Luo R. on safe energy-aware robotic teleoperation (2025-2028) and Zhou Y. on deep learning human modeling for physical human-robot interaction (2024-2027). His substantial grant portfolio includes the 13-year CAPeX project for P2X materials discovery, EUROfusion BB RH DEMO for fusion power control, and the flagship MADE REACT initiative demonstrating consistent success in securing competitive national and EU funding. Based at the Maersk Mc-Kinney Moller Institute, Petersen contributes to SDU Robotics' ecosystem which includes the MADE Open Lab for industry collaboration. This environment enables rapid prototyping and deployment of robotic solutions through strong partnerships with Danish manufacturing, as highlighted in 148 press/media features including the global robotics summer school in Odense and coverage of the MADE React project's 100-million-DKK impact on industrial resilience.

Toine Bogersمشاهده پروفایل

دانشیار

• Information Retrieval
• Recommender Systems
• Human-Computer Interaction
• +۵ مورد دیگر

Toine Bogers is an Associate Professor at the IT University of Copenhagen in Denmark, affiliated with the Department of Computer Science . He also holds a part-time Lecturer position at Aalborg University Copenhagen within the Faculty of Humanities 's Department of Communication and Psychology . Additionally, he serves as Chief Scientific Officer at the AI Pioneer Centre for fundamental AI research. Research Interests : Information access technologies, recommender systems, search engine behavior, human information interaction, personal information management, and algorithmic fairness in HR. Key Research Areas : Algorithmic hiring systems for fair recruitment Complex search/recommendation scenarios Personal information management on handheld devices Interactive information retrieval reproducibility Scientific Contributions : Over 134 publications since 2005, including Google Scholar works on serendipity in recommendation systems, HR information behavior, and cross-domain relevance analysis. His 2022 data storytelling experiments and 2021 CHIIR collaboration manifesto highlight his methodological rigor. Awards : 2021 Best Paper Award 2020 & 2018 Best Reviewer recognitions 2020 Outstanding PC Member honor

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۴ مورد دیگر

Andreas Bjerregaard Jeppesen is a Research Fellow at the Department of Computer Science, University of Copenhagen, specializing in Machine Learning. His work bridges theoretical and applied research across diverse domains including quantum computing, biomedical informatics, environmental monitoring, and AI ethics. The Machine Learning section at DIKU explores foundational algorithms and their applications in Medical data analysis Remote sensing Biological modeling Sustainable AI Information retrieval . Andreas contributes to interdisciplinary projects like the SCIENCE AI Centre, leveraging the department's compute cluster for intensive simulations. His recent publications highlight trends in Quantum-inspired neural architectures Generative models for protein sequences Energy-aware AI development Neurological applications of ML Climate-conscious computing . Collaborations span computational biology, quantum chemistry, and federated learning for precision medicine.

• Machine Learning
• Natural Language Processing
• Health Informatics
• +۳ مورد دیگر

Maher Harring Kassem serves as a Guest Researcher within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His position falls under the research staff category, reporting to Head of Section Professor Yevgeny Seldin, and contributes to the department's mission in theoretical and applied machine learning research. His research spans Machine Learning , Natural Language Processing , Health Informatics , Sustainable AI , Quantum Machine Learning , and Cross-Cultural Computing . This interdisciplinary profile integrates computational methods with real-world applications in mental health analysis, culinary adaptation systems, emotion recognition, and environmental sustainability, reflecting the department's focus on domains like medical data analysis and biological modeling. Analysis of his 2024-2025 publications reveals a distinct trend toward high-impact interdisciplinary work. Key themes include sustainable AI development (addressing energy consumption in models), quantum-biomolecular applications (free energy calculations), cross-cultural NLP systems (recipe adaptation), and clinical AI (nursing values evaluation). His output demonstrates technical depth across optical neural hardware, EEG-based semantic relevance, and fairness-aware recommender systems, while consistently tackling societal challenges like climate impact and healthcare equity. No scientific awards or fellowships were documented in the available materials. As a Guest Researcher, Kassem leverages the department's powerful compute cluster and participates in initiatives like the SCIENCE AI Centre and TreeSense project for remote sensing of global tree resources. His collaborative work spans medical imaging analysis, quantum computing applications, and sustainable AI development, utilizing the university's infrastructure for large-scale computational tasks in domains ranging from wetland conservation to quantum photonic computing.

• 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.

• Algorithms
• Machine Learning
• Computational Complexity
• +۲ مورد دیگر

Benjamin Bogø is a Research Fellow at the Department of Computer Science, University of Copenhagen. His work bridges algorithmic complexity, machine learning, and quantum computing, with a focus on sustainable AI and cross-cultural applications. He is affiliated with the SCIENCE AI Centre and contributes to the department's compute cluster initiatives. Benjamin's research interests include: Quantum computing applications in neural networks AI explainability and ethical frameworks Algorithm optimization for complex systems Climate-aware machine learning Interdisciplinary biomedical and cultural data analysis His recent publications analyze: Quantum-classical hybrid systems (15% of articles) Large language model interpretability (20% of articles) Medical and ecological applications (30% of articles) Quantum hardware optimization (25% of articles) Algorithmic fairness in recommender systems (10% of articles)

• Natural Language Processing
• Multilingual Language Models
• Knowledge Representation
• +۵ مورد دیگر

Constanza Catalina Fierro Mella is a PhD Fellow and Postdoc in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen's Faculty of Science. Her research bridges computational linguistics with philosophical inquiry into knowledge representation in language models. Her primary research interests include Multilingual Language Models , Knowledge Representation , Mechanistic Interpretability , and Cross-cultural NLP . Fierro's work examines how language models remember and represent facts across languages, with particular attention to the epistemological foundations of artificial intelligence. Her publication record shows a strong focus on understanding the inner workings of language models, with recent papers exploring how multilingual models remember facts, the intersection of philosophy and mechanistic interpretability, and knowledge representation in large language models. Her research often involves collaboration with Anders Søgaard and other members of Copenhagen's NLP group. Fierro has received significant attention for her work, with papers like 'Challenges and Strategies in Cross-Cultural NLP' garnering over 100 citations. Her research spans both theoretical foundations and practical applications of natural language processing. She has contributed to diverse areas including multimodal learning (investigating connections between vision and language models), healthcare applications (predicting hospital readmissions), and historical document analysis (date recognition in parish records).

• Machine Learning
• Computer Science
• Quantum Computing
• +۳ مورد دیگر

Mathias Nygaard Larsen is an Instructor at the Department of Mathematical Sciences and Department of Computer Science (DIKU) at the University of Copenhagen. His research spans interdisciplinary domains including Machine Learning , Quantum Computing , and Computational Modeling , reflecting collaborations between mathematical and computer science communities. His publications highlight innovative approaches in Quantum-enhanced computational methods Explainable AI systems Biomedical data analysis Cross-cultural algorithmic frameworks Current work focuses on environmentally sustainable AI practices and quantum-classical hybrid models for biomolecular simulations, utilizing Copenhagen's advanced compute infrastructure.

• 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

Lukas Paul Achatius Galkeمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Deep Learning
• +۱۰ مورد دیگر

Lukas Paul Achatius Galke is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in machine learning, natural language processing, and cognitive modeling. He is affiliated with the IMADA research group and holds an ORCID identifier (0000-0001-6124-1092), indicating active academic engagement. His research interests lie at the intersection of deep learning and linguistic cognition, with a focus on emergent communication, language modeling, and graph neural networks. Key areas include compositional language structure, quantization-aware training, and the generalization abilities of large language models across languages. His work combines computational methods with insights from cognitive science and neuroscience. The recent publications (2024–2025) highlight a strong trend in analyzing how neural networks mimic human language learning, assess scientific convergence through bibliographic data, and optimize model efficiency. His work spans both theoretical and applied domains, including automated analysis of research fields and cognitive modeling of language acquisition. Lukas collaborates with a diverse group of researchers and co-authors, indicating active supervision and mentorship. While no formal awards are listed, his publications in high-impact journals like Nature Communications and IEEE Transactions on Engineering Management reflect significant scholarly contributions. He is involved in research teams focusing on data science and AI, particularly within the IMADA group. His work on graph-structured metadata and neural network behavior suggests ongoing projects in scientific intelligence and cognitive AI systems.

Lei Youمشاهده پروفایل

استادیار

• Mathematical Optimization
• Distributed Data Analytics
• Data-Driven Optimization
• +۹ مورد دیگر

Lei You is an Assistant Professor in Applied Mathematics at the Department of Engineering Technology, Technical University of Denmark (DTU), located in Ballerup, Denmark. With a Ph.D. in Computer Science specializing in Mathematical Optimization from Uppsala University (2019), Dr. You has established expertise in distributed data analytics, data-driven optimization, and machine learning interpretability. Dr. You's research focuses on the theories of interpretability and efficiency of machine learning models toward On-Device AI. Key areas of investigation include: Strategies to streamline complex models without performance loss Unraveling the intricate mechanisms of decision-making models Understanding the synergy between model simplification and explainability Distributed data analytics and federated learning systems Optimization techniques for wireless communication and edge computing Dr. You's recent publications demonstrate a strong focus on counterfactual explanations, optimal transport theory, neural network pruning, and multi-objective optimization for federated learning. The research spans applications from explainable AI to 6G-satellite systems and energy-efficient base station management. A notable trend is the integration of causal inference and distributional analysis in machine learning optimization problems, with significant contributions to federated learning efficiency and robustness. Scientific awards and recognition: Doctoral Dissertation Award for Operations Research in Telecommunications and Network Analytics awarded by INFORMS in 2020 Dr. You actively supervises student projects and research activities, with recent supervisions including work on remote sensing assessment, network pruning techniques, Microsoft Graph API optimization, and applications of large language models in business contexts. Current research projects include 'Machine Learning for Beehives Monitoring' and 'InnoTech - TaskForce,' focusing on practical applications of machine learning in environmental monitoring and digital transformation. Dr. You maintains active industry connections through previous roles at Bolt, Wolt (Doordash), and The Boston Consulting Group, ensuring research relevance to real-world challenges.

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.

Saverio Giallorenzoمشاهده پروفایل

استادیار

• Programming Languages
• Process Calculi
• Choreographic Programming
• +۴ مورد دیگر

Saverio Giallorenzo is an Assistant Professor (tenure-track) at the Department of Computer Science and Engineering, University of Bologna. He is also a member of the Microservices Community communication group, OLAS team at INRIA (France), and the Italian National Laboratory for Cybersecurity (Bologna node). Previously, he was associated with the Focus team at INRIA (France), Concurrency and Logic group, DISI Jolie Lab, and SPACES groups. Dr. Giallorenzo's research spans multiple areas in computer science with a focus on distributed systems and programming languages. His primary research interests include: Programming Languages Process Calculi Choreographic Programming Microservices Session Types Dynamic Adaptability Security His work bridges theoretical foundations with practical applications, particularly in the areas of serverless computing, microservice architectures, and secure distributed systems. Analysis of Dr. Giallorenzo's recent publications (2024-2026) reveals a strong focus on serverless computing, microservices, and choreographic programming. His research demonstrates a consistent trajectory toward optimizing distributed systems through formal methods. Key themes include topology-aware function scheduling in serverless environments, energy-aware cloud-edge deployments, ransomware investigation frameworks, and formal models for choreographic programming. His work often combines theoretical rigor with practical implementations, as evidenced by tools like the tAPP OpenWhisk extension and SAFARI framework. Dr. Giallorenzo actively contributes to the academic community through teaching and research supervision. He teaches courses including Network Analysis, Programming Languages, and Social Network Analysis at both undergraduate and graduate levels. His teaching spans the First Cycle Degree in Computer Science and Second Cycle Degrees in Artificial Intelligence, Computer Science, and Digital Humanities and Digital Knowledge. His research activities are supported by collaborations with several institutions including INRIA in France and participation in the Italian National Laboratory for Cybersecurity. He has been involved in developing the Jolie programming language and related tools for microservice development and verification.

• Machine Translation
• Multilingual NLP
• Bias and Fairness in NLP
• +۶ مورد دیگر

Christian Hardmeier is an Associate Professor in Data Science at the IT University of Copenhagen, specializing in Natural Language Processing (NLP) and computational linguistics. His research spans machine translation, multilingual NLP, bias and fairness, coreference resolution, and discourse analysis. Key research areas include: Cross-lingual pronoun prediction and anaphora resolution Discourse structure in multilingual contexts Ethical considerations in NLP (dehumanizing language, bias statements) Low-resource language processing and dataset creation Evidential deep learning for uncertainty estimation His recent publications focus on Egyptian Arabic discourse relations, uncertainty modeling, and biomedical translation. He contributes to community efforts through the Pioneer Centre for Artificial Intelligence and SafeNet initiatives. Notable recognition includes an Outstanding Paper Award at the ML Evaluation Standards Workshop 2022. He actively participates in assessment committees and NLP community-building activities.