جستجوی استاد راهنمای دانشگاهی

Ali Basiratمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۵ مورد دیگر

Ali Basirat is an Associate Professor in the Department of Nordic Studies and Linguistics at the Faculty of Humanities, University of Copenhagen, affiliated with the Center for Language Technology (CST). His research focuses on data-driven approaches to language technology, particularly explainable natural language processing and morphosyntactic analysis, with emphasis on universal patterns across languages and efficient language processing tools. His academic qualifications include: PhD in Computational Linguistics from Uppsala University (awarded 2018) Certificate in Higher Education Pedagogy from Linköping University (awarded 2022) Dr. Basirat's research centers on mathematical modeling of natural languages, with core interests in universal language models, representation learning, dependency syntax, and explainable NLP. He investigates data-driven syntax, word embeddings, grammatical gender patterns across languages, and adaptation of large language models for environmental studies and sustainable development goals. His work bridges theoretical linguistics with practical NLP applications. Analysis of his 2020-2025 publications reveals three dominant trends: (1) Advancements in multilingual dependency parsing through linguistic typology and syntactic nuclei exploration; (2) Investigation of grammatical gender representation in word embeddings and language models; (3) Application of NLP techniques to climate conceptual history and environmental studies. His research increasingly addresses efficiency challenges in large language model adaptation while maintaining cross-lingual performance. No major scientific awards are currently documented in his public profile. Dr. Basirat supervises master's theses in natural language processing and computational linguistics, focusing on machine learning, language modeling, syntactic parsing, and knowledge graphs. He teaches graduate courses including Scientific Programming and Representation Learning for Natural Language Processing (RL4NLP) at the University of Copenhagen's IT & Cognition and Computer Science programs. He is a core member of the Center for Language Technology (CST) and actively collaborates with the Nordic Language Processing Laboratory (NLPL). His research network extends through membership in the Association for Computational Linguistics (ACL), facilitating international collaborations across European institutions.

• Construction Safety
• Automation and Robotics
• Building Information Modeling (BIM)
• +۳ مورد دیگر

Jochen Teizer is a Professor at the Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU). His research focuses on transforming construction industry productivity and safety through digital technologies like Digital Twins, BIM, and Extended Reality (XR). He specializes in construction safety, automation, lean production, and workforce efficiency. He holds roles such as Vice-President of the International Association on Automation and Robotics in Construction, and serves on the Editorial Board of the Journal of Automation in Construction. His work aligns with UN Sustainable Development Goals, emphasizing safer work environments and sustainable practices. Key Expertise: Construction Safety, Automation & Robotics, BIM, XR in Education Recent Projects: Digital Twin Applications, Noise Emission Prediction, Safety Ontology Frameworks Teizer has supervised multiple PhD students in areas like Green Construction Sites and Occupational Health and Safety (OHS) using Digital Twins. His awards include the CIBW099/W123 Best Paper Awards (2023, 2024) and the CONVR Best Presentation Award (2024).

• Quantum Optics
• Quantum Photonics
• Quantum Computing
• +۳ مورد دیگر

Love Alexander Mandla Pettersson is a Research Fellow at the Niels Bohr Institute , University of Copenhagen , specializing in Quantum Optics and Photonics . His work focuses on quantum computing protocols leveraging graph states and quantum emitters . Research Interests : Quantum state generation, fusion-based photonic quantum computing, loss-tolerant graph codes, and Bell state measurement applications. Publications highlight collaborations with leading researchers (e.g., Sørensen, Paesani) and innovations in deterministic graph code generation, resource-efficient state synthesis, and quantum communication resilience. Labs/Teams : Affiliated with the Quantum Optics group at the Niels Bohr Institute, advancing experimental and theoretical frameworks for photonic quantum systems.

• Software Engineering
• Process Modeling
• Business Process Management
• +۵ مورد دیگر

Thomas Troels Hildebrandt is a Professor in the Department of Computer Science at the University of Copenhagen, where he heads the Software, Data, People & Society research section. His work focuses on developing reliable and flexible software systems that adapt to user needs and legislative changes, with applications in digital law, workflows, and business processes. His educational background includes: PhD in Computer Science from Aarhus University (awarded February 23, 2000) Professor Hildebrandt's research spans Software Engineering , Process Modeling , and Business Process Management , with a focus on declarative approaches like Dynamic Condition Response (DCR) graphs. His work integrates formal methods to ensure system reliability in contexts ranging from smart contracts to public governance. He actively explores societal implications of AI, advocating for transparency and user-centered design in digital systems. Recent publications demonstrate a strong trend toward declarative process modeling applied to smart contracts and public governance . Key developments include DCR graphs for dynamic behavior modeling, cross-chain business logic monitoring, and digital compliance frameworks. His interdisciplinary approach bridges computer science with real-world societal challenges, particularly in adapting systems to evolving legislation and user requirements. No specific scientific awards are mentioned in the provided information. Professor Hildebrandt leads interdisciplinary research projects and serves on advisory boards for digitalization and AI. His work has fostered industry collaboration, including founding DCR Solutions based on his research. He acts as an independent consultant and speaker in digital transformation, with recent projects focusing on blockchain integration and public sector AI ethics. He directs the Software, Data, People & Society research section, which develops human-centered methods for adaptable digital systems. Current initiatives include DCR graph applications for GDPR compliance, smart contract security, and transparent AI in public services, with strong industry and government partnerships.

Roman Jurowetzkiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Natural Language Processing
• Technological Change
• +۴ مورد دیگر

Roman Jurowetzki is an Associate Professor in Innovation Studies and Applied Data Science at Aalborg University Business School (Faculty of Humanities and Social Sciences). He leads AI Denmark at Aalborg University and heads the AI Growth Lab, national initiatives supporting AI adoption in Danish SMEs. His work bridges computational methods (NLP, ML) with societal impact, focusing on emerging technologies like AI and neurotechnology. Key roles include steering CLAAUDIA (AAU's research computing unit) and founding the SSH Computational Social Science project. Education: PhD (2012) in computational methods Research Focus: Technological change, AI governance, innovation ecosystems Affiliations: IKE Group, MASSHINE, Danish Industrial Foundation Research interests span computational discourse analysis, patent classification models, and digital nomadism. Recent work highlights AI privatization trends and SME digital transformation strategies. Awards include the 2018 OECD challenge and 2014 best student paper.

Jakob Lykke Andersenمشاهده پروفایل

دانشیار

• Algorithms
• Graph Transformation
• Chemical Reaction Networks
• +۷ مورد دیگر

Jakob Lykke Andersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in algorithms with applications in cheminformatics and complex systems. He also holds a former external appointment as a Research Fellow at the Tokyo Institute of Technology (2015–2017). Research Interests: His work lies at the intersection of computer science and theoretical chemistry, focusing on algorithmic methods for analyzing chemical reaction networks. He employs hypergraphs, mixed-integer linear programming, and probabilistic models to study metabolic pathways, reaction databases, and prebiotic systems. His research emphasizes computational efficiency, formal modeling, and software implementation. Publication Trends: His recent publications (2019–2025) show a consistent focus on graph-based modeling of chemical systems, rule extraction from reaction databases, thermodynamic feasibility, and stochastic analysis. These works appear in high-impact journals in cheminformatics, bioinformatics, and complex systems, reflecting strong interdisciplinary collaboration. Scientific Contributions: While no specific awards are listed, his sustained research output and leadership in funded projects highlight significant contributions to algorithmic cheminformatics. Grants and Projects: He is actively involved in two major ongoing research projects: (1) Software Infrastructures for Teaching at Scale (funded by Innovation Fund Denmark, 2022–2025), and (2) DIREC (Danish Research Center for Digital Economy, 2020–2025), indicating active engagement in both educational technology and core computational research. Advising and Outreach: While no students are listed, he participates in academic advising through project supervision. He contributes to public discourse through media appearances on topics such as mathematics in health (e.g., intestinal system modeling in obesity) and educational well-being. Labs and Teams: He collaborates with interdisciplinary teams, including researchers from bioinformatics, chemistry, and computer science, particularly through projects involving Merkle, Flamm, Fagerberg, and Stadler. His work is associated with algorithmic cheminformatics and software framework development groups at SDU.

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.

• 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.

Henrik Bulskovمشاهده پروفایل

دانشیار

• Natural Logic Systems
• Database Querying
• Machine Learning
• +۳ مورد دیگر

Henrik Bulskov is an Associate Professor in the Department of People and Technology (Programming, Logic and Intelligent Systems) at Roskilde University. He holds an MSc and PhD. His work focuses on natural logic systems, database querying, and ontology-based information retrieval. He has contributed to projects such as SEAFACTS (digital maritime history platform) and NorDigHealth (regional digital health solutions). Education: MSc and PhD (specific disciplines not explicitly stated) Research interests include formal logic systems integration with databases, machine learning applications in bioinformatics, and semantic summarization through ontologies. Recent work explores query optimization in natural logic knowledge bases and disparity analysis in neural networks. His publications highlight advancements in computational logic for knowledge management and biomedical text analysis. Bulskov has participated in international conferences and contributed to media discussions on big data applications. Advising: No explicit student advisees listed Grants: Principal/Co-investigator in multiple projects including SIABO (2007–2012) and Duuoo Analysis (2021) Labs/Teams: Involved in interdisciplinary teams focusing on bioinformatics, health tech, and digital humanities through collaborative projects.

Martin Raussenمشاهده پروفایل

دانشیار

• Directed Algebraic Topology
• Concurrency Theory
• Geometric Models for Parallelism
• +۳ مورد دیگر

Martin Raussen is an Associate Professor at the Department of Mathematical Sciences, Aalborg University, Denmark. His work focuses on geometric and topological models for concurrency theory, directed algebraic topology, and transformation groups. He has contributed significantly to applying topological methods in computer science, particularly analyzing concurrent systems using higher-dimensional automata and directed spaces. He has been honored with Årets Underviser (Teacher of the Year) in 2018 and 2019, reflecting his dedication to education. His research involves collaborations on projects like the geometric analysis of mutual exclusion models and directed algebraic topology’s applications in concurrency. Raussen has published extensively in journals such as Journal of Applied and Computational Topology and EMS Magazine , with recent work including interviews with prominent mathematicians like Volker Mehrmann and Jean-Pierre Bourguignon. His publications span over four decades, emphasizing both theoretical advancements and educational outreach. He has supervised several initiatives through research networks like ACAT (Applied and Computational Algebraic Topology) and has contributed to EMS (European Mathematical Society) activities, including editorial work and conference organization.

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.

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.

• Harmonic Analysis
• Partial Differential Equations
• Stochastic Analysis
• +۳ مورد دیگر

Li Chen is an Associate Professor at the Department of Mathematics , Aarhus University . Their research focuses on the intersection of harmonic analysis, probability, and partial differential equations, particularly investigating interactions between randomness and analytic/geometric structures in smooth or rough environments. Key research areas include applications of heat semigroups and heat kernels, with a focus on hypoelliptic operators, fractional Laplacians, and stochastic processes on fractal and metric measure spaces. Recent work involves the Parabolic Anderson model, Dirichlet spaces, and geometric analysis on Lie groups. Articles highlight trends in stochastic PDEs, functional analysis on fractals (e.g., Vicsek set, Sierpinski gasket), and degenerate elliptic operators in weighted spaces. Co-authored works with F. Baudoin and others demonstrate interdisciplinary approaches to sub-Riemannian geometry and probabilistic methods in analysis.

Daniele Dell'Aglioمشاهده پروفایل

دانشیار

• Computer Science
• Knowledge Graph Embedding
• Differential Privacy
• +۶ مورد دیگر

Daniele Dell'Aglio is an Associate Professor at the Department of Computer Science , Aalborg University , affiliated with the Technical Faculty of IT and Design . His research focuses on privacy-preserving data synthesis, knowledge graphs, and semantic data integration. Academic Rank: Associate Professor Department: Computer Science School: Technical Faculty of IT and Design University: Aalborg University Research Interests : Development of privacy metrics for synthetic data generation Heterogeneous graph representation in knowledge graphs Interactive evaluation tools for data privacy Transformer-based biomedical data extraction Integration of gut-brain axis scholarly data Scientific Awards : Best paper at the Deep Learning for Knowledge Graphs workshop (2022) Collaborative Projects include HEREDITARY (Heterogeneous semantic data integration for gut-brain interplay) as Principal Investigator, and partnerships with NASA on Mars exploration data processing. His work spans privacy-preserving technologies, knowledge graph engineering, and biomedical text mining applications.

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.