جستجوی استاد راهنمای دانشگاهی

Arijit Khan is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He leads the Data Engineering, Science and Systems group and is affiliated with the Technical Faculty of IT and Design. His research focuses on Graph Neural Networks , Blockchain , Data Management , and AI interpretability . He is the Principal Investigator (PI) of a major project on Data Management, Fundamental Algorithms, and Machine Learning for Emerging Problems in Large Networks (2022–2027). Research Interests : Graph Data Management & Machine Learning Blockchain Transaction Analysis Large Language Model + Knowledge Graph Synergies Healthcare AI (e.g., ICU glucose prediction) Explainable AI for Graph Neural Networks Research Trends : His publications emphasize neuro-symbolic systems , uncertain graph analysis , and AI-driven blockchain insights . Recent work bridges large language models with knowledge graphs and explores GPU performance optimization via shader code analysis. Awards & Grants : No explicit awards listed, but his active research grants include a 5-year project on large network analysis with interdisciplinary applications in life and health sciences. Funding emphasizes algorithmic innovation and data science integration. Labs/Teams : Head of the Data Engineering, Science and Systems research group, focusing on AI for societal impact ('AI for the People') and scalable graph data systems. Collaborations span blockchain analytics, healthcare informatics, and GPU architecture design.

• Graph Neural Networks
• Blockchain
• Data Management
• +۵ مورد دیگر

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +۴ مورد دیگر

Patrick Munk is an Associate Professor at the National Food Institute, Technical University of Denmark (DTU), where he leads research in the Research Group for Genomic Epidemiology and contributes to the DTU Microbes Initiative. His work integrates genomics, metagenomics, and bioinformatics to study antimicrobial resistance (AMR) across human, animal, and environmental systems. He is actively involved in global surveillance efforts and is currently accepting PhD students. PhD, MSc Member, The Danish Young Academy (2024–2029) Patrick Munk’s research focuses on antimicrobial resistance, genomic epidemiology, metagenomics, and bioinformatics . His work leverages large-scale sequencing of sewage, livestock, and environmental samples to understand the global spread of resistance genes. He has led and contributed to major international studies, including the global sewage surveillance project across 101 countries, and has developed key genomic resources such as the PanRes and MetalResistance databases. His recent publications span high-impact journals like Nature Communications and Nature Microbiology , with themes centered on sewage-based surveillance, fecal resistomes in livestock, source attribution of microbial communities, and novel sequencing methodologies . His research has been widely covered in media and policy documents, reflecting its societal impact. Genomic analysis of sewage from 101 countries (Nature Communications, 2022) Time-series metagenomics for source-attributed surveillance (Nature Communications, 2024) High-throughput single-cell metagenomic sequencing (Frontiers in Microbiology, 2025) Patrick Munk has received recognition through significant media coverage, policy citations, and widespread academic engagement. His work has been highlighted in over 40 news outlets, referenced in policy sources and patents, and discussed across social media and academic platforms like Mendeley and Wikipedia. He supervises multiple PhD students and is a Principal Investigator or Supervisor on several active and completed projects, including Bioinformatic methods for multi-metagenome integration and Host range and epidemiology of antibiotic resistance . He is also a co-creator of key datasets that support open science in AMR research. His lab is part of a broad collaborative network involving DTU, international consortia, and public health agencies.

• Antimicrobial Resistance
• Genomic Epidemiology
• Metagenomics
• +۱۲ مورد دیگر

Katja Hose is a Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Her research focuses on Data, Knowledge and Web Engineering with specializations in AI for the People and Artificial Intelligence and Machine Learning. She maintains an active research profile with numerous publications and projects. Department of Computer Science Technical Faculty of IT and Design Aalborg University Research areas: Query Processing, Semantic Web, Linked Data, Knowledge Graphs Professor Hose's research interests center on knowledge representation, semantic web technologies, and AI applications. Her work spans from theoretical database systems to practical applications in healthcare, environmental assessment, and microbial data analysis. She has made significant contributions to knowledge graphs, large language models, and semantic search technologies, with particular emphasis on addressing hallucinations in AI systems and improving table search in semantic data lakes. Her recent publications demonstrate a strong trend toward integrating knowledge graphs with large language models, developing evaluation frameworks for AI hallucinations, and applying data science to diverse domains including healthcare and environmental sustainability. Her research bridges theoretical computer science with practical applications that address real-world challenges. NLP4KGC Best Paper Award (2023) ESWC 2023 Best Demo Award (2023) 2020 AMiner AI 2000 Most Influential Scholars AIME 2020 Best Paper Nomination (2020) ESWC 2019 Best Demo Award Nomination (2019) Professor Hose leads multiple significant research projects including ARISTOTLE (AI for clinical risk assessment), DarkScience (microbial data analysis), and the Poul Due Jensen Professorate in Big Data and AI. She has supervised numerous PhD students and collaborates extensively across disciplines, particularly in healthcare applications of AI and environmental assessment technologies. Her research has attracted substantial funding from sources like Villum Fonden and Danish E-infrastructure Cooperation. She is actively involved in several interdisciplinary research teams, including collaborations with microbiologists on microbial dark matter projects and with environmental scientists on digital environmental assessment systems. Her work on the ARISTOTLE project demonstrates strong connections between AI research and clinical applications, while her DarkScience project bridges computer science with microbiology.

• Query Processing
• Semantic Web
• Linked Data
• +۷ مورد دیگر

Daniel Hardt serves as Associate Professor in the Department of Management, Society and Communication at Copenhagen Business School. His interdisciplinary research bridges computational linguistics, artificial intelligence, and social analysis, with particular focus on natural language processing applications and theoretical linguistic phenomena. His primary research domains include Computational Linguistics (specializing in ellipsis resolution and sluicing phenomena), Natural Language Processing (developing methods for psychographic classification and sentiment analysis), and Artificial Intelligence (examining large language model capabilities and limitations). Recent work analyzes travel behavior during crises, gender effects in evaluations, and GDPR policy comprehension through NLP techniques. His publications span top venues including Linguistic Inquiry , Tourism Management , and ACL proceedings. Hardt actively engages with practical business applications through 27 media contributions discussing AI implementation, ChatGPT transparency, and data-driven leadership strategies. His academic service includes organizing events like the 2019 "Fake News" conference at CBS and presenting at international venues including JSAI 2024. With 28 supervised academic works documented, he maintains substantial mentoring activity while contributing to public discourse on digital transformation challenges.

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۴ مورد دیگر

Mark D. Scherz is an Associate Professor at the Natural History Museum of Denmark, University of Copenhagen. His work focuses on evolutionary biology, taxonomy, and herpetofauna of Madagascar, integrating molecular and morphological approaches. ERC Starting Grant (GEMINI) for vertebrate miniaturization genomics Co-PI on DFG Taxon-OMICS grant Develops integrative taxonomic methods using micro-CT and museomics Research Interests : Specializes in the evolutionary dynamics of giant pseudo-island systems like Madagascar, investigating macroevolution, convergent evolution, and speciation. Key projects include: Phylogenetic analysis of microhylid and mantellid frogs Comparative osteology of miniaturized vertebrates Biofluorescence mechanisms in geckos and chameleons Biogeographic patterns in Malagasy reptiles/amphibians Publication Trends : Recent work emphasizes cross-disciplinary approaches (2025: skull morphology in snakes; 2024: amphibian genomics) 2021 studies on fluorescence and miniaturization Long-term taxonomic revisions of amphibians (2019-2024) Scientific Awards : ERC Starting Grant (2025) DFG Priority Programme participant (Taxon-OMICS) Collaborations : Works closely with Miguel Vences (Technical University of Braunschweig) and Frank Glaw (Bavarian State Collection of Zoology), focusing on Malagasy amphibians and reptiles.

• Macroevolution
• Convergent Evolution
• Biogeography
• +۶ مورد دیگر

Jens Dick-Nielsen is a Professor at Copenhagen Business School in the Department of Finance . His work primarily focuses on fixed income markets, liquidity, credit risk, and financial market microstructure. Institution: Copenhagen Business School Department: Department of Finance Academic Rank: Professor Research interests include: Corporate bond markets and liquidity Bank equity risk and financial premiums Data transparency and TRACE system analysis Market segmentation in arbitrage Role of underwriter relationships in bond issuance Policy implications in banking and capital markets Recent publications highlight trends in bond market dynamics, financial intermediation, and liquidity risk. Key themes include empirical analysis of TRACE data, dealer networks, and post-crisis market structures. Scientific awards and honors: Jens Dick-Nielsen has supervised 10 research works and contributed to 23 media appearances. His collaborations span topics like banking capital costs, liquidity analysis, and market microstructure studies.

• Finance
• Fixed Income
• Credit Risk
• +۴ مورد دیگر

Christoph Houman Ellersgaard is a Lecturer at the Department of Organization, Copenhagen Business School, specializing in power elite networks, corporate governance, and social inequality. His work intersects with the United Nations Sustainable Development Goals (SDGs), particularly addressing systemic power structures and democratic accountability. Research focuses on technocratic elements of the Danish power elite Analyses historical and contemporary elite career networks Examines Greenland's political economy and elite dynamics Critiques foundation governance and corporate domination Recent publications highlight climate governance challenges, network analysis methodologies, and the EU's Horizon research program. He contributes to public debates through media outlets like Politiken and Information, addressing inequality, corporate power, and policy transparency.

• Power Elite Networks
• Corporate Governance
• Historical Sociology
• +۵ مورد دیگر

Maria Del Rio-Chanona is an Assistant Professor in the Department of Computer Science at University College London (UCL), where she conducts interdisciplinary research at the intersection of network science, machine learning, and economic modeling. She is also a member of the External Faculty at the Complexity Science Hub since January 2025, reflecting her ongoing engagement with complex systems research. Her academic background includes a PhD in Mathematics from the University of Oxford, where she was part of the Institute for New Economic Thinking at the Oxford Martin School, and undergraduate studies in Physics at UNAM, Mexico. Her research interests center on understanding socioeconomic transformations using computational methods. She specializes in applying Large Language Models (LLMs), Agent-Based Models (ABMs), and network analysis to study the impacts of generative AI, the net-zero transition, and global crises like the COVID-19 pandemic on labor markets, economic resilience, and public discourse. Her work often involves analyzing large-scale digital trace data, including online labor platforms and social media, to uncover behavioral and structural shifts. The recent publications reveal a consistent focus on labor market dynamics in the face of technological disruption. Her work examines how generative AI reshapes demand for freelance skills, reduces public knowledge sharing on platforms like Stack Overflow, and influences mental health discourse in relation to employment decisions. She also investigates broader economic modeling, including pandemic shock propagation and employment transitions during decarbonization. Among her scientific recognitions is the Emerging Scientific Award from the Complex Systems Society in 2023. She has collaborated with international policy organizations such as the International Monetary Fund (IMF) and the International Labour Organisation (ILO), underscoring the policy relevance of her research. Maria Del Rio-Chanona has held prestigious research positions, including as a James S. McDonnell Foundation (JSMF) Postdoctoral Fellow at the Complexity Science Hub and a Visiting Fellow at the Growth Lab, Harvard Kennedy School. These roles have enabled her to lead and contribute to high-impact interdisciplinary projects. While no current advisees are listed, she actively engages with PhD opportunities and interdisciplinary research networks. She is involved in multiple research initiatives, including the development of HuBERT, an NLP algorithm for extending the Seshat Global History database, and projects studying labor transitions in net-zero scenarios. Her work on the HiST-LLM benchmark for historical knowledge in LLMs highlights her innovative integration of AI with social science.

• Network Science
• Agent-Based Modeling
• Large Language Models
• +۷ مورد دیگر

Jan Baumbach is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he leads cutting-edge research at the intersection of computer science, bioinformatics, and biomedical data science. His work integrates machine learning, network biology, and systems medicine to address complex challenges in health and disease. His research interests include Bioinformatics , Machine Learning , Gene Regulatory Networks , Drug Repositioning , Biomedical AI , and Computational Biology . He applies advanced computational methods to analyze large-scale biological data, with applications in cancer, metabolic diseases, dermatology, and infectious diseases. The 15 most recent publications highlight a strong trend toward biomedical artificial intelligence , network-based analysis , and translational bioinformatics . His work spans from foundational machine learning in healthcare to clinical applications in osteoarthritis, bone regeneration, and skin biology. A consistent theme is the integration of multi-omics data and the development of privacy-preserving federated learning frameworks for distributed healthcare systems. Jan Baumbach has been principal investigator on several major research projects, including: EU Horizon2020: Privacy-preserving federated machine learning in distributed healthcare Danish National Research Foundation: ATLAS Center for Functional Genomics of Tissue Plasticity Villum Foundation: Big Data Bioinformatics (Young Investigator Programme) Carlsberg Foundation: Computational profiling of bacterial volatile metabolomes (ProVol) He has supervised 13 PhD students and has an extensive publication record of 262 works. His research has been featured in high-profile media outlets, emphasizing the societal impact of drug repositioning and AI in medicine. He leads a research group focused on computational systems biology , with strong collaborations across Europe in the fields of genomics, metabolomics, and clinical data science.

• Bioinformatics
• Machine Learning
• Computational Biology
• +۷ مورد دیگر

Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.

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

Alvaro Torralba is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with the Technical Faculty of IT and Design. His research focuses on symbolic search, heuristic functions, and planning algorithms within artificial intelligence and machine learning. Notable projects include the ConAn initiative exploring contrastive analysis for state-space exploration. He has contributed extensively to classical planning, probabilistic planning, and automated planning competitions, earning awards such as the First Prize in the Agile Track of the 10th International Planning Competition (IPC’23). His work often bridges theoretical advancements with practical applications, including game-based network update synthesis and believable non-player character development. Research outputs include over 60 publications since 2011, with a focus on optimizing search algorithms and enhancing planning efficiency through techniques like operator-potential heuristics and bidirectional search strategies. His scientific contributions span algorithmic innovation, verification methodologies, and large-scale abstraction evaluation. Collaborations and datasets include foundational work on PDDL generators and pattern databases, with open-access resources available via Zenodo. As a program committee member and award-winning researcher, Torralba actively contributes to advancing the frontiers of AI planning and decision-making systems.

• Symbolic Search
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Tara Maria Boland is a Postdoc in the Department of Physics at the Technical University of Denmark, specializing in Computational Atomic-scale Materials Design. She maintains an active research profile with significant contributions to materials science infrastructure. Her research spans critical areas of modern computational science: Computational Materials Science and Atomic-scale Modeling Materials Discovery and Design Database Integration and Data Exchange Systems API Development for Scientific Applications 2D Materials and Heterostructures Machine Learning Applications in Materials Science Boland's publication record reveals a dual focus on fundamental materials research and practical computational tools. Her work on the OPTIMADE API has established new standards for materials data exchange, while her contributions to GPAW provide essential infrastructure for electronic structure calculations. The research demonstrates strong international collaboration across 10+ institutions with notable media coverage. As part of DTU Physics' Computational Atomic-scale Materials Design group, Boland collaborates extensively on projects advancing computational methodologies for materials science, with particular emphasis on creating accessible research infrastructure for the global community.

• Computational Materials Science
• Materials Discovery
• Database Integration
• +۳ مورد دیگر

Leon Derczynski is an Associate Professor of Computer Science at the IT University of Copenhagen , with a dual role as Principal Research Scientist/LLMSEC at NVIDIA . He leads the Strømberg NLP research group and coordinates NLP South at ITU, while also being affiliated with the Machine Learning group. Specializes in Natural Language Processing , Machine Learning , and LLM security Focus on Misinformation detection , Clinical text mining , and Danish language technology Research grants include Verif-AI (2.9M DKK), ClinRead (544K DKK), and LITHME (EU COST action, €11K). He has coordinated major projects like COMRADES and PHEME , and contributed to uComp and TrendMiner . Scientific recognition includes the University of Sheffield Exceptional Contribution Award (twice), WEBIST Best Student Paper award , and FP7 funding . His technical work includes the garak.ai LLM vulnerability scanner and generalised-brown clustering library. Actively supervises students and maintains numerous GitHub repositories (86 public projects) related to NLP, machine learning, and computational linguistics. He has delivered keynotes and guest lectures , including at Innopolis University (Russia) and PET (Danish Security and Intelligence Service).

• Natural Language Processing
• Machine Learning
• Misinformation Detection
• +۳ مورد دیگر

Andreas Pavlogiannis is an Associate Professor in the Department of Computer Science at Aarhus University. His research focuses on formal methods , algorithmic verification , automata theory , concurrency , static and dynamic program analysis , network diffusion , evolutionary graph theory , and evolutionary game theory . Teaching courses: Programming Languages (Bachelor) , Algorithmic Model Checking (Master) , and Program Analysis (Master) Service: Program committee member for POPL, ESOP, AAAI, IJCAI, CONCUR, OOPSLA, and organizer of CONFEST'25 His research has been supported by the Austrian Science Fund (FWF), VILLUM Foundation, Stibo Foundation, and Danish Council for Independent Research (DFF). He is actively recruiting PhD and PostDoc researchers. Recent publications span quantum computing , concurrent systems , evolutionary dynamics , and network science , with particular emphasis on symbolic algorithms , dynamic analysis , and graph-based models .

• formal methods
• algorithmic verification
• automata theory
• +۶ مورد دیگر