جستجوی استاد راهنمای دانشگاهی

Panagiotis Karrasمشاهده پروفایل

استاد

• Data Management Systems
• Graph Algorithms
• Data Mining
• +۳ مورد دیگر

Panagiotis Karras is a Professor at the Department of Computer Science , University of Copenhagen , with a faculty affiliate status at Aarhus University . His research focuses on robust and versatile methods for data access, mining, and representation. Education: MSc in Electrical and Computer Engineering from National Technical University of Athens , PhD in Computer Science from University of Hong Kong Research Interests: Spanning data management systems, graph algorithms, and privacy-preserving data publishing, his work addresses challenges in efficient query processing, influence propagation in networks, and semantic-aware data compression. Notable contributions include adaptive indexing techniques, graph alignment frameworks, and algorithms for fair stable marriage problems. Recent Publications (2024-2025): Focus on graph alignment, subgraph matching, and influence maximization, with applications to knowledge graphs, social networks, and time-series data analytics. Scientific Recognition: 2008 Young Scientist Award from the Hong Kong Institute of Science Keynote invitations at ACM SIGKDD and IEEE ICDE Professor Karras actively seeks motivated PhD students to collaborate on these research directions. His lab maintains a powerful compute cluster and modern facilities for data-intensive research.

• Network Analysis
• Social Networks
• Data Mining
• +۶ مورد دیگر

Michele Coscia is an Associate Professor in the Department of Data Science at the IT University of Copenhagen. He also serves as the Head of Programme for the BSc in Data Science. His research focuses on network analysis, social networks, data mining, and their applications in understanding human mobility, complex systems, economic development, and memetics. He has contributed to projects such as the Pioneer Centre for Artificial Intelligence and leads initiatives like ROMNET (Past social network reconstruction from material culture data) and Work2Vec (exploring deep learning in healthcare and work hours analysis). His research interests encompass interdisciplinary network science, including misinformation dynamics, ideological polarization, and the impact of social media systems. He has developed methodologies like the generalized Euclidean measure for multilayer networks and explored cultural data analytics through case studies like Italian music history. His work also addresses real-world challenges, such as quantifying the effects of violence on migration patterns and optimizing network sampling strategies for cost efficiency. Michele Coscia has received the Årets Forskningsmiljø 2022 award, recognizing his contributions to fostering outstanding research environments. He actively engages with media, discussing topics like misinformation and data visualization. As a principal investigator, he oversees projects funded by institutions such as the Villum Foundation and Danish National Research Foundation. His research spans academic collaborations across countries, reflecting his global network and commitment to advancing network theory and its applications.

Hua Luمشاهده پروفایل

استاد

• Computer Science
• Data Mining
• Data Science
• +۴ مورد دیگر

Hua Lu is a Professor in the Department of People and Technology Programming, Logic and Intelligent Systems at Roskilde University in Denmark. His research focuses on computer science with specialties in database systems, data mining, spatial data processing, and AI/ML applications for databases. He leads projects on AI-powered spatial databases and data-driven systems. His research interests span multiple domains including data science fundamentals, IoT data streams, location-based services, and database optimization techniques. Recent publications demonstrate strong focus on spatial-temporal data processing, machine learning integration with databases, and practical applications of data science. Professor Lu has received multiple scientific awards including Best Paper nominations and runner-up awards at premier conferences, recognizing his contributions to database and mobile computing research.

• Data systems
• Knowledge graphs
• Large-scale data analysis
• +۴ مورد دیگر

Zoi Kaoudi is an Associate Professor at the IT University of Copenhagen, affiliated with the Data, Systems, and Robotics school and the Data-intensive Systems and Applications department. Her research focuses on advancing data systems, knowledge graphs, and large-scale data analysis. She leads the Rank4QO project (2024–2027), funded by the Carlsberg Foundation, which explores query optimization using ranking algorithms. Her work emphasizes machine learning integration in data management systems, including frameworks like Apache Wayang, which unifies diverse data analytics platforms. Key collaborations include projects with Volkswagen Group and SAP, addressing dynamic graph processing and knowledge graph embeddings. Notable contributions include innovative approaches to parameter management (e.g., Good Intentions ), automated data science pipelines ( DORIAN ), and efficient graph processing algorithms. Her recent publications (2023–2025) highlight advancements in machine learning systems, distributed data processing, and adaptive optimization techniques. Current projects aim to bridge machine learning and data management through frameworks like Wayang and Dorian, with applications in air cargo revenue management and semantic web systems.

Peter Schneider-Kampمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Peter Schneider-Kamp is a Professor of Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on Artificial Intelligence, Machine Learning, Privacy-Preserving Techniques, and Algorithms. He has led projects such as the Danish Foundation Models initiative and the PREPARE cardiovascular disease project. His work spans synthetic data frameworks (e.g., Syntheval), quantization-aware neural networks (BitNet), and autonomous drone systems (Drones4Safety). He has been honored with awards including the Researcher Award 2014 and the Friedrich-Wilhelm-Preis 2009. Key research interests include optimizing sorting networks, termination analysis, and UAV-based infrastructure inspection. He has contributed to over 112 publications and actively participates in academic activities like organizing the 13th ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming. Schneider-Kamp also engages in educational roles, teaching courses such as DS806 and DM564 on database systems. His projects highlight interdisciplinary impact, including cardiovascular disease risk estimation (PREPARE) and AI-driven health advice analysis. He collaborates internationally, with recent work featured in venues like the International Conference on Agents and Artificial Intelligence.

Alexandros Gelastopoulosمشاهده پروفایل

پژوهشگر ارشد

• Mathematical Modeling
• Probability Theory
• Stochastic Processes
• +۴ مورد دیگر

Alexandros Gelastopoulos is a Research Fellow at the Institute for Advanced Studies in Toulouse with a PhD in Mathematics from Boston University (2019). His research employs mathematical modeling to study biological and social systems, focusing on probability theory, stochastic processes, and reinforcement dynamics in social contexts like cumulative advantage and ranking-based behaviors. Primary research interests include analyzing how popularity rankings influence social systems, studying brain oscillations for memory functions, and exploring decision-making paradoxes. His work often bridges mathematical rigor with behavioral sciences to uncover mechanisms driving social inequalities and neural computations. His publication trends show strong emphasis on social dynamics (e.g., lock-in effects, reinforcement processes) and computational neuroscience (e.g., neural rhythms, memory substrates), using stochastic modeling and empirical validation across disciplines. European Commission's Seal of Excellence (2024) Collaborates extensively with international researchers on projects involving mathematical sociology and behavioral experiments. Teaches game theory and mathematical courses while developing expository materials on real analysis and information theory.

• Data Management
• Location-Based Services
• Trajectory Data
• +۷ مورد دیگر

Christian S. Jensen is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His primary research focuses on data management, spatiotemporal systems, and AI-driven solutions for mobility and cyber-physical systems. He leads projects like DiCyPS (Data-Intensive Cyber-Physical Systems) and MALOT (Managing Mobility Data Quality for Location of Things). He has published over 700 papers, with recent work emphasizing time series forecasting, trajectory analysis, and edge computing. Notable contributions include frameworks like Memory Guided Transformers and TEAM for traffic prediction. His work has been recognized with awards including the IEEE TCDE Impact Award (2019) and the Order of Dannebrog (2016). Jensen actively collaborates internationally, holding roles in organizations like the Max Planck Institute and the Villum Foundation. His research bridges theory and practice, addressing real-world challenges in smart cities, energy systems, and autonomous driving.

• Computational Epidemiology
• Digital Epidemiology
• Network Science
• +۱ مورد دیگر

Nicolò Gozzi serves as a Senior Research Scientist at the National Research Council of Italy (CNR), specializing in computational approaches to public health challenges. His work bridges epidemiology, network science, and data analytics to address complex disease dynamics through interdisciplinary collaboration with institutions including Northeastern University and the Bruno Kessler Foundation. Gozzi's research focuses on Computational Epidemiology, Digital Epidemiology, Network Science, and Data Science. He pioneers methods for modeling behavioral responses during pandemics, analyzing mobility networks, and developing forecasting systems. His work integrates digital surveillance with traditional epidemiological frameworks to study vaccine impacts, health inequities, and intervention effectiveness—particularly evident in his extensive contributions to COVID-19 and influenza research. Analysis of his 15 most recent publications reveals a consistent trajectory in modeling infectious disease dynamics through computational frameworks. His work spans behavioral epidemiology (examining vaccination decisions and risk compensation), network resilience (assessing mobility patterns during interventions), and digital infrastructure applications (including cybersecurity analogies). A defining characteristic is his use of real-world data for policy-relevant forecasting, demonstrated through projects like Influcast and RespiCast. Gozzi leads or contributes to critical public health infrastructure projects including Epydemix (an open-source epidemic modeling toolkit), AWaRe (global antimicrobial stewardship), Influcast (influenza forecasting), and RespiCast (European respiratory disease hub). These initiatives emphasize collaborative, data-driven approaches to disease surveillance and intervention planning across multiple geographic and institutional contexts.

Ernests Lavrinovicsمشاهده پروفایل

پژوهشگر

• Machine Translation
• Transfer Learning
• Large Language Models
• +۴ مورد دیگر

Ernests Lavrinovics is a PhD fellow at the Department of Computer Science , Aalborg University , affiliated with the Technical Faculty of IT and Design and Data, Knowledge and Web Engineering research group. His work focuses on Natural Language Processing (NLP) , Large Language Models (LLMs) , and addressing challenges in machine translation and knowledge graph integration for resource-poor languages. His research explores critical NLP themes including hallucination detection in LLMs , adapter-based regularization techniques for multilingual transfer, and multitask benchmark development for creole languages. He contributes to datasets like MultiHal and CreoleVal , emphasizing semantic grounding and cross-lingual evaluation. Recent publications analyze adapter architectures as regularizers in low-resource settings (ACL 2025) He co-developed MultiHal (2025), a multilingual dataset for LLM hallucination evaluation His work spans knowledge graph applications , transfer learning , and multilingual representation challenges As an active academic, he has organized events like the Copenhagen NLP Symposium (2025) and AAU NLP Symposium (2024). He participates in technical programs at venues such as ACL and MRL Workshops , with citations in Scopus and Mendeley readership.

• Theoretical Computer Science
• Algorithms
• Edge Estimation
• +۵ مورد دیگر

Holger Dell is a Lecturer in Theoretical Computer Science and Algorithms at the IT University of Copenhagen. His research focuses on computational complexity, graph theory, and algorithmic efficiency. Active in polynomial-time algorithms and oracle-based methods Contributions to edge estimation in hypergraphs and fairness in node embeddings Key collaborations with BARC (Basic Algorithms Research Copenhagen) project Research trends show expertise in causal modeling, finite field polynomial solving, and graph embedding techniques. Recent work emphasizes algorithmic fairness and abstract causal relationships. Participated in a major project funded by the Villum Foundation (2017-2024) as a collaborator.

Patrick Westphalمشاهده پروفایل

پژوهشگر

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

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

• Plant Biochemistry
• Plant Genomics
• Systems Biology
• +۴ مورد دیگر

Marek Mutwil serves as an Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen, specializing in plant biochemistry with research spanning genomics, systems biology, and computational approaches to plant science. His work bridges experimental biology and data-intensive methodologies to address fundamental questions in plant environmental responses. His research program focuses on deciphering regulatory networks in plant stress adaptation, particularly through cross-species analyses of abiotic stress mechanisms in hydroponic systems. A significant portion of his recent work involves developing computational infrastructure for plant science, exemplified by the PlantConnectome knowledge graph that integrates literature-derived biological relationships across 71,000+ plant research articles. This dual emphasis on experimental stress physiology and bioinformatics resource development positions his work at the intersection of molecular plant biology and data science. Analysis of his 2025 publications reveals a consistent trajectory toward integrative plant systems biology, combining hydroponic crop stress experiments with large-scale knowledge graph applications. These works demonstrate growing emphasis on translational bioinformatics tools that convert fragmented plant science literature into structured, queryable biological networks. Scientific Awards: No awards were documented in the provided text. Advising and Grants: The source material contains no references to graduate students, postdoctoral trainees, or research funding sources. Labs and Teams: While affiliated with the Section for Plant Biochemistry, no specific laboratory structure, team members, or collaborative consortia are described in the available information.

• Computational Chemistry
• Catalysis
• Quantum Chemistry
• +۳ مورد دیگر

Julius Johannes Seumer is a Postdoctoral Research Fellow in the Department of Chemistry at the Faculty of Science, University of Copenhagen. His research focuses on computational approaches to catalyst design and chemical synthesis optimization, with expertise in quantum mechanics simulations and evolutionary algorithms. His primary research interests include computational chemistry, catalysis, quantum chemical modeling, and machine learning applications in chemical synthesis. Key areas involve regioselectivity prediction, transition metal complex analysis, nitrogen fixation catalysts, and automated synthesis planning using genetic algorithms. Analysis of his recent publications reveals strong trends in computational catalyst discovery, particularly using genetic algorithms for exploring chemical space beyond traditional libraries. His work spans organic, inorganic, and physical chemistry domains with emphasis on Morita-Baylis-Hillman reactions, C-H activation, and dinitrogen fixation. Seumer collaborates extensively with Professor Jens H. Jensen and other researchers at the University of Copenhagen, with publications appearing in high-impact journals including Angewandte Chemie , Chemical Science , and Beilstein Journal of Organic Chemistry . His computational methodologies have garnered attention across academic networks, with citations in Scopus and engagement on platforms including X (Twitter), Bluesky, and Mendeley. His work demonstrates significant interdisciplinary impact across chemistry and computational sciences.