جست‌وجوی استاد و پژوهشگر

• Algorithms and Complexity
• Theoretical Computer Science
• Data Structures
• +۳ مورد دیگر

Nikolaus Alexander Schrack is a Lecturer at the Department of Computer Science (DIKU) of the University of Copenhagen. His work is associated with the Algorithms and Complexity (AC) section, which focuses on theoretical computer science, including data structures, graph algorithms, computational complexity, and algorithmic paradigms with applications in machine learning. Research Interests: His research aligns with the AC section's exploration of efficient computation, combinatorial problem-solving, and theoretical foundations impacting real-world challenges. Key areas include algorithm design for massive data, optimization, and interdisciplinary applications. Publications: A 2022 publication in the Findings of the Association for Computational Linguistics investigates the use of Abstract Meaning Representation (AMR) for legal and logical reasoning tasks. Contact: Email: nikolaus.schrack@di.ku.dk Address: Universitetsparken 1, DK-2100 Copenhagen Ø

• Computer Science
• Data Management
• Indoor Location-Based Services
• +۴ مورد دیگر

Tiantian Liu is an Assistant Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Their research focuses on indoor location-based services, trajectory analysis, and spatial data management. Key areas include indoor navigation algorithms, temporal-variation-aware path queries, and uncertainty-aware contact tracing systems. Education details are not explicitly provided in the text. The researcher has collaborated on the Data Management Foundations for Indoor LBS project (2019-2021), contributing to foundational work in indoor spatial databases. Research interests span computer science , data management , and spatial computing . Notable contributions include keyword-aware routing systems (IKAROS), robust trajectory similarity computation (CLEAR), and time-constrained indoor pathfinding frameworks. Their work integrates machine learning, database systems, and algorithmic design to address challenges in indoor environments. In 2021, Liu co-authored the SSTD 2021 Best Paper Candidate . Their research emphasizes practical applications such as crowd-aware path planning and uncertainty modeling in indoor positioning data. Current work explores continuous similarity search for vessel trajectories (2025) and novel methods for handling temporal variations in indoor environments. The researcher's lab focuses on intelligent building systems and context-aware spatial analytics.

Eleni Tzirita Zacharatouمشاهده پروفایل

استادیار

• Spatial Data Management
• Geospatial Analytics
• Query Processing
• +۱ مورد دیگر

Eleni Tzirita Zacharatou is Assistant Professor in the Department of Computer Science at the IT University of Copenhagen. Her research develops efficient data management tools for modern analytics workloads, with specialization in spatial data processing and geospatial applications. She leads projects on query optimization, spatial indexing, and distributed geocomputation. Her research focuses on improving the efficiency of spatial data analytics through novel indexing structures, approximation techniques, and hardware acceleration. Key areas include spatial join optimizations, geospatial stream processing, and scalable earth observation data systems. She develops specialized algorithms for polygon processing, raster operations, and spatiotemporal indexing. Dr. Zacharatou's publications demonstrate innovations in geospatial query processing, including raster-based approximations for spatial joins, distributed execution of zonal statistics, and FPGA-accelerated sketching techniques. Her work bridges database systems research with practical applications in earth observation, IoT monitoring, and transportation analytics. Awards & Recognition: Distinguished Reviewer (VLDB 2022) SIGMOD Best Demonstration Award (2018) She leads the GeoSpatial Systems Lab, developing open-source tools for processing massive geospatial datasets. Current projects include distributed spatial aggregation frameworks and learned indexing techniques for earth observation platforms.

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.

Lorenzo Dall'Amicoمشاهده پروفایل

عضو هیئت علمی

• Network science
• Temporal networks
• Machine learning
• +۱۳ مورد دیگر

Lorenzo Dall'Amico is a Postdoctoral Researcher at ISI Foundation in Turin, Italy, and an assistant professor at the University of Torino, where he teaches a course on complex networks. He is a key member of the research team led by Professor Ciro Cattuto, focusing on the analysis of complex, temporal, and proximity networks with applications in epidemiology, social sciences, and human behavior modeling. PhD in Signal, Image, Speech, and Telecommunications, 2021 – Université Grenoble Alpes Master in Physics of Complex Systems, 2018 – Politecnico di Torino M2 in Physics of Complex Systems, 2018 – Paris Sud (XI) BSc in Physical Engineering, 2016 – Politecnico di Torino His research lies at the intersection of statistical physics, mathematics, and computer science. He develops interpretable representations of high-dimensional network data, with a particular focus on temporal and proximity networks. His work has significant applications in epidemic modeling, public health, and social dynamics. He is deeply involved in interdisciplinary projects such as COVID-19 Real Time Epidemiology and Periscope , both aimed at improving pandemic response through data-driven modeling. His recent publications span top journals including Nature Communications , Science Advances , and Physical Review E , with themes centered around temporal graph embeddings, community detection in dynamic graphs, and the integration of socioeconomic factors into epidemic models. He has developed the open-source Julia package CoDeBetHe.jl for efficient spectral community detection in static and dynamic graphs, which is widely used in network science research. His scientific contributions include: Development of embedding-based distances for temporal graphs Creation of generalized contact matrices for improved epidemic modeling Efficient algorithms for distributed representations using SoftMax normalization Analysis of household and school-based contact patterns in disease transmission Lorenzo actively mentors through open science practices, releasing code and datasets alongside his publications. He has advised or collaborated with researchers across disciplines, contributing to projects funded by the Botnar Foundation. His work emphasizes data for good , applying advanced network science to real-world public health challenges. He leads and contributes to research teams focused on digital epidemiology and network-based modeling, leveraging high-resolution proximity data from projects like SocioPatterns. His future work aims to further bridge theoretical network science with practical applications in public health policy and intervention design.

Carlos M. Lima Azevedoمشاهده پروفایل

دانشیار

• Transportation Engineering
• Smart Mobility
• Urban Planning
• +۶ مورد دیگر

Carlos M. Lima Azevedo is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Transport Division, Intelligent Transport Systems Section within the Department of Technology, Management and Economics. He also serves as a Research Affiliate at MIT's ITSLab. His research focuses on mathematical modeling of human mobility, smart mobility services, and integrated transportation technologies. He has held roles including Research Scientist at MIT's ITSLab and Executive Director of MIT's Transportation Education Committee. Education includes a PhD from MIT (2014) and an MSc from LNEC (2008). Key projects include SimMobility (a simulation platform) and Tripod (sustainable travel incentives). Teaching includes courses on transportation systems and data analysis at MIT. His research interests span traffic simulation, safety analysis, and agent-based modeling. Recent work emphasizes equity in shared mobility, AI-driven traffic control, and urban sustainability. He collaborates on projects like Mobility of the Future and FMOD (Flexible Mobility On-Demand). His contributions include frameworks for automated mobility-on-demand systems and policy-sensitive models for transportation networks.

Peter Dologمشاهده پروفایل

دانشیار

• Recommender Systems
• Web Engineering
• Machine Learning
• +۵ مورد دیگر

Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design. His research focuses on Recommender Systems, Web Engineering, Machine Learning, and Health Informatics. He leads projects such as the MEco Medical Ecosystem and the IWIS Intelligent Web and Information Systems initiative. His work emphasizes explainable AI, knowledge graphs, and social media analytics for public health monitoring. Notable contributions include hypergraph-based recommendation models and sensitive information detection in legal documents. Projects include MEco (medical event surveillance) and IWIS (web systems personalization), funded by EU and national grants. His research spans 148 publications, with recent work on explainable recommendation systems and neural networks for medical data analysis. Collaborations include the University of Vienna and international conferences like ECIR and RecSys. Media coverage highlights his work on using social media for epidemic detection. Key research areas include recommendation systems with attention mechanisms, knowledge graph integration, and ethical AI applications in healthcare. His methodologies bridge machine learning with user-centric design, emphasizing transparency and explainability in AI-driven systems.

Sune Lehmannمشاهده پروفایل

استاد

• Networks and Complexity Science
• Social Data Science
• Complex Systems Analysis
• +۴ مورد دیگر

Sune Lehmann is a Professor of Networks and Complexity Science at DTU Compute, Technical University of Denmark, and a Professor of Social Data Science at the Center for Social Data Science (SODAS), University of Copenhagen. His research focuses on quantitative analysis of social systems using large-scale datasets, integrating approaches from complex systems physics, machine learning, and statistical analysis. Key areas include modeling complex networks, human mobility, sleep patterns, epidemic spreading, and social media behavior. Education: B.Sc. and M.Sc. in Physics from the Niels Bohr Institute (2001-2003), Ph.D. in Complex Networks from DTU (2007). Professional roles include membership in the Danish Royal Academy of Sciences and Letters, and advisory roles in Danish government task forces during the COVID-19 pandemic. Research Interests: Complex networks, social data science, human behavior analysis, epidemiological modeling, and the application of machine learning to social systems. Notable contributions include studies on SARS-CoV-2 genomic data, temporal networks, and behavioral dynamics during pandemics. Scientific Contributions: Over 100 publications, including work on graph representation learning, surrogate network generation, and interdisciplinary collaborations. Supervised multiple PhD students in areas like social network properties, air pollution impacts, and mobility patterns. Awards: Member of the Danish Royal Academy of Sciences and Letters (202X). Labs/Teams: Active in DTU Compute and SODAS, leading projects on network science, social behavior, and policy-relevant research on tech giants and AI governance.

Nina Gierasimczukمشاهده پروفایل

دانشیار

• Epistemic Logic
• Formal Learning Theory
• Artificial Intelligence
• +۳ مورد دیگر

Nina Gierasimczuk is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), affiliated with the Algorithms, Logic and Graphs research group. Her work focuses on formal learning theory, epistemic logic, and computational models of cognitive processes. She holds a PhD from 2010, titled Knowing one's limits: Logical analysis of inductive inference . Her research interests include the logical foundations of learning, multi-agent systems, and the dynamics of knowledge and belief revision. She has supervised multiple PhD students in areas like dynamic logic, formal learning, and AI applications. Key projects involve modeling cognitive agents, semantic universals, and belief revision mechanisms in artificial intelligence contexts. Publications span topics such as causal intervention learning, semantic alignment in populations, and the integration of logic with learning theory. Her work bridges theoretical computer science, philosophy, and cognitive science. She is actively involved in international workshops and conferences, contributing to the advancement of dynamic logic and epistemic systems. Professional activities include examining PhD theses, advising on multi-agent systems, and collaborating on projects like hospital staff planning with multi-agent goals. Her research has been published in venues like Journal of Logic and Algebraic Programming and proceedings of the International Workshop on Dynamic Logic .

Panagiotis Karrasمشاهده پروفایل

استاد

• Data Mining
• Database Systems
• Graph Data Management
• +۷ مورد دیگر

Panagiotis Karras is a Professor of Computer Science at the University of Copenhagen and a faculty affiliate at Aarhus University. His research centers on robust and versatile methods for data access, mining, and representation. He has held academic positions at Aalborg University, Skoltech, Rutgers, NUS, the University of Zurich, and DTU, reflecting a broad international academic footprint. His research interests lie at the intersection of database systems, data mining, and machine learning, with a focus on graph data management, data privacy, indexing, influence maximization, and clustering. He aims to develop scalable and efficient algorithms that maintain high utility under privacy and accuracy constraints. His recent publications reveal a strong trend in graph-based methods, including graph alignment (GRASP, FREDE), influence and diffusion modeling, data anonymization, and adaptive indexing. These works appear in premier venues such as VLDB, SIGMOD, KDD, ICDE, and AAAI, indicating sustained high-impact contributions. He has been recognized with the 2008 Young Scientist Award in Physical/Mathematical Science from HKIS. Karras is actively seeking motivated PhD students and has supervised numerous collaborative projects. While specific grant details are not listed, his continuous publication output and affiliations suggest active research funding. His work often involves interdisciplinary collaboration, particularly in graph analysis and privacy-preserving technologies. He has contributed significantly to the development of efficient data synopses, anonymization frameworks (e.g., SABRE, L-opacity), and scalable graph processing systems, often in collaboration with leading researchers in the database and data mining communities.

Kai Zhaoمشاهده پروفایل

پژوهشگر ارشد

• Time Series Analysis
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Kai Zhao is a Research Fellow in the Department of Computer Science at the Technical Faculty of IT and Design, Aalborg University, Denmark, specializing in time-series analytics and machine learning applications. Research Interests: Time Series Analysis Machine Learning Data Mining Contrastive Learning Graph Neural Networks Anomaly Detection His recent publications (2023-2025) demonstrate a cohesive focus on graph-based and contrastive learning techniques for time-series challenges, particularly addressing few-shot scenarios, multivariate forecasting, and anomaly detection in IoT contexts. Methodological innovations include dynamic graph modeling and generative contrastive frameworks. Scientific Awards: No awards mentioned in available documentation. As Principal Investigator, he led the project 'Time Series Analyse with Few-shot Learning' (2022-2023), advancing few-shot learning applications for time-series analysis. His collaborative network spans multiple institutions, evidenced by co-authorships with researchers from Aalborg University and international partners. His research integrates theoretical frameworks with practical implementations, showing strong emphasis on robustness and adaptability in real-world time-series applications.

Finn Årup Nielsenمشاهده پروفایل

دانشیار

• Neuroinformatics
• Text Mining
• Data Mining
• +۸ مورد دیگر

Finn Årup Nielsen is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Cognitive Systems department under Applied Mathematics and Computer Science. His career spans neuroinformatics, text mining, and social media analysis, with a focus on integrating neuroscience and computer science. Education : Master in Engineering (1993-1996), Technical University of Denmark Research Interests : Neuroinformatics: Developing software and databases for brain imaging data and neuroscience text analysis Social Media Analysis: Mining Twitter and Wikipedia for citation patterns, sentiment, and topic trends Knowledge Graphs: Wikidata integration, lexeme alignment, and semantic representation Environmental Informatics: Applying AI to sustainable assessment through projects like DreamsKG Project Supervision : Supervised PhD projects on federated learning, neural network explainability, and big data analytics at DTU Contributed to foundational neuroimaging research in the Human Brain Project since 1994

Lenka Tetkováمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Lenka Tetková is a Postdoctoral Research Fellow at the Technical University of Denmark , affiliated with the School of Computing and the Cognitive Systems department. Her work focuses on Artificial Intelligence , Machine Learning , and their applications in Speech Recognition , Image Classification , and Biological Data Analysis . Her research explores Transformer stacks in speech models, convex decision regions in deep networks, and explainable AI for biological systems. She also investigates model pruning to reduce computational effort while maintaining performance. Her recent publications highlight trends in speech representation models , neural network convexity , and hybrid symbolic-AI approaches for resource-constrained environments. These works span computer science , engineering , and biological data analysis . Lenka collaborates with researchers like Dr. Lars Kai Hansen and contributes to interdisciplinary projects at the intersection of AI , signal processing , and life sciences .

Gozde Unalمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Gozde Unal is a Full Professor in the Department of Computer Engineering at Istanbul Technical University's Faculty of Computer and Informatics Engineering, where she also serves as director of the ITU Vision Lab and founding professor of the AI&Data Engineering Department. She previously held faculty positions at Sabancı University and conducted industry research at Siemens Corporate Research. Her educational background includes a PhD in Electrical and Computer Engineering with a Mathematics minor from North Carolina State University (2002), following postdoctoral work at Georgia Institute of Technology. Professor Unal's research pioneers intersections of Artificial Intelligence and Computer Vision, with significant contributions to medical imaging, 3D reconstruction, and architectural heritage preservation. Her work on diffusion models, adversarial point cloud attacks, and training-free segmentation demonstrates technical innovation while addressing real-world challenges in healthcare diagnostics and cultural conservation. The integration of multi-modal dynamics and evidential deep learning reflects her forward-looking approach to uncertainty quantification in AI systems. Analysis of her 2023-2025 publications reveals dominant trends in point cloud processing for historical reconstruction, medical image segmentation using Bayesian frameworks, and continual learning architectures. These works consistently bridge theoretical advances with applications in architectural heritage documentation and clinical diagnostics, particularly through novel diffusion model adaptations. Her scientific leadership has been recognized through prestigious awards: L’Oreal Turkey’s Female Scientist Award in Life Sciences (2010) Distinguished Young Scientist Award from TUBA (GEBIP) Marie Curie Alumni Association Career Award (2017) As technical program co-chair for MICCAI 2016 and MIDL 2019, she has shaped international discourse in medical image computing. Her lab direction indicates active graduate supervision, though specific student names aren't documented in the source material. The ITU Vision Lab under her leadership drives cutting-edge research in medical image analysis and 3D scene understanding, while her role in founding the ITU-AI Research Center establishes institutional frameworks for interdisciplinary AI innovation across the university.

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

Han Tang is a Research Fellow at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . He is affiliated with the Machine Learning section, which engages in both theoretical and applied research across domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. Research activities at the department include collaboration with the SCIENCE AI Centre and the TreeSense center for remote sensing and deep learning of global tree resources. The section also provides access to a powerful compute cluster for advanced computational tasks. His recent work includes research on contextual molecule representation learning, leveraging chemical reaction knowledge for data-driven applications in computational chemistry and artificial intelligence. This aligns with the department’s focus on integrating machine learning with interdisciplinary challenges.