Yijun Bian is a Postdoctoral Research Fellow at the Department of Computer Science (DIKU), University of Copenhagen, specializing in machine learning research. They are affiliated with the Machine Learning section and the SCIENCE AI Centre, focusing on theoretical and applied aspects of fair machine learning, federated learning, and adversarial robustness. Their research spans multiple domains including: Fairness-aware algorithms (HL-HGAT, FairSHAP) Federated learning security Heterogeneous graph modeling Adversarial attack analysis Ensemble learning optimization Biomedical data modeling Recent publications highlight their work on fairness evaluation, topological graph attention networks, and robustness in vision-based tasks. They actively contribute to advancing trustworthy AI systems through theoretical foundations and practical implementations.
Stefano Paesani is an Associate Professor at the University of Copenhagen, affiliated with the Niels Bohr Institute (Department of Quantum Optics) within the Faculty of Science. His research focuses on quantum photonics, quantum computing, and integrated photonic systems. He explores scalable quantum architectures leveraging quantum emitters and graph states, with a particular emphasis on error correction, photonic nonlinearity, and high-dimensional entanglement. His work includes developing fusion-based photonic computing schemes, optimizing loss-tolerant architectures, and advancing programmable silicon-nitride integrated circuits for quantum information processing. Recent contributions address deterministic photon source interfacing, reconfigurable nonlinear circuits, and high-speed lithium niobate processors. His research also intersects quantum machine learning and Hamiltonian learning, utilizing quantum systems to model complex physical phenomena. Stefano collaborates with interdisciplinary teams across the University of Copenhagen’s Quantum Hub, contributing to initiatives in quantum communication, quantum simulation, and quantum sensing. His experimental setups often involve solid-state quantum emitters and advanced photonic platforms, aiming to bridge theoretical models with practical quantum technologies.
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.
Chris Schwiegelshohn is an Associate Professor at the Department of Computer Science, Aarhus University, specializing in theoretical computer science and algorithm design. His research focuses on clustering algorithms, coresets, approximation algorithms, and fairness in machine learning. He has contributed significantly to the development of efficient algorithms for large-scale data analysis and privacy-preserving techniques. Key research interests include optimization in k-means clustering, distributed privacy protocols, and fair recommendation systems. His work bridges theoretical foundations with practical applications in data mining and machine learning. Publications span topics such as PAC learning for k-means, dynamic facility location, and fair projections for balanced recommendations. He has explored tradeoffs between computational efficiency and accuracy in big data clustering, as well as low-distortion clustering techniques for ordinal data. No scientific awards or grants are explicitly listed in the provided texts. His advising record remains unspecified.
Riko Jacob is an Associate Professor and Head of Section in Theoretical Computer Science at the IT University of Copenhagen. His research focuses on algorithms, complexity, sorting, differential privacy, and combinatorial optimization. He leads projects such as DIREC (Digital Research Centre Denmark) and DISTRUST (Distributed business process execution under partial trust), emphasizing interdisciplinary collaboration. Research Interests: Jacob's work spans algorithm design, competitive analysis, randomized algorithms, and matrix operations. His recent studies address bichromatic sorting, saddlepoint detection, and parallel sorting with comparison errors, contributing to foundational advancements in theoretical computer science. Projects: Jacob is the Principal Investigator (PI) in key initiatives including DIREC (2020–2025), focusing on digital research innovation, and DISTRUST (2020–2025), addressing secure distributed systems. He also contributed to SSS (Scalable Similarity Search) under EU funding. Awards: No scientific awards explicitly listed, though his extensive publication record reflects scholarly impact. Advising & Grants: Supervised 1 student (name unspecified). Secured funding through Innovation Fund Denmark and EU grants, totaling millions in research support. Labs/Teams: Affiliated with the Center for Information Security and Trust and collaborates on global networks in algorithmic research.
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.
Russa Biswas is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. Their research focuses on Large Language Models, Knowledge Graphs, and Natural Language Processing, with a particular emphasis on multilingual systems, cybersecurity implications, and improving LLM factuality. Biswas holds a PhD in Computer Science from the Karlsruhe Institute of Technology, a Master’s degree from Saarland University, and completed a postdoctoral fellowship at the Hasso Plattner Institute (2023-2024). Education: PhD in Computer Science, Karlsruhe Institute of Technology (Embedding Based Link Prediction for Knowledge Graph Completion) Master’s in Informatik, Saarland University Postdoctoral Research at Hasso Plattner Institute (2023-2024) Research interests include combating LLM hallucinations through knowledge-graph grounded evaluation (e.g., MultiHal dataset), quantifying LLM vulnerabilities, and enhancing factuality via scalable reasoning techniques. Their work addresses cross-lingual challenges, embedding inversion attacks, and typological analysis of linguistic systems. Recent publications explore topics like multilingual dataset creation, security implications of LLMs, and knowledge-graph completion. Biswas collaborates widely, with contributions to venues like AAAI, NAACL, and the Journal of Web Semantics.
Juan Manuel Rodriguez is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. His affiliation is within The Technical Faculty of IT and Design, specializing in Data, Knowledge, and Web Engineering. Dr. Rodriguez holds a PhD in Computer Science and has a strong focus on research areas including Recommender Systems, Web Services, Deep Learning, and Social Media Analysis. His work often intersects with applications in information retrieval, healthcare systems, and crisis management. His research explores topics such as misinformation detection, transformer-based news recommendation, and clinical data analysis using knowledge graphs. He has contributed to projects like HEREDITARY (2024–2027), which integrates heterogeneous data for gut-brain research. Rodriguez has also engaged in media collaborations, including work highlighted in 'Udforsker Mars: Studerende fra Aalborg samarbejder med NASA' (June 2024). Key technical interests include leveraging neural networks for user behavior modeling, optimizing retrieval systems, and addressing ethical challenges in AI. His recent publications (2020–2025) span domains from pandemic mental health tracking to parking availability prediction, reflecting a multidisciplinary approach to computational challenges.
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'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.
Prof. Yamir Moreno is a Professor of Physics at the Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, where he also serves as Director of the Institute for Biocomputation and Physics of Complex Systems (BIFI) and leads the Complex Systems and Networks Lab (COSNET). He holds international affiliations as Research Director at CENTAI Institute (Turin, Italy) and External Professor at the Complexity Science Hub Vienna (Austria), and was previously a Principal Scientist and Area Coordinator at the ISI Foundation. PhD in Physics, University of Zaragoza (Summa Cum Laude, 2000) Ramón y Cajal Fellow (2005-2010) First-class Fellow, ISI Foundation Fellow, American Physical Society (2021) Fellow, Network Science Society (2022) His research focuses on complex systems , network science , epidemic modeling , and social dynamics . He investigates disease dynamics, diffusion processes, nonlinear systems, and higher-order network structures using tools from physics, mathematics, and data science. His work bridges theoretical foundations with real-world applications in public health, urban systems, and social behavior. Recent publications highlight trends in multilayer and higher-order networks , digital twins of cities , epidemic modeling with behavioral feedback , and social contagion in complex systems . His research integrates data-driven modeling, machine learning, and network theory to study urban resilience, information spread, and collective behavior. Complex Systems Society’s Senior Scientific Award (2019) Highly Cited Researcher (Web of Science, 2019) Service Award, Complex Systems Society (2022) Editor-in-Chief, Journal of Complex Networks Editor, PLoS Computational Biology, Chaos, Solitons & Fractals, Proceedings of the Royal Society A Divisional Associate Editor, Physical Review Letters (2014-2020) Prof. Moreno has supervised 23 PhD theses and over 30 master’s theses. He has led multiple EU-funded projects and has served on advisory boards for the EU Future and Emerging Technologies and the WHO Collaborative Center on Complexity Sciences for Health Systems. His collaborative network spans institutions in Europe, Latin America, and North America. He leads the Complex Systems and Networks Lab (COSNET) at BIFI, a multidisciplinary team focused on network theory, computational epidemiology, and social dynamics. The lab develops models for epidemic forecasting, urban resilience, and collective behavior using high-performance computing and data analytics.
Natalie Schluter is a Lecturer in Theoretical Computer Science at the IT University of Copenhagen , affiliated with NLPnorth and Machine Learning Algorithms. Her research focuses on Natural Language Processing (NLP) , Machine Learning , and Data Science , with specific contributions to automatic summarization, cross-lingual inference, and dependency parsing. She has secured multiple research grants and actively engages in public discourse on gender equality in academia. Principal Investigator (PI) for projects like GOOGLELOWNLP (2020–2025) and Danish Language Inclusion (2019–2021). Key research areas: Danish language technology, neural networks, and syntactic modeling. Email: nael@itu.dk Her publications highlight trends in machine translation , graph-based parsing , and low-resource NLP , often leveraging deep neural networks. She has contributed to 18 publications and participated in organizing events like the Women in Data Science (WiDS) Copenhagen conference. Media contributions include advocacy for gender equality in IT and NLP.
Ramoni Ojekunle Adeogun is an Associate Professor at the Department of Electronic Systems, The Technical Faculty of IT and Design, Aalborg University. His research focuses on 6G wireless networks, AI-driven communication systems, and industrial automation, with expertise in channel modeling, propagation graph analysis, and IoT-based monitoring. Role: Project Leader for AI-Native User-Centric Air Interface for 6G (2023–2025) Collaborations: With G. Berardinelli, P. Mogensen, I. Rodriguez Research Highlights Advancing sub-millisecond latency in 6G networks Machine learning for IoT occupancy detection Polarimetric channel modeling via propagation graphs Dynamic channel allocation for industrial automation Publications Trends Over 85 publications spanning 2013–2025, with recent work on AI-native air interfaces, polarimetric channel modeling, and ultra-reliable low-latency communication for 6G. His studies integrate machine learning, stochastic modeling, and wireless propagation analysis. Grants & Projects Managing the EUR 6.8 million CENTRIC project (2025), focusing on AI-driven wireless innovation. Previously led interference prediction research (2019–2022) and contributed to IoT-based occupancy detection (2019).
Urška Šadl serves as Associate Professor at the Faculty of Law, University of Copenhagen, holding a promotion track to full professorship since 2023. She maintains a part-time professorship at the European University Institute in Florence since 2016. Her primary academic affiliation is with the Center for Global Mobility Law (MOBILE) where she coordinates the Legal Change work package examining law's response to external crises. Dr. Šadl earned her law degrees from the University of Ljubljana (BA and MA), an LLM from The College of Europe in Brugge, and completed her PhD at the University of Copenhagen in 2012 with the thesis The European Palimpsest: Justificatory practices and precedent construction at the Court of Justice of the European Union . Her academic journey includes guest researcher positions at King's College London, University of Oxford's Institute of European and Comparative Law, University of Michigan (as Grotius Scholar), and Columbia Law School. Her research fundamentally bridges European Union law with empirical methodologies, focusing on the internal market, free movement of persons, and European citizenship. She pioneers the integration of citation network analysis, machine learning, and judicial politics insights into legal scholarship, particularly examining the European Court of Justice's decision-making processes, precedent construction, and interactions with national courts. This interdisciplinary approach has positioned her at the forefront of empirical legal studies within EU law scholarship. Analysis of her recent publications reveals a consistent trajectory toward methodological innovation in legal analysis. Her work increasingly employs computational techniques to map legal evolution, particularly through citation networks and community detection algorithms applied to ECJ case law. The thematic focus remains centered on judicial behavior and institutional dynamics within the EU legal order, with growing emphasis on how external pressures transform legal interpretation and application. Sapere Aude Research Leader Grant (Danish Council for Independent Research) for the five-year project Judging Under the Influence Co-founder of the European Society for Empirical Legal Studies (ESELS) Co-coordinator of the IUROPA collaborative research project (Swedish Research Council grant) Co-editor of the Oxford Handbook of Comparative Judicial Behavior Dr. Šadl directs significant research initiatives including the Sapere Aude-funded Judging Under the Influence project that examines national courts, European Commission, and Member State governments' influence on ECJ interpretive techniques. She co-coordinates the IUROPA project investigating European Union agency networks. Her work with the European Society for Empirical Legal Studies has established important infrastructure for data-driven legal scholarship across Europe. Within the MOBILE Center for Global Mobility Law, she leads the Legal Change work package analyzing how law adapts to external crises. Her research group employs interdisciplinary methodologies combining legal analysis with network science and computational approaches to study institutional transformations in EU law.
Camilla Birch Okkels is a researcher at the Department of Theoretical Computer Science Algorithms at the IT University of Copenhagen. Her work focuses on high-dimensional data analysis, outlier detection, and nearest neighbor graph algorithms. Her research trends emphasize Scalable outlier detection methods Density-based clustering via locality-sensitive hashing Approximate nearest neighbor graph construction These efforts align with broader fields like machine learning and data mining.
