Aristides Gionis is a Research Professor Fellow at Aalto University specializing in advanced network analysis and data mining. His work bridges theoretical graph algorithms with practical applications in dynamic and signed networks. His research focuses on graph theory and temporal network analysis , particularly in discovering dense subgraphs, community structures, and event patterns in evolving networks. Key methodologies include contrastive subgraph mining for explainable AI in brain networks and polarization detection in social systems. His publication trends reveal consistent contributions to top-tier venues like KDD, PAKDD, and ECML PKDD, with emphasis on algorithmic solutions for network segmentation, correlation mining, and interpretable classification. Primary application domains include social networks, neuroscience, and temporal data streams. As a Fellow at Aalto University, he leads research in computational network science without explicit departmental affiliation stated in available sources.
Paul Cosma is a Postdoctoral Researcher at the Department of Computer Science (DIKU), University of Copenhagen, specializing in the Software, Data, People & Society (SDPS) section. His work focuses on declarative process modeling, formal verification, and explainable AI systems, with strong connections to process mining and Petri net theory. He completed his PhD at the University of Copenhagen's Faculty of Science in 2024 with a thesis on declarative process models as verifiable AI. His research interests center on declarative process modeling and formal verification of complex systems. Cosma develops techniques for improving model simplicity through nested group discovery and creates frameworks like BERMUDA for participatory mapping of domain activities to event data. His work bridges theoretical computer science with practical applications in business process management and AI explainability, emphasizing human-centered design principles where software systems are developed with societal impact in mind. Cosma's publication record shows a clear trajectory in process modeling research, with recent work focusing on transforming Dynamic Condition Response Graphs to Safe Petri Nets (2023) and improving declarative model simplicity (2024). His research demonstrates strong interdisciplinary connections between formal methods, AI verification, and human-computer interaction, particularly in making complex process models accessible and verifiable. Cosma actively collaborates with researchers including Thomas Hildebrandt, Tijs Slaats, and Axel Christfort, primarily within the SDPS section at DIKU. His work receives consistent citations in process mining literature, with his 2023 PETRI NETS paper accumulating 1 citation and his CAiSE 2024 paper gaining 2 Scopus citations. He maintains an ORCID profile (0000-0001-8022-6402) and contributes to open-access research through the university's Pure repository. Based in Sigurdsgade 41, Copenhagen N, Cosma operates within DIKU's collaborative research environment that emphasizes industry partnerships and interdisciplinary work. His recent PhD defense (June 13, 2024) marks his transition from doctoral candidate to postdoctoral researcher, positioning him to expand his contributions to process-aware information systems and verifiable AI.
Jens Petersen is an Associate Professor at the Department of Computer Science , University of Copenhagen, specializing in medical image analysis. He works within the Image Analysis, Computational Modelling, and Geometry research section. Education: B.Sc. (2005), M.Sc. (2010) in Computer Science from University of Copenhagen; Ph.D. (2014) in Medical Image Analysis from University of Copenhagen with visiting researcher experience at University College London. Research Interests: Focus on medical image analysis techniques for segmentation and statistical analysis of tubular/branching structures like airways and carotid arteries. Key methodologies involve graph cuts, optimal surface methods, and computational modeling. Current research includes AI-guided tumor delineation, longitudinal image synthesis, and physics-based deformable registration. Publications Trends: Recent work spans AI applications in oncology, computational methods for cardiovascular imaging, and algorithm development for radiation therapy. Collaborations include international institutions and cross-disciplinary teams in machine learning and medical imaging.
Jacob Holm is a Tenure Track Assistant Professor in the Department of Computer Science at the University of Copenhagen, specializing in the Algorithms and Complexity research section. His work focuses on theoretical computer science with emphasis on graph algorithms and data structures. Dr. Holm's research interests span multiple areas of theoretical computer science: Dynamic graph algorithms, particularly for planar graphs Biconnectivity and triconnectivity in dynamic settings Efficient data structures for graph problems Parallel and distributed algorithms for graph processing Computational geometry and pursuit-evasion problems His publication record shows 25 research outputs including 17 article in proceedings, 6 journal articles, 1 book chapter, and 1 Ph.D. thesis. His work demonstrates consistent contributions to theoretical computer science, with numerous publications in top venues like the ACM-SIAM Symposium on Discrete Algorithms (SODA). Analysis of his recent publications reveals a strong focus on worst-case performance guarantees for dynamic graph problems, particularly in planar graph settings where maintaining efficiency during updates presents significant theoretical challenges. Dr. Holm maintains an active research profile with an ORCID identifier (0000-0001-6997-9251) and collaborates extensively with researchers in the theoretical computer science community, particularly with Eva Rotenberg as evidenced by multiple co-authored publications. His work bridges theoretical computer science with practical applications, developing algorithms that maintain efficiency even as graphs dynamically change.
Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.
Markus Strohmaier is Professor and Chair of Data Science in the Economic and Social Sciences at the University of Mannheim, with affiliations as Scientific Coordinator at GESIS – Leibniz Institute for the Social Sciences and External Faculty Member at the Complexity Science Hub Vienna. His interdisciplinary work bridges computer science, economics, and the social sciences. University of Mannheim – Chair for Data Science in the Economic and Social Sciences GESIS – Scientific Coordinator for Digital Behavioral Data Complexity Science Hub Vienna – External Faculty Former Professor at RWTH Aachen University and University of Koblenz-Landau Previous Post-Doc and Visiting Roles at Stanford University, Xerox PARC, University of Toronto, and Graz University of Technology His research focuses on computational social science , algorithmic fairness , network science , and the modeling of human behavior using machine learning and large-scale data. He develops methods to analyze textual, relational, and emerging data types to understand socioeconomic systems and digital societies. The recent articles reflect a strong trend in studying inequality in algorithmic systems , governance in decentralized organizations (DAOs) , and psychological profiling of AI . His work spans high-impact journals like Nature and Scientific Reports , emphasizing fairness, transparency, and societal impact of data-driven technologies. Notable scientific contributions include: Editor-in-Chief of EPJ Data Science (2018–2022) Founding co-chair of the Computational Social Science section of the German Informatics Society He advises students and leads research projects on algorithmic fairness, digital governance, and behavioral modeling. His team engages in both fundamental methodological development and applied studies in real-world digital platforms. He has been involved in significant grants and collaborative initiatives around digital behavioral data and computational social science infrastructure. His lab and projects include the Algorithmic Fairness initiative and the interactive visualization tool Planets of Disparity , which explores how algorithms behave on different network structures. These efforts aim to enhance public understanding and technical scrutiny of algorithmic systems.
Siddharth Bhaskar is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark. His research lies at the intersection of theoretical computer science and mathematical logic, with a focus on programming language foundations and formal structures. His research interests include structured programming, imperative languages, graph traversal algorithms, and the application of category theory (particularly functors) to computation. These areas reflect a deep engagement with the mathematical underpinnings of programming and algorithmic processes. The two recent publications show a consistent trend in formalizing programming concepts through mathematical structures—particularly using universal constructions from category theory and extending structured programming into transfinite domains. This indicates a research trajectory grounded in computability, logic, and the semantics of programming languages. No scientific awards mentioned in the provided text. Siddharth Bhaskar has no listed advisees or grant information in the available data. However, his peer-reviewed publications in high-quality conference proceedings (CiE and MFCS) suggest active research supervision and potential involvement in collaborative projects, though specific details are not disclosed. There is no mention of specific laboratories, research groups, or teams in the provided text. However, his affiliation with the Department of Mathematics and Computer Science and his research profile suggest potential collaboration within formal methods or logic research circles at the University of Southern Denmark.
Ivor van der Hoog serves as an Assistant Professor specializing in Theoretical Computer Science. His institutional affiliation remains unspecified in the provided text, though his departmental focus is clearly centered on foundational computational theory within an academic setting. His research spans core domains of Theoretical Computer Science including Algorithms, Computational Geometry, and Data Structures, with significant emphasis on Discrete Mathematics and Graph Theory. This work addresses fundamental problems in computational efficiency, geometric modeling, and network analysis, contributing to the mathematical underpinnings of computer science through rigorous theoretical frameworks. No scientific awards or honors were documented in the available materials. Information regarding academic advising responsibilities, research grants, laboratory affiliations, or collaborative teams was not present in the current text excerpt, indicating these details require further verification through additional sources.
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 .
Yurij Holovatch is a Professor and Chief Researcher at the Institute for Condensed Matter Physics (ICMP) of the National Academy of Sciences of Ukraine in Lviv, where he founded the Laboratory for Statistical Physics of Complex Systems. He is a co-founder and co-director of the L4 collaboration and the International Doctoral College in Statistical Physics of Complex Systems, linking ICMP with the Universities of Leipzig (Germany), Coventry (UK), and Lorraine (France). He is also a full member of the National Academy of Sciences of Ukraine. Research Interests: His work focuses on phase transitions and critical phenomena in structurally disordered magnets, scaling of macromolecules and conformational properties of complex polymers, complex networks (ordering, stability, spreading), and increasingly extends into digital humanities and human migration . His research bridges theoretical physics with data-driven modeling of social and urban systems. Publication Trends: His recent publications (2023–2024) reveal a sustained focus on critical behavior in disordered systems using Monte Carlo simulations, exact and asymptotic analysis of models like the Potts and Blume-Capel models, and innovative applications of statistical physics to collective decision-making, transportation networks, and migration. These works reflect a strong interdisciplinary trend, integrating physics-based modeling with data analytics and social science questions. Davydov Prize for studies in theoretical and biological physics (2020) Honorary Ambassador of Lviv (2020) Visiting Professor (Honorary), Coventry University (since 2018) Advising and Grants: As co-director of the International Doctoral College, he plays a central role in training PhD students in statistical physics across Ukraine, Germany, UK, and France. He organizes the annual Ising Lectures workshop in Lviv since 1997, fostering international collaboration. He serves as editor of the book series Order, Disorder and Criticality (World Scientific), with Volume 7 published in 2023. Labs and Teams: He founded and leads the Laboratory for Statistical Physics of Complex Systems at ICMP Lviv. He co-directs the L4 collaboration and the International Doctoral College, which function as cross-institutional research and educational networks integrating theoretical and computational physics across Europe.
Eva Rotenberg 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 section. Her research is centered on theoretical computer science, particularly algorithms, data structures, and graph theory. Research Interests: Her work focuses on graph algorithms , especially in planar and dynamic graphs, data structures for efficient computation, and combinatorial optimization . Key topics include approximation algorithms , worst-case analysis , edge connectivity , and local density estimation in distributed settings. She investigates algorithmic solutions with strong theoretical guarantees. The recent publications (2024–2025) reveal a consistent focus on graph-theoretic problems in discrete algorithms, with applications in dynamic and distributed systems. Trends include adaptive data structures, sorting via partial orders, and connectivity augmentation in geometric graphs. Scientific Awards: No specific awards listed in the provided text. Advising and Grants: She is the main supervisor for multiple active PhD projects at DTU, including Dynamic Graph Algorithms , Combinatorial Algorithms on Graphs and Geometry , and Hierarchical Compression of Highly-Repetitive Data . These projects indicate successful grant acquisition and leadership of a vibrant research group. Her supervision spans theoretical algorithms and their applications in data compression and network analysis. Labs and Teams: She is a core member of the Algorithms, Logic and Graphs group at DTU, which conducts fundamental research in discrete mathematics and theoretical computer science. This team actively publishes in top venues and collaborates on national and international projects.
Kathrin Kirchner is an Associate Professor at the Department of Engineering Technology and Didactics at DTU. Her research focuses on transformative effects of digital technologies in workplaces, including AI, data science, and hybrid work dynamics. She explores how these technologies impact employee well-being, organizational strategies, and business models. Currently, she designs courses on analytics, leadership with data, and AI adoption in organizations. She has held editorial roles at the International Journal of Workplace Health Management and Electronic Markets. Kathrin is also a Board member of the Scandinavian Academy of Industrial Engineering and Management (ScAIEM). Her education includes a PhD in Information Systems from Friedrich Schiller University Jena (Germany), with postdoctoral work in healthcare process mining. She previously taught at the Berlin School of Economics and Law. Research interests span digital platforms, knowledge work, algorithmic management, and virtual collaboration. Notable projects include CVEinAI (Critical Virtual Exchange in AI) and studies on AI’s impact on managerial work. Awards include Researcher of the Year 2024 and ICIS 2023 Best Paper (Runner-Up). Teaching includes master’s courses like “From Analytics to Action” and bachelor-level “Innovation Pilot.” She supervises PhD students and advises on AI implementation challenges. Her work aligns with UN SDGs related to quality education and decent work.
Hans Martin Kjer is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the UltraSound and Biomechanics group within the Visual Computing Center and the Center for Fast Ultrasound Imaging. His research bridges engineering and medical imaging, with a strong emphasis on developing and validating advanced ultrasound techniques for biomedical applications. Research Interests: His work focuses on super-resolution ultrasound imaging, microvascular analysis, 3D reconstruction of biological structures, and image registration. He applies computational methods to improve the resolution and accuracy of ultrasound, particularly in renal and lymph node vasculature imaging. His research contributes to the UN Sustainable Development Goals in health and well-being through innovative diagnostic tools. Publication Trends: Over the past several years, Kjer has consistently published in high-impact journals and conferences in biomedical engineering and imaging. His recent work emphasizes the validation of super-resolution ultrasound against micro-CT, realistic 3D blood flow simulation, and the application of AI in enhancing imaging resolution. These studies reflect a strong trend toward quantitative, reproducible, and clinically relevant imaging solutions. Scientific Contributions: While no specific awards are listed, his leadership in major research projects and frequent collaborations with leading experts in ultrasound (e.g., Jørgen Arendt Jensen) underscore his significant role in the field. Advising and Funding: Kjer serves as a supervisor and principal investigator in several funded research initiatives, including AI for Extreme Super-Resolution CT , 3DIM: 3D Imaging Center , and QIM: Center for Quantification of Imaging Data from Max IV . He mentors PhD students and collaborates across disciplines, contributing to both biomedical and materials science imaging projects. Laboratories and Teams: He is an integral member of the Center for Fast Ultrasound Imaging and the Visual Computing Center at DTU. These teams focus on cutting-edge ultrasound technologies, image processing algorithms, and multimodal imaging integration, positioning Kjer at the forefront of computational biomedical imaging in Denmark.
Petra Hermankova serves as an Assistant Professor in Classical Archaeology at Aarhus University's School of Culture and Society, where she also contributes to the Social Resilience Lab. Her academic appointment is active with ongoing projects through 2025 and recent publications in 2025 confirm her current faculty status. Her research focuses on digital epigraphy and the application of computational methods to ancient inscriptions. She employs text mining, machine learning, and social network analysis to study inscription functions across Graeco-Roman societies, economic history patterns, and labor specialization. Her work emphasizes FAIR and Open Science principles in archaeological data management, particularly through leadership in the Epigraphy.info initiative which develops international standards for digital epigraphy. Hermankova's publication trends reveal increasing engagement with computational approaches to ancient social networks and epigraphic big data , with recent work integrating SPARQL querying, RDF modeling, and network visualization techniques. Her research bridges traditional archaeological methods with cutting-edge digital humanities tools, focusing on Mediterranean communities from Thrace to the Roman Empire. She actively participates in major collaborative projects including The Past Social Networks Project (Carlsberg Foundation, 2022-2025), PPAP: Perachora Peninsula Archaeological Project (2019-2024), and SDAM: Small data - Big Challenges (2019-2023). Hermankova regularly organizes international workshops through Epigraphy.info and teaches practical courses in digital epigraphy methodology.
Christina Lioma is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen . She has held academic positions including Associate Professor (2014-2018) and Freja Fellow/Assistant Professor (2012-2013) at the same institution. M.Hons (University of Glasgow, 2001) M.Sc. (University of Manchester, 2003) Ph.D (University of Glasgow, 2007) Her research focuses on Information Retrieval , Text Analytics , and Recommender Systems within Applied Machine Learning and Natural Language Processing . Recent work examines fairness-relevance tradeoffs in recommendation systems and neural mechanisms for knowledge conflict tracing. Recent publications in Nature Communications and top conference proceedings (WWW, SIGIR) explore hybrid computation architectures, brain-based language generation, and fairness evaluation metrics. She actively participates in academic conferences as organizer and speaker, including the European Conference on Information Retrieval.
