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Arthur ZimekView profile

Professor

• Outlier Detection
• Clustering Algorithms
• High Dimensional Data Analysis
• +3 more

Arthur Zimek is a Professor and Head of the Data Science section at the Department of Mathematics and Computer Science, University of Southern Denmark. He holds a Dr. rer. nat. habil. (Habilitation) from Ludwig-Maximilian University of Munich. His research focuses on outlier detection, clustering algorithms, high-dimensional data analysis, and machine learning. Zimek has held guest professorships at TU Vienna and has been a postdoctoral fellow at the University of Alberta. Education includes a Diploma in Bioinformatics, Magister Artium in Philosophy, and a Diploma in Theology, all from German institutions. His work emphasizes algorithm development and evaluation, with contributions to open-source tools like ELKI. He has received awards including the ACM SIGKDD 2017 Notable Article and the 2025 Author Service Award. Key research areas include clustering validation, semi-supervised learning, and interpretable machine learning. Projects involve cardiovascular disease prediction and synthetic data evaluation. He teaches courses on data mining, statistical learning, and spatiotemporal data analysis.

Marco ChiarandiniView profile

Associate Professor

• Operations Research
• Optimization Algorithms
• Integer Programming
• +10 more

Marco Chiarandini is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), with a secondary affiliation to the SDU Climate Cluster. His research focuses on Operations Research, Optimization Algorithms, and their applications in domains such as transportation, healthcare, and education. He has led or contributed to projects addressing dynamic traffic estimation, dementia care app design, and sustainable software development. Chiarandini has supervised students including P. Tampakis and C. McCarthy, and his work has been featured in interdisciplinary collaborations with institutions globally. His recent articles explore topics like goal-scoring prediction in football and urban traffic modeling, reflecting his expertise in data science and algorithmic design. His academic contributions span over 50 publications, emphasizing methodological advancements in optimization and heuristic algorithms. He actively participates in conferences and peer review, and has coordinated EU-funded projects like Data Science for University Management (2019–2023). Chiarandini’s teaching includes courses on linear programming and metaheuristics, integrating practical applications with theoretical foundations.

Peter Schneider-KampView profile

Professor

• Data Science
• Artificial Intelligence
• Machine Learning
• +5 more

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.

Charles VesteghemView profile

Associate Professor

• Artificial Intelligence in Clinical Contexts
• Personalized Medicine
• Health Data Infrastructure
• +4 more

Charles Vesteghem is an Associate Professor and Group Leader of Clinical AI at the Department of Clinical Medicine, Aalborg University, affiliated with Aalborg University Hospital. He holds a PhD in Clinical Data Science from Aalborg University (2022) and dual MSc degrees in Electrical Engineering from CentraleSupélec (2006) and KTH Royal Institute of Technology (2006). His research focuses on AI-driven solutions for clinical decision-making, personalized medicine, and health data infrastructure. He leads projects like the ARISTOTELES initiative for AI in atrial fibrillation management and has contributed to FAIR data standards in oncology. His work spans predictive modeling, electronic health record analysis, and translational research in oncology. Notable outputs include studies on precision medicine survival outcomes and NLP applications for cancer data extraction. Vesteghem collaborates widely with institutions like the Center for Clinical Data Science and has engaged in high-impact media discussions on AI ethics and healthcare innovation. He has authored over 30 peer-reviewed publications and datasets, including systematic reviews on NLP in healthcare and analyses of Danish cancer treatment registries. His contributions bridge clinical practice and computational methods, emphasizing real-world implementation of AI solutions.

Tung KieuView profile

Assistant Professor

• Data Science
• Time Series Analysis
• Forecasting
• +4 more

Tung Kieu is a Tenure Track Assistant Professor in the Department of Computer Science at Aalborg University (Denmark), affiliated with The Technical Faculty of IT and Design and the Daisy Center for Data-intensive Systems. His research focuses on data engineering, time series analysis, anomaly detection, and machine learning applications in traffic forecasting and smart systems. Education: Ph.D. in Computer Science (Awarded May 2021). Research interests include time series forecasting, traffic modeling, robust autoencoder architectures for anomaly detection, and spatio-temporal data analysis. His work contributes to UN Sustainable Development Goals related to smart cities and infrastructure. Recent publications explore bias mitigation in text-video retrieval (BiMa), topology-aware traffic forecasting (TEAM), and stochastic routing in uncertain road networks. His frameworks emphasize lightweight algorithms (LightTS), causal relational learning, and continual calibration for quantized models (QCore). Collaborations involve international teams in data management and AI, with notable work on ensemble methods, explainable AI, and transfer learning in smart building systems.

Edward AbelView profile

Associate Professor

• Computer Science
• Data Science
• Ethics in AI
• +5 more

Edward Abel is an Associate Professor in the Department of Design, Media and Educational Science at the University of Southern Denmark. His research focuses on computational approaches to decision-making, fairness, and explainability in AI systems, particularly in how these systems influence human choices. He explores technical algorithm design alongside ethical implications, emphasizing transparency and accountability. Abel holds a PhD in Computer Science and participates in external roles such as the Steering Committee of the eXplainable Artificial Intelligence Hub. Education : PhD in Computer Science His research interests span computational decision-making systems, recommender systems, and the intersection of computer science with humanities disciplines. He investigates how AI can be designed to align with user needs while addressing equity and interpretability challenges. Notable contributions include studies on fairness in pandemic policy decisions and the development of the VADA architecture for data wrangling. Abel’s work often bridges technical innovation with critical social inquiry, aiming to ensure AI systems are both effective and ethically robust. His recent projects include analyzing UK COVID-19 restrictions through dominance-based rough sets analysis and exploring diversity in music recommendation systems. He collaborates extensively with interdisciplinary teams and has published over 30 peer-reviewed articles. His datasets, such as the UK COVID-19 restriction dataset, are widely referenced in academic research and policy discussions.

• Computational Epidemiology
• Data Science
• Complex Systems
• +4 more

Daniela Paolotti is a Senior Research Scientist at the ISI Foundation in Turin, Italy, with a BSc, MSc, and PhD in Physics. Her interdisciplinary research integrates computational epidemiology, data science, complex systems, and behavioral sciences to model disease spread and social dynamics. She coordinates a Europe-wide participatory surveillance network for influenza-like illnesses (since 2008) and contributes to projects like PIPELINE (pregnancy/infant preparedness), SIESTA (secure data analytics), AWaRe (antimicrobial stewardship), and EpiPose (COVID-19 impact mitigation). Her work spans public health , digital epidemiology , and social impact data science , with emphasis on real-time disease tracking, behavioral interventions, and policy-informing models. Recent publications analyze COVID-19 clinical patterns, vaccine hesitancy on social media, climate change discourse, and food insecurity drivers. She leads collaborative initiatives such as Influcast (influenza forecasting), Respicast (respiratory disease modeling), and Influweb (participatory surveillance), leveraging big data and AI for global health challenges.

Elma DervicView profile

Research Fellow

• Machine Learning
• Network Science
• Data Science
• +9 more

Elma Dervic is a Postdoctoral Researcher at the Section for Science of Complex Systems at the Medical University of Vienna and a resident scientist at the Complexity Science Hub Vienna. Her work integrates data science, network science, and machine learning to address complex challenges in healthcare, human migration, and supply chain resilience. Research Interests: Machine Learning and Network Science for health data Modeling disease trajectories and comorbidity networks Systemic risk analysis in global supply chains Migration dynamics and refugee integration Agent-based modeling of healthcare systems Her recent research has focused on unraveling cradle-to-grave disease trajectories using population-wide health data and analyzing systemic risks in antibiotic and food supply chains. Her publications span high-impact journals such as Nature Communications , Nature Human Behaviour , and Translational Psychiatry . The articles reflect a strong interdisciplinary trend, combining medical data with network theory and machine learning to uncover hidden patterns in disease progression, gender disparities in health outcomes, and structural vulnerabilities in critical systems. Scientific Awards: Life Sciences Research Award 2024 – for breakthroughs in understanding disease trajectories Elma has been actively involved in advising and collaborative research, contributing to major projects such as ASCII (Austrian Supply Chain Intelligence Institute), D4Dairy, and MigraCapax. She has also secured research visibility through TEDx talks and media coverage in outlets like International Business Times and US Pharmacist. Her work on data transparency in medical research has led to the creation of open datasets, including the CSH Covid-19 Control Strategies List. Labs and Research Teams: Complexity Science Hub Vienna (CSH) Section for Science of Complex Systems, Medical University of Vienna Co-founder of BeeAnd.me – an IoT startup applying data science to beekeeping Core member of the Montenegrin AI Association (MAIA)

Ana María JaramilloView profile

Research Fellow

• Complex Systems
• Network Science
• Algorithmic Fairness
• +2 more

Ana María Jaramillo is a Research Fellow at the Complexity Science Hub in Vienna, Austria, where she has contributed to the MAMMOth project (Multi-attribute, Multimodal Bias Mitigation in AI Systems) since June 2023, focusing on algorithmic fairness and bias mitigation in AI systems. Her academic foundation includes a PhD in Computer Science from the University of Exeter, where she investigated the effects of segregation and diversity on scientific impact through computational sociology of science frameworks. Dr. Jaramillo's research integrates complex systems theory with network science to model social behaviors across science, education, and public health domains. She develops data-driven interventions to build equitable academic networks, with core objectives centered on advancing cognitive justice and democratizing knowledge access. Her methodological expertise lies in network analysis of behavioral patterns and systemic biases. Her publication record reveals a consistent trajectory in algorithmic fairness and science-of-science applications. The 2025 hyperFA*IR framework innovates fair ranking for constrained candidate pools using hypergeometric statistics, while her 2023 co-authorship network study empirically demonstrates how segregation patterns suppress scientific productivity. These works collectively bridge computer science, sociometrics, and ethical AI design. No scientific awards were documented in the source materials. Available information does not specify student supervision activities or grant funding details. She actively participates in the MAMMOth project team under Fariba Karimi's leadership, contributing to both technical development and public outreach initiatives including the MAMMOth Talks podcast series. Her March 2024 CSH Salon presentation on historical gender disparities in science and her International Women's Day commentary further demonstrate her commitment to structural equity in academic ecosystems.

• Artificial Intelligence
• Educational Technology
• Learning Analytics
• +3 more

Euan D. Lindsay, a researcher at Aalborg University in Denmark, focuses on the intersection of artificial intelligence and educational technology. His work explores the transformative potential of generative AI in academic feedback systems, learning analytics, and problem-based learning environments. A recurring theme in his research is the ethical and practical implications of automating educational processes. Research Interests: Lindsay’s scholarship spans Artificial Intelligence , Educational Technology , and Learning Analytics , with specific attention to generative AI applications, automated assessment tools, and pedagogical innovation in problem-based learning. He also investigates systemic reforms in higher education, particularly through the lens of academic leadership in Poland. Recent Work: In 2025, Lindsay published on generative AI ethics , Polish higher education reform , and student perspectives in AI-integrated classrooms. Earlier in 2024, his studies concentrated on automated assessment systems , learning analytics dashboards , and large language models for feedback . These contributions highlight his commitment to leveraging AI for scalable, data-driven educational improvements.

Johannes WachsView profile

Associate Professor

• Network Science
• Data Science
• Complexity Science
• +14 more

Johannes Wachs is an Associate Professor at the Institute of Data Analytics and Information Science, Corvinus University of Budapest, and a Research Fellow at the Centre for Economic and Regional Studies. He is affiliated with the Complexity Science Hub Vienna, where he has been a faculty member since April 2020. His interdisciplinary research bridges data science, network science, and complexity to study digital economies, open source software, and societal challenges. PhD in Network Science, Central European University (2019) MS in Applied Mathematics, Central European University (2012) BS in Mathematics and Economics, Tulane University (2009) His research focuses on the application of network and data science to understand social, economic, and technical systems. Key interests include open source software ecosystems, AI’s impact on knowledge sharing, corruption detection, urban inequality, and digital innovation. He uses large-scale digital trace data to model complex behaviors in online communities, software development, and public policy. His recent publications reveal a strong trend in analyzing digital platforms such as GitHub and Stack Overflow, studying brain drain in tech, and assessing climate and health risks in Austria. His work combines network modeling, machine learning, and empirical analysis to uncover patterns in human behavior and systemic risk. IMF Anti-Corruption Challenge Winner (2020) Principal Investigator, CRISP Project (2021–2024), funded by FFG Grants from Hungarian Research Funding Agency (OTKA), City of Vienna, and WU Projects Johannes Wachs actively supervises PhD and Master’s students, including Hannah Schuster and Brigi Németh. He has taught courses in computational social science, social networks, and data mining at institutions including RWTH Aachen, CEU, and WU Vienna. He is launching a new MSc in Social Data Science at Corvinus in 2025. He leads the CRISP project, which builds semantic data pools for real-time crisis response and intervention, integrating heterogeneous data sources for impact forecasting and policy transparency.

Michele CosciaView profile

Associate Professor

• Network Science
• Complex Networks
• Digital Humanities
• +10 more

Michele Coscia is an Associate Professor in the Department of Computer Science at the IT University of Copenhagen (ITU), where he conducts research at the intersection of network science, digital humanities, and data analytics. He leads the NERDS research group and supervises PhD students and postdoctoral researchers working on financial crime detection, archaeological networks, and work environment modeling. PhD in Computer Science, University of Pisa (2012) Former researcher at the Center for International Development (CID), Harvard University (6 years) Visiting researcher at Barabási Lab, Northeastern University His research focuses on developing and applying network science methodologies such as noise-corrected backboning , node attribute analysis , and network variance to study complex systems. His work spans diverse domains including: Archaeology : Inferring social and biological relationships from material culture at Neolithic sites like Çatalhöyük. Cultural Analytics : Mapping Italian music networks, analyzing Wikipedia’s gender bias, and studying ideological polarization on social media. Social Media Dynamics : Investigating meritocracy vs. topocracy, intolerance feedback loops, and information virality on platforms like Reddit and Twitter. Sports Analytics : Analyzing predictability trends in team sports and the impact of economic systems on league competitiveness. His publications appear in high-impact journals such as Science Advances , EPJ Data Science , and Applied Network Science . He is the author of The Atlas for the Aspiring Network Scientist , a comprehensive open-access textbook now in its second edition, which covers graph theory, machine learning on graphs, and statistical foundations of network analysis. Recent trends in his work show a growing emphasis on interdisciplinary applications of network science, particularly in archaeology and cultural studies, often in collaboration with institutions such as Aarhus University and the National Research Center for Work Environment. His research consistently promotes open science, with datasets and code publicly shared. Co-PI on a Villum Synergy project applying network analysis to Roman Empire archaeological data Active contributor to the CUDAN (Cultural Data Analytics) community Developing methods for uncertain and incomplete network data Michele Coscia’s work demonstrates a strong commitment to methodological innovation and real-world impact across the humanities, social sciences, and computational domains.

• Sexology
• Gender Studies
• Behavioral Sciences
• +3 more

Christian Graugaard is a Professor of Sexology at Aalborg University, affiliated with the Faculty of Medicine and the Department of Clinical Medicine. He is a key member of the Center for Sexology Research and leads Project SEXUS, aiming to study Danish sexual behavior comprehensively. His work emphasizes the intersection of biological, psychological, and cultural factors in human sexuality, challenging simplistic gender-based stereotypes. Research interests include gender differences in sexual behavior, societal norms influencing sexual health, and the cultural dimensions of human sexuality. He actively participates in public discourse, as seen in his DR-podcast interview on 'Ramt af kærlighed,' where he discussed the complex interplay between biology and culture in shaping sexual identities. Though no specific awards or grants are detailed here, his publications span advanced technical domains like spatiotemporal data analysis, federated learning, and trajectory modeling, suggesting interdisciplinary research collaborations. His work on systems like OneDB and SWASH highlights contributions to distributed computing and data science, which may underpin his methodologies in large-scale sexual behavior studies. He currently holds no listed students or formal advisees in the provided texts, and his involvement in labs/teams is limited to the Center for Sexology Research and Project SEXUS.

• Material Flow Analysis
• Remote Sensing
• Emission Reduction
• +6 more

Kun Sun is a postdoctoral researcher at the Department of Green Technology (IGT) within the Faculty of Engineering at the University of Southern Denmark. His work intersects machine learning, remote sensing, and urban sustainability, focusing on material flow analysis and environmental impact assessment. Research Interests: Active in material flow analysis, remote sensing applications, and emission reduction strategies, Kun Sun's research combines deep learning techniques with environmental science to address urban sustainability challenges. His recent projects include building material identification and sociometabolic transitions in Eastern Europe. Scientific Awards: Contributed to the project "Exploring the green transformation of the Danish pork supply chain and its contribution to carbon neutrality" (2025). Projects: Participates in ongoing research analyzing the Danish pork supply chain's role in achieving carbon neutrality. This work emphasizes circular economy strategies and industry transformation. Media Contributions: Has been featured in three 2025 press releases discussing AI applications for building material recycling, climate benefits in AI, and scanning technologies for urban sustainability.

Ali KhosraviView profile

Associate Professor

• Machine Learning
• Energy Systems Analysis and Optimization
• Power-to-X Technologies
• +7 more

Ali Khosravi is an Associate Professor at the University of Southern Denmark (SDU) , affiliated with the Institute of Mechanical and Electrical Engineering and SDU Mechatronics (CIM) . His work focuses on integrating machine learning with energy systems, particularly in the areas of power-to-X technologies, renewable energy optimization, and thermodynamic systems. Research Interests: Machine learning applications in energy systems Power-to-X technologies (methanol, hydrogen) Thermodynamic analysis and optimization Renewable energy integration and storage Solid oxide fuel cells (SOFC) and electrolysis systems His recent publications span high-impact journals such as Energy , Renewable Energy , and International Journal of Hydrogen Energy , focusing on topics like methanol production from CO₂ and solar hydrogen, stochastic modeling for energy systems, and AI-driven optimization of fuel cell systems. Scientific Awards: Villum Young Investigator Grant (2023) Listed among World's Top 2% Scientists in Energy (2021, 2022) Postdoctoral Fellowship Award (2018) PhD Scholarship Award (2016) Teaching & Supervision: Course instructor for "Mechanical Semester Project 3" Supervisor for Special Course in Mechanical Engineering PhD supervision: M. Lautenschläger, M. Taslimi, N. Mirzakhani, L. Xia, and others Research Labs & Teams: He leads the DDPTX: Deciphering the Dynamics of Power-to-X Systems project (2024–2029), funded by Villum Fonden, and actively participates in several international collaborations including the Denmark & Australian Partnership on Green Hydrogen Value Chains and GREMEOH-Power-to-X.