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Kurt NørmarkView profile

Associate Professor

• Computer Science
• Programming Languages
• Object-Oriented Programming
• +8 more

Kurt Nørmark is an Associate Professor in the Department of Computer Science at Aalborg University, under The Technical Faculty of IT and Design, Denmark. He is actively engaged in research and teaching, particularly in the areas of programming education and programming language design. His research interests focus on Object-Oriented Programming , Programming Languages , Program Understanding , and Educational Technology in introductory programming. He has explored the use of video and digital tools to enhance student learning in programming courses, contributing to modern pedagogical approaches. The recent publications reflect a consistent trend in computing education , emphasizing gradual abstraction , program documentation , and student engagement through multimedia. These works are primarily situated within the broader fields of Computer Science Education and Educational Technology. Kurt Nørmark has participated in educational research projects, including the PBL Exchange initiative, which supports Problem-Based Learning through IT platforms. While no formal list of advisees or awards is provided, his contributions to curriculum development and digital teaching tools indicate active involvement in academic mentoring and educational innovation. He is associated with research initiatives aimed at improving teaching methodologies in computer science, particularly through the integration of videos and digital platforms in classroom settings.

Søren KnudsenView profile

Assistant Professor

• Information Visualization
• Human-Computer Interaction
• Data Visualization
• +3 more

Søren Knudsen is an Assistant Professor at the Digital Design Department, IT University of Copenhagen and holds a Marie Curie Fellowship for postdoctoral work at the University of Copenhagen. He serves as Academic Director of the Human Data Interaction Lab. Positions: Assistant Professor, Marie Curie Postdoctoral Scholar Affiliations: IT University of Copenhagen, University of Copenhagen Key Roles: Academic Director, Human Data Interaction Lab His research centers on information visualization and human-computer interaction , particularly in face-to-face and online contexts. He explores how large-area displays and social media can enhance data comprehension and collaborative discussion. Notable work includes surveys on interactive visualization techniques for large displays, frameworks for data visualization education , and EMR-based algorithms for healthcare performance evaluation. His publications span journals like IEEE Transactions on Visualization and Computer Graphics and PLOS ONE . Awards and honors: Marie Curie Fellowship He collaborates across disciplines, with ties to healthcare analytics, educational technology, and computer science departments. His recent studies address challenges in visualization design handoff , proxemics , and ubiquitous data interaction .

• Machine Learning
• Probabilistic Inference and Reasoning
• Stochastic Processes
• +1 more

Christoffer Olling Back is a Postdoctoral Researcher at the Department of Computer Science (Faculty of Science, University of Copenhagen) specializing in Human-Centred Computing . His work bridges applied and theoretical machine learning, focusing on probabilistic inference, stochastic processes, and computability theory. Education PhD in Computer Science (2017-2020) - University of Copenhagen MSc in Artificial Intelligence (2010-2011) - University of Edinburgh BA in Psychology (w/ Computer Science) (2004-2008) - Lewis and Clark College Current research explores predictive workflow models using location data through the iAware project (collaboration with Systematic, PowerNet, and Bispebjerg Hospital). Previous work investigated ERP system datasets in the DIREC consortium. His publications span topics in process mining, probabilistic modeling, and healthcare informatics. Recent achievements include 15 research outputs (2024-2016) covering process discovery, workflow simulation, and entropy-based log analysis. Collaborations with institutions like Roskilde University and industry partners demonstrate interdisciplinary impact. Scientific Awards Dean's List (2007) Nordea Fonden Scholarship (2010) As an educator, he serves as guest lecturer, assistant teacher, and tutor in computer science, machine learning, and software engineering. His professional background includes industry roles at ServiceNow Denmark ApS (2021-2024) and Gekkobrain (2020-2021).

• Natural Language Processing
• Large Language Models
• Bias Detection
• +3 more

Zain Muhammad Mujahid is a PhD Fellow at the Department of Computer Science , University of Copenhagen (UCPH). His research focuses on Natural Language Processing with emphasis on Large Language Models (LLMs), bias detection, and fact-checking methodologies. Research Interests Factuality and bias prediction in news media LLM evaluation and error analysis Cross-lingual fact-checking systems Arabic-centric language modeling Evidence attribution in summarization AI safety in multilingual contexts Publications Zain's recent work addresses critical challenges in trustworthy AI, including automating error detection in NLG systems, developing cross-lingual bias detection frameworks (SAFARI), and creating benchmarks like Factcheck-Bench for evaluating automatic fact-checkers. His research also explores bilingual safety evaluation in Kazakh-Russian contexts and cultural adaptation of LLMs for Arabic language processing.

Andrea PaudiceView profile

Assistant Professor

• Machine Learning
• Optimization
• Robust Learning
• +3 more

Andrea Paudice is an Assistant Professor in the Department of Computer Science at Aarhus University. Their primary affiliation is with the Department of Computer Science, located at Åbogade 34, Building 5335, Room 317 in Aarhus N, Denmark. Paudice's research focuses on theoretical and algorithmic aspects of machine learning, optimization under uncertainty, adversarial robustness, and clustering methods. Research interests include developing robust statistical learning frameworks for heavy-tailed distributions, designing optimization algorithms with provable guarantees in stochastic settings, and exploring active learning strategies for efficient label usage. Recent work emphasizes high-probability bounds for stochastic methods, median-of-means techniques, and zeroth-order optimization under budget constraints. Publications span topics like adversarial noise mitigation, margin-based active learning, and exact cluster recovery via oracle queries. While no specific grants or awards are listed, their work demonstrates contributions to foundational machine learning theory and algorithmic robustness. No lab affiliations or student advising information is included in the provided text.

Ira AssentView profile

Professor

• Machine Learning
• Data Mining
• Clustering Algorithms
• +5 more

Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.

Tiziana Di MatteoView profile

Professor

• Econophysics
• Complex Systems
• Complex Networks
• +7 more

Tiziana Di Matteo is a Professor of Econophysics in the Department of Mathematics at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. She is also External Faculty at the Complexity Science Hub and affiliated with the UCL Centre for Blockchain Technologies, the Complex System Laboratory, the Complex Systems Society, and the Museo Storico della Fisica e Centro Studi e Ricerche “E. Fermi”. Her research focuses on econophysics, complex systems, complex networks, and data science , with applications in financial modeling, systemic risk, and multiscaling analysis. She integrates methods from statistical physics and network science to analyze financial markets and economic interactions. The most recent publications reveal a strong trend in financial network modeling, multiscaling volatility, tensor-based learning for multidimensional data, and early warning systems using scaling properties . Her work bridges econophysics and traditional financial economics, emphasizing empirical validation and interdisciplinary approaches. Scientific Leadership and Editorial Roles: Editor-in-Chief, Journal of Advances in Mathematical Physics Main Editor, Physica A Editor, European Physical Journal B Editor, Artificial Intelligence in Finance Former Editor-in-Chief, Journal of Network Theory in Finance Guest Editor for multiple special issues Tiziana is the co-founder of the Econophysics Network and has served as a consultant for the Financial Services Authority, hedge funds, and financial companies. She has authored over 100 papers and delivered keynote talks globally. She supervises PhD students and leads collaborative research in complex financial systems. She is actively involved in interdisciplinary research collaborations with institutions like the Francis Crick Institute and industrial partners including EDF Research and Unilever.

Miroslav MirchevView profile

Academic

• Network Science
• Machine Learning in Graphs
• Digital Disinformation Detection
• +7 more

Miroslav Mirchev is a postdoctoral researcher at the Complexity Science Hub in Vienna, Austria, and a professor at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, North Macedonia. His work bridges network science, machine learning, and digital disinformation detection. Research Interests: His primary research focuses on network science and machine learning in graphs, particularly in modeling and detecting digital disinformation in online social networks. He also investigates applications in digital currency ecosystems, portfolio optimization, and algorithmic fairness. His interdisciplinary work spans computer science, finance, and complex systems. The recent articles highlight a strong trend toward applying graph-based AI methods to financial systems and disinformation detection. Key themes include the use of graph neural networks for company classification, network-based portfolio diversification in crypto and stock markets, and privacy-preserving synchronization in dynamical networks. These works reflect a convergence of AI, network theory, and real-world socio-technical systems. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: While no direct advisees are listed, Miroslav Mirchev contributes to major research initiatives such as DesInfact (disinformation detection) and AMALFI (machine learning in cryptoasset graphs). He collaborates with researchers across institutions, indicating active involvement in funded research projects, though specific grants are not detailed. Labs and Teams: He is affiliated with the Complexity Science Hub and participates in research projects including DesInfact and AMALFI, focusing on disinformation and digital currency ecosystems. These teams involve interdisciplinary collaboration with experts in AI, network science, and financial systems.

Jørgen VilladsenView profile

Associate Professor

• Logic
• Artificial Intelligence
• Multi-Agent Systems
• +5 more

Roles and Affiliations: Jørgen Villadsen is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Department of Applied Mathematics and Computer Science and the Algorithms, Logic and Graphs Section. He serves as Head of Study and focuses on formal logic, multi-agent systems, and computer science education. Education: MSc in Computer Science and Engineering (1989, DTU) and PhD in Computer Science (1995, DTU). Research Interests: Villadsen’s work centers on logic and its applications in computer science and artificial intelligence, including formal methods, multi-agent systems, type theory, and non-classical logics. He emphasizes the development of proof assistants like Isabelle and tools for teaching logic and formal reasoning. Recent Research Trends: His publications from 2020–2017 highlight contributions to logic education, automated reasoning, and multi-agent system design. Key areas include Isabelle-based proof systems, hybrid logic formalization, and competitive agent development in programming contests. Scientific Contributions: He has led teams in the Multi-Agent Programming Contest, published extensively in journals like Annals of Mathematics and Artificial Intelligence , and contributed to open-source tools like NaDeA and SPA. Grants and Projects: Involved in projects such as CONTROL (constraint-based language processing) and HyLoMOL (hybrid logic integration). Supervised numerous MSc and PhD students in multi-agent systems and formal methods. Labs/Teams: Active in the Multi-Agent Systems Lab, leading development of agents for competitions and educational tools. Collaborates internationally on logic, AI, and formal verification.

Jakob Lykke AndersenView profile

Associate Professor

• Algorithms
• Graph Transformation
• Chemical Reaction Networks
• +7 more

Jakob Lykke Andersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in algorithms with applications in cheminformatics and complex systems. He also holds a former external appointment as a Research Fellow at the Tokyo Institute of Technology (2015–2017). Research Interests: His work lies at the intersection of computer science and theoretical chemistry, focusing on algorithmic methods for analyzing chemical reaction networks. He employs hypergraphs, mixed-integer linear programming, and probabilistic models to study metabolic pathways, reaction databases, and prebiotic systems. His research emphasizes computational efficiency, formal modeling, and software implementation. Publication Trends: His recent publications (2019–2025) show a consistent focus on graph-based modeling of chemical systems, rule extraction from reaction databases, thermodynamic feasibility, and stochastic analysis. These works appear in high-impact journals in cheminformatics, bioinformatics, and complex systems, reflecting strong interdisciplinary collaboration. Scientific Contributions: While no specific awards are listed, his sustained research output and leadership in funded projects highlight significant contributions to algorithmic cheminformatics. Grants and Projects: He is actively involved in two major ongoing research projects: (1) Software Infrastructures for Teaching at Scale (funded by Innovation Fund Denmark, 2022–2025), and (2) DIREC (Danish Research Center for Digital Economy, 2020–2025), indicating active engagement in both educational technology and core computational research. Advising and Outreach: While no students are listed, he participates in academic advising through project supervision. He contributes to public discourse through media appearances on topics such as mathematics in health (e.g., intestinal system modeling in obesity) and educational well-being. Labs and Teams: He collaborates with interdisciplinary teams, including researchers from bioinformatics, chemistry, and computer science, particularly through projects involving Merkle, Flamm, Fagerberg, and Stadler. His work is associated with algorithmic cheminformatics and software framework development groups at SDU.

• Algorithmics
• Online Algorithms
• Graph Algorithms
• +7 more

Lene Monrad Favrholdt is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), Faculty of Science. Her research is centered on algorithms, particularly online algorithms, graph algorithms, and combinatorial optimization. She is an active contributor to theoretical computer science with a focus on algorithmic performance under uncertainty and with predictions. Her research interests include: Online Algorithms and Competitive Analysis Algorithm Design with Predictions Graph Algorithms (e.g., edge coloring, minimum spanning trees) Combinatorial Optimization (e.g., bin packing, knapsack problems) Resource Allocation and Scheduling The recent trend in her publications shows a strong emphasis on integrating machine learning predictions into classical online algorithms to improve competitive ratios and practical performance. Her work spans theoretical analysis and experimental evaluation, appearing in top venues such as Algorithmica, ICML, and WADS. Her scientific achievements include receiving SDU's teaching award in 2022. She has served as an editor and peer reviewer for major algorithmic conferences, including the European Symposium on Algorithms (ESA) and Scandinavian Symposium on Algorithm Theory (SWAT). SDU's teaching award Lene Monrad Favrholdt has been involved in teaching courses such as Logic and Linear Algebra and has contributed to PhD supervision. She is actively engaged in research collaboration and academic service, including participation in workshops and conferences on new techniques in online algorithms. While specific grants are not mentioned, her extensive publication record and academic activities indicate sustained research funding and collaboration.

Torben Bach PedersenView profile

Professor

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

Professor Torben Bach Pedersen at Aalborg University's Department of Computer Science within The Technical Faculty of IT and Design is a leading expert in Data Engineering, Artificial Intelligence, and Energy Systems. With over 394 publications and 19 completed projects, he directs research at the Daisy – Center for Data-intensive Systems and leads innovations in energy flexibility and smart grid technologies. Key research areas: Data Warehousing, AI/ML, Energy Systems, Smart Grids Major projects: domOS (Smart Building OS), FEVER (Virtual Power Plants), DiCyPS (Cyber-Physical Systems) His research spans data-intensive systems, AI applications in energy management, and smart infrastructure development. Recent work focuses on transformer-based network AI and energy flexibility metrics. Scientific recognition includes: Æresdoktor (Honorary Doctor) at TU Dresden (2021) Best Paper Award Runner-Up (2019) WWW 2017 Best Demo Award Best Poster Award World Smart Grid Forum (2013) Member of Danish Academy of Technical Sciences (2013) As principal investigator and supervisor in 17 PhD projects, he advances AI-driven solutions for 6G wireless systems, smart buildings, and energy market optimization.

Sajad HomayounView profile

Assistant Professor

• Cybersecurity
• Machine Learning
• Artificial Intelligence
• +7 more

Sajad Homayoun is a Tenure Track Assistant Professor at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. His research focuses on integrating Artificial Intelligence with cybersecurity to develop advanced solutions for threat detection and mitigation. PhD in Computer Science Specialization in Cybersecurity & Machine Learning His technical expertise spans: AI-driven intrusion detection systems Adversarial machine learning (AML) Explainable AI (XAI) for security transparency Time series and graph analysis MLOps implementation with MLflow Python development using pandas, scikit-learn, and TensorFlow Research trends from his publications show strong emphasis on: Combating phishing attacks through ML Applying deep learning to crowdsensing trustworthiness Failure prediction in industrial systems Extracting behavioral patterns from ransomware Blockchain-based security applications He teaches Network Security courses during fall semesters and maintains active collaborations with researchers across Denmark and international institutions.

Troels Christian PetersenView profile

Associate Professor

• Particle Physics
• Machine Learning
• Statistics & Data Analysis
• +1 more

Troels Christian Petersen is an Associate Professor at the Niels Bohr Institute , specializing in Experimental Particle Physics . He contributes extensively to the ATLAS experiment at CERN and the IceCube experiment at the South Pole , with a focus on Machine Learning applications in particle physics. His research spans: Particle Physics : LHC experiments, Higgs boson decays, and searches for exotic particles Machine Learning : Geometric learning and neural networks for event classification Statistics & Data Analysis : Hypothesis testing and data reduction techniques Calibration : Enhancing detector performance through precision methods Recent publications highlight his work on: Graph Neural Networks for IceCube Gradient-boosted trees for glacier modeling Higgs boson properties and vectorlike quark searches Scientific Recognition: Science Faculty Dissemination Prize (2015) Teaching Award of the Faculty (2021) He supervises Master's students and teaches acclaimed courses on applied statistics and machine learning .

• Biophysics
• Computational Biology
• Chemical Reaction Networks
• +6 more

Aayush Arya is a Research Fellow at the Cosmic Dawn Center (DAWN) within the Niels Bohr Institute at the University of Copenhagen . His work bridges theoretical and computational approaches in astrophysics, biophysics, and prebiotic chemistry. Research interests: Computational modeling of protein phase separation and condensate recognition via molecular dynamics and active learning Development of graph-based and network analysis tools for uncovering hidden chemical reactions in prebiotic systems and atmospheric chemistry Application of laser resonance chromatography and mass spectrometry to study cosmic and atomic-scale phenomena Exploring nucleic acid analogs and autocatalytic networks in abiotic environments Recent work highlights include: Investigating the Formose Reaction's emergent behaviors using network analysis and high-resolution mass spectrometry Advancing computational workflows for discovering prebiotic reaction pathways Modeling transport properties in laser resonance chromatography systems