Find Academic Supervisors

Heather Christine LentView profile

Researcher

• Natural Language Processing
• Machine Translation
• Low-resource Languages
• +3 more

Heather Christine Lent is a Researcher in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. Her work focuses on advancing natural language processing (NLP) for low-resource and marginalized languages, particularly Creoles, and addressing security challenges in multilingual systems. She leads the "Multilingual Modelling for Resource-Poor Languages" project (2022–2025), funded by Carlsbergfondet and Google. Her research integrates computational linguistics with ethical considerations, emphasizing equitable technology access. Notable contributions include the CreoleVal benchmark for multilingual evaluation and studies on NLP security in multilingual contexts. She collaborates internationally, including with institutions like George Mason University, and engages in community-driven dataset creation. Publications highlight themes like adversarial attacks on multilingual models, data quality audits, and cross-lingual transfer learning. She actively participates in workshops and tutorials aimed at fostering inclusive NLP practices, such as the "Connecting Ideas in Lower-Resource Scenarios" initiative.

• Machine Learning
• Deep Learning
• Remote Sensing
• +3 more

Fabian Christian Gieseke is an Associate Professor in the Department of Computer Science at the University of Copenhagen, specializing in Machine Learning. His research focuses on developing advanced computational methods for environmental monitoring and large-scale data analysis. Primary research interests include: Application of deep learning to remote sensing and geospatial data Large-scale forest biomass estimation using LiDAR and satellite imagery National-scale ecological monitoring systems Efficient algorithms for processing massive environmental datasets His recent publications demonstrate a strong emphasis on applying machine learning to climate science and ecological conservation. Work frequently involves: Cross-disciplinary collaborations with environmental scientists Development of novel deep learning architectures for 3D data Large-scale mapping of natural resources Solutions for global environmental challenges

• 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.

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.

Ioana O. BerceaView profile

Assistant Professor

• Data Structures
• Randomized Algorithms
• Approximation Algorithms
• +1 more

Ioana O. Bercea is a tenure-track Assistant Professor in the Computer Science Department at KTH Royal Institute of Technology, affiliated with the Division of Theoretical Computer Science. She holds a PhD in Computer Science from the University of Maryland (advised by Samir Khuller), a Master's from the same institution (advised by Aravind Srinivasan), and a BS in Mathematics and Computer Science from the University of Chicago. Her research focuses on Data Structures (dictionaries, Bloom filters), Randomized Algorithms (hashing, clustering), and Computational Geometry (Traveling Salesman Problem). Her recent publications emphasize scalable data structures, dynamic filters, and algorithmic efficiency, with applications in database systems and high-dimensional optimization. Awards & Honors: Best Artifact of ACM SIGMOD 2023 Future Digileader (Digital Futures, 2023) VR Starting Grant (2024) She advises PhD student Jonas Østergaard Klausen (University of Copenhagen) and collaborates with research groups including BARC (Copenhagen) and Digital Futures. Her VR Starting Grant funds the project "DataTech: Techniques for scalable data storage and analysis."

Martin Pekár ChristensenView profile

Research Fellow

• Computer Science
• Semantic Data Lakes
• Semantic Web
• +3 more

Martin Pekár Christensen is a Research Fellow at the Department of Computer Science , The Technical Faculty of IT and Design , Aalborg University . His work focuses on Semantic Data Lakes , Semantic Search , and Table Semantics , with an emphasis on efficient algorithms for data discovery. Research Trends : His recent publications (2024-2025) address semantic table search in large-scale data lakes, including benchmark datasets for evaluation and novel methods for query optimization. These works intersect Computer Science , Knowledge Graphs , and Information Retrieval . Collaborations : He collaborates internationally with researchers like Antonia Leventidis and Katja Hose. His 2025 publication at EDBT and 2024 contribution to SIGIR highlight cross-institutional teamwork. Research Datasets : Christensen co-developed the Semantic Table Search Dataset (Zenodo, 2023) and the RDF2Vec Embedding Generator (Zenodo, 2022), both open-access and widely cited.

Toine BogersView profile

Associate Professor

• Information Retrieval
• Recommender Systems
• Human-Computer Interaction
• +5 more

Toine Bogers is an Associate Professor at the IT University of Copenhagen in Denmark, affiliated with the Department of Computer Science . He also holds a part-time Lecturer position at Aalborg University Copenhagen within the Faculty of Humanities 's Department of Communication and Psychology . Additionally, he serves as Chief Scientific Officer at the AI Pioneer Centre for fundamental AI research. Research Interests : Information access technologies, recommender systems, search engine behavior, human information interaction, personal information management, and algorithmic fairness in HR. Key Research Areas : Algorithmic hiring systems for fair recruitment Complex search/recommendation scenarios Personal information management on handheld devices Interactive information retrieval reproducibility Scientific Contributions : Over 134 publications since 2005, including Google Scholar works on serendipity in recommendation systems, HR information behavior, and cross-domain relevance analysis. His 2022 data storytelling experiments and 2021 CHIIR collaboration manifesto highlight his methodological rigor. Awards : 2021 Best Paper Award 2020 & 2018 Best Reviewer recognitions 2020 Outstanding PC Member honor

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +3 more

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +3 more

Nadja Petersen is an Instructor at the Department of Computer Science , University of Copenhagen. Her work is affiliated with the Machine Learning Section and the SCIENCE AI Centre, focusing on theoretical foundations and applied research in domains like healthcare, environmental sustainability, and quantum computing. Email: nape@di.ku.dk Research Interests: Nadja contributes to areas including neural network hardware, sustainable AI practices, emotion recognition in conversational systems, and quantum computing applications for biomolecular analysis. Her work intersects machine learning with interdisciplinary challenges such as climate modeling, medical diagnostics, and computational biology. Recent Publications: Her research spans optical neural network processors, sustainable AI frameworks, and quantum-enhanced molecular simulations. Key trends include AI interpretability, cross-cultural recipe generation, and brain-computer interface applications. Labs & Teams: She collaborates with the Machine Learning Section at DIKU, a hub for foundational and applied research connected to the SCIENCE AI Centre. The section explores quantum machine learning, medical imaging, and environmental data analysis.

Henrik BulskovView profile

Associate Professor

• Natural Logic Systems
• Database Querying
• Machine Learning
• +3 more

Henrik Bulskov is an Associate Professor in the Department of People and Technology (Programming, Logic and Intelligent Systems) at Roskilde University. He holds an MSc and PhD. His work focuses on natural logic systems, database querying, and ontology-based information retrieval. He has contributed to projects such as SEAFACTS (digital maritime history platform) and NorDigHealth (regional digital health solutions). Education: MSc and PhD (specific disciplines not explicitly stated) Research interests include formal logic systems integration with databases, machine learning applications in bioinformatics, and semantic summarization through ontologies. Recent work explores query optimization in natural logic knowledge bases and disparity analysis in neural networks. His publications highlight advancements in computational logic for knowledge management and biomedical text analysis. Bulskov has participated in international conferences and contributed to media discussions on big data applications. Advising: No explicit student advisees listed Grants: Principal/Co-investigator in multiple projects including SIABO (2007–2012) and Duuoo Analysis (2021) Labs/Teams: Involved in interdisciplinary teams focusing on bioinformatics, health tech, and digital humanities through collaborative projects.

Jorge-Arnulfo Quiané-RuizView profile

Associate Professor

• Big Data Systems
• Cross-Platform Data Processing
• Distributed Computing

Jorge-Arnulfo Quiané-Ruiz was an Associate Professor specializing in scalable data infrastructures and distributed systems. He led the Rheem/Apache Wayang project and earned accolades for innovations in big data analytics. His work addressed query optimization and fault tolerance in cloud environments.

Mette SkovView profile

Associate Professor

• Information Behavior
• Digital Cultural Heritage
• User Experience Design
• +5 more

Mette Skov is an Associate Professor at Aalborg University's Department of Communication and Psychology, within the Faculty of Social Sciences and Humanities. She leads the CHAP research group (Challenges and Movements in Performing Digitalization) and is part of the Purposeful Technology Lab. Her work focuses on human interaction with information systems, including digital museum visitors, student information practices, and casual leisure information needs. She teaches qualitative methods and information behavior in the Communication and Digital Media program and coordinates the IT Master's module on Behavior and Awareness. Her research spans digital cultural heritage, user experience design, and interactive information retrieval. Notable projects include 'Venner med Naturen' and contributions to cybersecurity education via the CLEAR initiative. She serves on the ISIC Steering Committee (2024–2030) and the Institute Council at her department. Research Trends: Recent work emphasizes AI applications in retrieval (e.g., LLMs for leisure search) and long-term digital heritage studies (e.g., Danish museum website evolution). She also explores educational technology, including virtual role models and digital learning tools. Awards: Best Poster Award at iConference 2016 Labs/Teams: CHAP Group, Purposeful Technology Lab Grants/Projects: 11 projects including cybersecurity initiatives and museum communication studies

Peyman AfshaniView profile

Associate Professor

• Geometric algorithms and data structures
• Algorithmics
• Algorithms for hierarchical memory
• +3 more

Peyman Afshani is an Associate Professor in the Department of Computer Science at Aarhus University, Denmark. His research focuses on geometric algorithms, data structures, and algorithms for hierarchical memory systems. He maintains an active research profile with consistent publications since 2009, including 61 research outputs as documented. His primary research interests include: Geometric algorithms and data structures Algorithmics Algorithms for hierarchical memory Range Query optimization Computational Geometry Analysis of his publication trends since 2009 shows sustained output with 3-5 publications annually, peaking at 5 in multiple years (2011, 2012, 2014, 2017, 2019, 2021, 2023, 2024). His recent work (2023-2025) demonstrates continued focus on computational geometry problems including simplex range reporting, semialgebraic range reporting, and convexity in 3D search algorithms. His research shows strong theoretical foundations with practical applications in data structure design and query optimization. Collaboration networks indicate significant engagement with international researchers, particularly in computational geometry and data structure research domains. Professor Afshani supervises PhD students (at least one documented case) and maintains an active research program with consistent output across two decades.