Find Academic Supervisors

• Medical Image Analysis
• Image Segmentation
• Image Registration
• +3 more

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.

• Artificial Intelligence
• Machine Learning
• Data Science
• +4 more

Jens Ulrik Hansen is an Associate Professor at Roskilde University's Department of People and Technology. His work bridges artificial intelligence, data science, and social epistemology. He holds a PhD and MSc, and his research focuses on explainable AI, machine learning applications in healthcare, social network dynamics, and logical frameworks for multi-agent systems. Hansen participates in interdisciplinary projects like #echopol (studying social media polarization) and ROROGREEN (digital innovation in maritime shipping). He has published widely on topics including AI ethics, expert knowledge integration in ML systems, and opinion dynamics in social networks. Research Interests: Artificial Intelligence & Machine Learning Data Science & Big Data Analysis Explainable AI Social Network Analysis Formal Epistemology Key Projects: #echopol: Echo chambers and polarization dynamics ROROGREEN: Green RORO shipping innovations FeedbackBox: Collaborative workshop methodologies Awards: No specific prizes listed, though his work has received media attention for innovations in AI research and healthcare applications.

Abdulkadir CelikkanatView profile

Assistant Professor

• Computer Science
• Machine Learning
• Data Science
• +3 more

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.

Andreas PavlogiannisView profile

Associate Professor

• formal methods
• algorithmic verification
• automata theory
• +6 more

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 .

Hans Martin KjerView profile

Associate Professor

• Ultrasound Imaging
• Super-Resolution Imaging
• Biomedical Image Analysis
• +7 more

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 HermankovaView profile

Assistant Professor

• Digital Epigraphy
• Greek Epigraphy
• Latin Epigraphy
• +5 more

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.

• Information Retrieval
• Data Science
• Text Analytics
• +5 more

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.

Andrea VandinView profile

Associate Professor

• Formal methods
• Qualitative and quantitative system modeling and analysis
• Domain Specific Languages
• +7 more

Andrea Vandin serves as an Associate Professor in the Formal Methods for Safe and Secure Systems section at DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark. Her research integrates theoretical computer science with practical system analysis, focusing on formal verification methodologies for complex systems. Her primary research interests include: Formal methods for system verification Qualitative and quantitative modeling techniques Domain-specific language development Large-scale performance analysis Model reduction algorithms Chemical reaction network analysis Statistical model checking Recent publications demonstrate expertise in constrained lumping of chemical systems, network embedding through equitable partitions, and stochastic conformance checking. Her work bridges computer science, mathematics, and systems biology with applications in sustainable system design. She actively contributes to UN Sustainable Development Goals through formal verification approaches for safe and secure systems. Her research shows strong trends toward applying model reduction techniques to chemical reaction networks and agent-based systems, with increasing focus on statistical verification methods and probabilistic modeling. The integration of MultiVeStA with Mesa for Python agent-based models represents a significant contribution to verification capabilities in simulation environments.

Yushuai LiView profile

Assistant Professor

• Digital Twin
• Energy Internet
• Reinforcement Learning
• +7 more

Yushuai Li is an Assistant Professor in the Department of Computer Science at Aalborg University. His research focuses on digital twin technologies, energy internet systems, distributed optimization, and cyber-physical security for energy networks. Institution: Aalborg University, Denmark Academic Rank: Assistant Professor Email: yushuaili@ieee.org, yusli@cs.aau.dk Research Interests: Li's work bridges artificial intelligence with energy systems, emphasizing: Digital Twin for Energy and Transportation Integration Reinforcement Learning in Power Trading Distributed Control for Microgrids Privacy-Preserving Energy Dispatch Autonomous Driving-Energy System Coupling Scientific Contributions: His recent publications address critical challenges in energy internet resilience, including: Distributed control under stealthy attacks Noise-resilient microgrid operations Multi-timescale optimization algorithms Event-triggered control strategies Secure peer-to-peer energy trading Honors & Awards: Recipient of multiple prestigious awards, including: Best Paper Awards (MPCE, ICCSIE, IEEE EI2) Excellent Young Expert Award (MPCE, 2023) National Natural Science Prizes (CAA 2022-2023) Highly Cited Papers (8 ESI Highly Cited, 2 ESI Hot Papers) H-index 24 with 2500+ Google Scholar Citations Academic Leadership: Serves as Associate Editor for four IEEE journals and chairs sessions at leading conferences like IEEE SmartGridComm, ISIE, and IEEE EI2. His 70+ publications span top venues including IEEE Transactions on Cybernetics, Smart Grid, and ACM SIGMOD.

Ali BasiratView profile

Associate Professor

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +5 more

Ali Basirat is an Associate Professor in the Department of Nordic Studies and Linguistics at the Faculty of Humanities, University of Copenhagen, affiliated with the Center for Language Technology (CST). His research focuses on data-driven approaches to language technology, particularly explainable natural language processing and morphosyntactic analysis, with emphasis on universal patterns across languages and efficient language processing tools. His academic qualifications include: PhD in Computational Linguistics from Uppsala University (awarded 2018) Certificate in Higher Education Pedagogy from Linköping University (awarded 2022) Dr. Basirat's research centers on mathematical modeling of natural languages, with core interests in universal language models, representation learning, dependency syntax, and explainable NLP. He investigates data-driven syntax, word embeddings, grammatical gender patterns across languages, and adaptation of large language models for environmental studies and sustainable development goals. His work bridges theoretical linguistics with practical NLP applications. Analysis of his 2020-2025 publications reveals three dominant trends: (1) Advancements in multilingual dependency parsing through linguistic typology and syntactic nuclei exploration; (2) Investigation of grammatical gender representation in word embeddings and language models; (3) Application of NLP techniques to climate conceptual history and environmental studies. His research increasingly addresses efficiency challenges in large language model adaptation while maintaining cross-lingual performance. No major scientific awards are currently documented in his public profile. Dr. Basirat supervises master's theses in natural language processing and computational linguistics, focusing on machine learning, language modeling, syntactic parsing, and knowledge graphs. He teaches graduate courses including Scientific Programming and Representation Learning for Natural Language Processing (RL4NLP) at the University of Copenhagen's IT & Cognition and Computer Science programs. He is a core member of the Center for Language Technology (CST) and actively collaborates with the Nordic Language Processing Laboratory (NLPL). His research network extends through membership in the Association for Computational Linguistics (ACL), facilitating international collaborations across European institutions.

Manfred JaegerView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +5 more

Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.

Tomer SagiView profile

Associate Professor

• Data Integration
• Artificial Intelligence
• Computer Science
• +3 more

Tomer Sagi is an Associate Professor in the Department of Computer Science at Aalborg University (AAU), Denmark. He is affiliated with The Technical Faculty of IT and Design and leads projects in the AI for the People and BLUE – Marine & Maritime Research groups. His research focuses on data integration, ontology engineering, artificial intelligence applications in healthcare and environmental science, and knowledge graph development. PhD in Information Systems from Technion-Israel Institute of Technology (2015) Former Lecturer at University of Haifa (2017–2022) Principal Investigator/Co-PI in projects like ODINI (AI-based Data Integration), MEHDIE (Middle Eastern Heritage Knowledge Graph), and DarkScience (Microbial Data Science) Research Interests: Data Integration, AI for Ocean Science, Medical Informatics, Ontology Evaluation, Multilingual Knowledge Systems, and Explainable AI. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. Key Projects (2022–2025): DarkScience: Metagenomic data analysis funded by Villum Foundation ODINI: AI-driven ocean data fusion and 3D reconstruction MEHDIE: Multilingual historical knowledge graphs for Middle Eastern heritage Awards: Received NLP4KGC Best Paper Award (2023) and AIME 2020 Best Paper Nomination. His contributions span 46+ publications, 8 datasets, and media coverage on AI applications in healthcare and environmental science. Labs/Teams: Active in AI for the People (applied AI solutions) and BLUE (marine data science). Collaborations include work on virtual twin technology for stroke management and medical data analytics.

Davide MottinView profile

Associate Professor

• Graph Theory
• Machine Learning
• Data Mining
• +4 more

Davide Mottin is an Associate Professor at the Department of Computer Science, Aarhus University. His primary research focuses on graph theory, machine learning, and data mining, with significant contributions to knowledge graphs, algorithm design, and interdisciplinary applications in drug discovery and material science. He holds a leadership role in large international conferences such as CIKM 2024 as a Program Chair. His research explores scalable graph algorithms (e.g., subgraph matching, alignment), robust knowledge graph cleaning, and leveraging large language models for scientific tasks. Mottin has pioneered work on spectral methods for graph analysis (e.g., NetLSD, VERSE embeddings) and developed frameworks for interactive data exploration (e.g., X2Q, MetaExp systems). Key contributions include FUGAL for graph alignment and Ucode for community detection Active in reproducibility efforts, as seen in retraction notices and algorithmic redesigns Focus on practical applications in drug discovery via evolution-based models (EvolMPNN) He has authored over 60 peer-reviewed publications and holds grants supporting interdisciplinary research at the intersection of computer science and life sciences. Mottin is affiliated with the university's AI and data science initiatives, contributing to both theoretical advancements and real-world system implementations.

Zhaoming YangView profile

Research Fellow

• Natural Gas Pipeline Engineering
• Deep Learning
• Resilience Optimization
• +5 more

Zhaoming Yang is a Postdoctoral Researcher (B.Eng.) at Aalborg University's Department of the Built Environment within the Faculty of Engineering and Science. His research focuses on critical infrastructure resilience, specifically natural gas pipeline systems. His primary research interests include: Natural Gas Pipeline Engineering and System Resilience Deep Learning applications for gas calorific value prediction Graph Neural Networks for pipeline network analysis Uncertainty quantification in infrastructure systems Data-driven optimization of gas supply networks Dr. Yang's publication record demonstrates strong expertise in applying data science to energy infrastructure challenges, with recent work emphasizing: Resilience optimization through demand-side management (2025) Self-organized criticality analysis in pipeline networks (2025) Advanced GNN methods for gas quality assignment (2023) Uncertainty-based resilience evaluation frameworks (2023) His research shows consistent collaboration with international scholars including Faber, Zio, Su, and Zhang. No scientific awards or formal student advisement records are currently documented in available sources. His work operates within Aalborg University's Structures, Geotechnics and Risk division, contributing to critical infrastructure safety research with significant citation impact (30+ citations for key 2023 publications).

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.