Find Academic Supervisors

• Semantic Web
• Ontology-Based Data Access
• Knowledge Graphs
• +3 more

Martin Giese is affiliated with the University of Oslo (Department of Informatics) and the University Clinic Tübingen (Department of Cognitive Neurology). He is a researcher with a focus on semantic technologies, ontology-based data access, and visual query systems. Research Themes : Semantic Web, Ontology Engineering, Knowledge Graphs, Geological Informatics, Probabilistic Logic, Automated Reasoning Key Collaborations : Siemens, Statoil, Norwegian Petroleum Directorate, and various European research institutions Technical Contributions : Developed visual query systems (OptiqueVQS), ontology-driven geological modeling (GeoFault), and semantic data integration frameworks for industrial applications. His work spans both theoretical logic and practical implementations in big data environments. Publications : Recent articles focus on fault ontologies, process representation, and semantic embeddings. Earlier work includes foundational research in automated theorem proving and UML formalization.

• Semantic Web
• Knowledge Graphs
• Machine Learning
• +3 more

Ahmet Soylu is a researcher at Oslo Metropolitan University , Norway, with a focus on Semantic Web , Knowledge Graphs , and Machine Learning applications in Cloud Computing and Smart Manufacturing . His work bridges semantic technologies with data-driven solutions for industrial contexts, particularly in collaboration with Bosch . Key research themes: Knowledge Graph Embeddings, Cloud Cost Optimization, Industrial Data Analytics Collaborations: Dumitru Roman, Evgeny Kharlamov, Radu Prodan Recent publications (2024–2025) explore hyperbolic knowledge graph embeddings, sustainable LLM inference, and cloud storage optimization. These works integrate semantic modeling with ML for edge-cloud systems and industrial data extrapolation. His methodology emphasizes graph-based approaches for cloud cost modeling, microservice scheduling, and AI innovation discovery in open-source repositories. Applications include welding quality monitoring, maritime supply chain optimization, and semantic ML pipelines.

• Network Management
• Cognitive Radio Networks
• Internet of Things (IoT) Security
• +7 more

Francine Krief is a Professor with extensive research in network management , QoS optimization , and context-aware security across cognitive radio networks, IoT, and vehicular systems. Her work integrates blockchain and fuzzy logic for secure, efficient resource allocation. Key Research Areas : Network Security, Cognitive Radio, IoT, Edge Computing Collaborations : Active partnerships with researchers like Mohamed Aymen Chalouf, Badr Benmammar, and Tidiane Sylla Publication Trends : Over the past decade, Krief has focused on blockchain applications in IoT security (2021), energy-efficient cognitive radio (2019), and SDN-based vehicular networks (2019). Recent work (2025) addresses user-centric IoT service placement in edge infrastructures.

• Software Engineering
• Requirements Engineering
• Functional Size Measurement
• +5 more

Carmine Gravino is a Professor in the Department of Computer Science at the University of Salerno, Italy, with an extensive publication record spanning over two decades from 2001 to 2025. His academic career demonstrates consistent research productivity with numerous publications in top software engineering venues including IEEE Transactions on Software Engineering, Empirical Software Engineering, and Information and Software Technology. Gravino's research spans multiple domains within software engineering and its intersection with emerging technologies. His primary research interests include software measurement (particularly COSMIC functional size measurement), requirements engineering, code quality analysis, and the application of artificial intelligence to software engineering problems. In recent years, he has expanded his research into educational technology, focusing on the development of educational metaverses and the integration of generative AI in learning environments. His work also extends to healthcare applications, particularly in diabetes prediction using machine learning techniques. The trajectory of Gravino's recent publications reveals a strategic evolution from traditional software engineering topics toward the integration of cutting-edge AI technologies. His 2023-2025 works prominently feature large language models applied to functional size measurement, requirements engineering, and code analysis. Simultaneously, he has developed a significant research stream in educational technology, creating frameworks for educational metaverses that incorporate ethical considerations for emotion recognition and AI integration. His healthcare-related work demonstrates interdisciplinary collaboration, applying software engineering rigor to medical prediction problems. Gravino maintains an active research group with frequent collaborations, particularly with Filomena Ferrucci (69 joint publications), Genny Tortora (33), Giuseppe Scanniello (30), Vincenzo Deufemia (29), and Michele Risi (29). His work appears in both journal and conference venues, with a strong emphasis on empirical validation through controlled experiments and case studies. His research has practical applications in software project management, requirements engineering processes, and educational technology development.

• Artificial Intelligence in Education
• Learning Analytics
• Intelligent Tutoring Systems
• +6 more

Marco Temperini is a Professor at Sapienza University of Rome's Department of Computer, Control and Management Engineering within the School of Engineering. With a publication record spanning over three decades (1990-2024), he has established himself as a leading researcher in educational technology with particular expertise in artificial intelligence applications for learning systems. His work bridges computer science and pedagogy, focusing on creating intelligent tools that enhance teaching and learning experiences. Temperini's research interests center on artificial intelligence in education, learning analytics, and intelligent tutoring systems. His work explores how AI technologies, particularly machine learning and natural language processing, can be effectively applied to educational contexts. Recent research has focused on leveraging large language models for educational assessment, developing chatbots for student support, and creating analytics tools for teachers to understand student learning patterns. His approach emphasizes practical implementations that address real educational challenges while maintaining scientific rigor. Analysis of his recent publications reveals a strong trend toward applying cutting-edge AI technologies to educational problems. His work demonstrates increasing focus on large language models for automated assessment, with particular attention to comparing open-source versus proprietary solutions. He has also maintained consistent research on concept mapping, peer assessment methodologies, and MOOC analytics, showing both depth in specific areas and adaptability to emerging technologies. His research consistently addresses scalability challenges in technology-enhanced learning environments. Professor Temperini has been actively involved in numerous international conferences including ICALT, ITHET, ITS, and IV, often serving in organizational roles. His collaborative work spans multiple European institutions, with particularly strong connections to the Italian AI and educational technology communities. He has contributed significantly to conference proceedings as both author and organizer, helping shape research directions in educational technology. His research methodology combines theoretical frameworks from educational psychology with advanced computational techniques. He frequently employs Item Response Theory for assessment validation and applies machine learning approaches to analyze educational data. This interdisciplinary approach has yielded practical tools like Q2A-II for peer assessment and TutorChat for supporting dyslexic learners, demonstrating his commitment to translating research into tangible educational benefits.

• Computer Networks
• Optical Networking
• Software-Defined Networking (SDN)
• +3 more

Malathi Veeraraghavan is a Professor in the Department of Computer Science at the University of Virginia . Her research focuses on Computer Networks , Optical Networking , Software-Defined Networking (SDN) , and Data Transfer Protocols , with applications in Grid Computing and High-Performance Computing . Recent work includes the PORTFILER system for malware detection via network profiling, SnF scheduling for inter-datacenter optical networks, and reliable multicast protocols over multi-domain SDNs. She has contributed to congestion control frameworks and network function virtualization surveys. Key collaborations span institutions like the University of Virginia, Universiti Teknologi Malaysia, and Bell Labs. Her publications appear in journals such as IEEE Transactions on Cloud Computing and conferences like GLOBECOM and INFOCOM .

• Database Systems
• Data Stream Processing
• Uncertain Data Management
• +4 more

Tingjian Ge is an academic researcher with a focus on database systems, graph stream processing, and uncertain data management. His work spans theoretical foundations and practical implementations in data science, with a career trajectory showing increasing specialization in real-time network analytics and secure data processing. PhD from Brown University (2009) Active in top-tier venues: ICDE, KDD, VLDB, WWW Research Interests include: Approximate query processing under resource constraints Graph stream modeling and edge shedding techniques Security and privacy in data systems Temporal network analysis and predictive modeling Optimization of multi-core and cloud-based query execution Scientific Contributions demonstrate expertise in: Developing sketch data structures for graph streams Creating fairness-aware prediction frameworks Advancing differential privacy mechanisms Designing efficient temporal subgraph algorithms

Daniela NicklasView profile

Professor

• Data Management
• Sensor-Based Applications
• Mobile Software Systems
• +5 more

Prof. Dr. Daniela Nicklas is a full professor at the University of Bamberg, Germany, holding the Chair of Mobile Systems within the Faculty of Business Information Systems and Applied Computer Science since 2014. She serves as the degree program representative for B.Sc. Informatik, speaker of the Bamberg Graduate School for Smart City Science (founded November 2024), and ambassador for the Gesellschaft für Informatik. She is also an elected member of the Review Board for Computer Science at the German Research Foundation (DFG). Her research focuses on bridging the gap between physical and digital worlds through data management for sensor-based systems and context-aware applications. Her work emphasizes data stream management technologies applied to smart cities, precision farming, pervasive computing, and situational awareness. She has developed innovative approaches for quality-aware data stream processing, sensor data management, and context modeling. Her research demonstrates how mobile computing systems can transform urban environments and agricultural practices through real-world applications. Her recent publications reveal strong trends in privacy-preserving techniques for mobility data, smart city data quality challenges, anomaly detection in stream data, and IoT device management. These works reflect her ongoing commitment to addressing practical challenges in sensor-based applications while maintaining rigorous academic standards. IBM Exploratory Stream Analytics Innovation Award for 'Data Stream Technology for Future Energy Grid Control' (2009) Prof. Nicklas actively participates in academic service, having chaired numerous conferences including BTW 2025 as General Co-Chair and PerCom 2020 as Program Chair. She serves on the editorial boards of Datenbank-Spektrum and previously for International Journal of Pervasive Computing and Communications. She also serves as a reviewer for multiple funding organizations including DFG, DAAD, and Alexander von Humboldt Foundation. She manages the Smart City Research Lab Bamberg with Professors Marc Redepenning and Astrid Schütz, which serves as a test bed for smart city applications in real-world environments. Her leadership extends to the Living Lab Bamberg infrastructure, which provides a practical environment for exploring smart city research challenges in the wild. She frequently collaborates with international institutions, as evidenced by her invited talks at Osaka University, Nara Institute of Science and Technology, and other international venues in 2024.

Henning TrsekView profile

Professor

• Cybersecurity
• Industrial Automation
• Internet of Things
• +3 more

Prof. Henning Trsek serves as Director of the Institute Industrial IT (inIT) at OWL University of Applied Sciences since 2024, leading research in Interconnected Automation Systems. He concurrently contributes to the Cybersecurity Training Lab, a Fraunhofer IOSB-INA collaboration, and teaches graduate courses in Network Security and Industrial Communication. His academic journey spans over two decades with deep industry integration through rt-solutions.de GmbH. Educational Background: PhD in Information Technology (2015, Otto-von-Guericke-Universität Magdeburg) MSc in Information Technology (2003-2005, Halmstad University/Aalborg University) Diploma in Electrical Engineering (2000-2003, Fachhochschule Lippe und Höxter) Trsek's research centers on industrial cybersecurity and intelligent automation , with seminal work in isochronous wireless networks and OT security frameworks . His current focus integrates large language models for automated risk assessment and TSN-based security architectures , addressing critical gaps in Industry 4.0 security validation. Recent publications demonstrate methodological evolution from protocol-specific analysis (2016-2020) toward holistic security automation (2023-2025). Analysis of his 15 most recent publications reveals dominant themes: automated security risk assessment (60% of works), industrial network security (25%), and emerging threat landscapes (15%). The research trajectory shows increasing emphasis on AI-driven security validation and modular security frameworks for flexible production systems. Scientific Recognition: Best Paper Award at ETFA 2012 Trsek actively supervises graduate theses through inIT's research projects while securing industry partnerships for applied cybersecurity initiatives. His committee leadership includes IEEE Industrial Electronics Society roles and technical program coordination for ETFA (2020-2025) and INDIN conferences. Current grants focus on AI-enhanced OT security validation through the Cybersecurity Training Lab collaboration. He directs the Interconnected Automation Systems research group at inIT, operating from Lemgo's Campusallee facility. The team maintains close ties with Fraunhofer IOSB-INA for industrial testbed development and participates in Germany's Allianz für Cybersicherheit for national security standardization.

• Machine Learning
• Neuroscience
• Medical Informatics
• +3 more

Sebastian Niehaus is a Doctoral Researcher affiliated with the Max Planck Institute for Human Cognitive and Brain Sciences and TU Dresden. His work bridges Neuroscience and Machine Learning , focusing on Medical Informatics and Statistical Computing . He develops scalable AI applications for clinical workflows through standardized data harmonization pipelines.

• Case-Based Reasoning
• Artificial Intelligence
• Knowledge Management
• +5 more

Professor Klaus-Dieter Althoff is a faculty member at the University of Hildesheim, working within the Intelligent Information Systems Division of the Mathematics, Natural Sciences, Economics & Computer Science school. His research focuses on applying artificial intelligence techniques, particularly case-based reasoning (CBR), to architectural design support systems and knowledge management applications. His research interests center on Case-Based Reasoning methodologies , AI-assisted architectural design , semantic building information modeling , and knowledge representation systems . Althoff's work bridges computer science with practical applications in architecture, developing systems that automate and enhance early-stage design processes through machine learning and case-based approaches. Analysis of his recent publications (2021-2024) reveals a strong trend toward integrating deep learning with traditional case-based reasoning for architectural applications. His research increasingly focuses on autocompletion of architectural spatial configurations , BIM (Building Information Modeling) enhancement , and explainable AI for design support . The work spans theoretical CBR methodology development and practical implementations in architectural design tools. Althoff maintains active research collaborations across disciplines, with publications spanning architecture, computer science, and cybersecurity applications of case-based reasoning. His work demonstrates consistent methodological development in CBR frameworks like FLEA and SEASALT, while expanding into new application domains including network security and fitness training systems.

Uwe NaumannView profile

Professor

• Algorithmic Differentiation
• Computational Engineering
• Scientific Computing
• +6 more

Uwe Naumann is a Universitätsprofessor (University Professor) for Computer Science at RWTH Aachen University, Germany, and Principal Scientist at the Numerical Algorithms Group (NAG) Ltd., Oxford, UK. His research focuses on Algorithmic Differentiation (AD) , including combinatorial problems in AD, integration of AD into numerical methods, AD software development, and AD in parallel high-performance computing. He also explores static and dynamic program analysis, transformation, optimization, and adjoint methods in computational science, engineering, and finance. Education : Dr. rer. nat. (PhD-equivalent in Mathematics, TU Dresden, 1999), Diplom in Mathematics (TU Dresden, 1996). Employment : Principal Scientist, NAG Ltd. (since 2022). Program Director, SIAM Activity Group on Applied and Computational Discrete Algorithms (2021-2022). Professor for Computer Science, RWTH Aachen (since 2004). Postdoctoral Researcher, Argonne National Laboratory (2002-2004). Research Trends : His recent publications emphasize GPU-accelerated optimization, interval arithmetic, Jacobian chaining, differential-algebraic equations, matrix-free methods, and adjoint sensitivity analysis. Contact : naumann@stce.rwth-aachen.de , +49 241 80-28920, IT Center, Room 230, RWTH Aachen.

Andreas AbelView profile

Researcher

• Microarchitecture Analysis
• Performance Prediction
• Cache Systems
• +5 more

Dr. Andreas Abel is a postdoctoral researcher at Saarland University working in the Real-Time and Embedded Systems Lab under Prof. Jan Reineke. His work focuses on the intersection of computer architecture, performance analysis, and security, with a particular emphasis on x86 microarchitectures. His research interests span microarchitecture analysis, performance prediction, cache systems, computer security, hardware-software interaction, and reverse engineering. Dr. Abel has made significant contributions to understanding CPU microarchitectures through tools like uops.info, which provides detailed latency, throughput, and port usage data for x86 instructions, and nanoBench, a low-overhead tool for running microbenchmarks on x86 systems. His work bridges theoretical computer science with practical performance analysis, enabling more accurate prediction of program behavior on modern processors. His publication record shows a consistent trajectory from formal methods and model checking in his early career toward increasingly sophisticated analysis of modern processor microarchitectures. Recent work focuses on accurate throughput prediction (FACILE, uiCA), security implications of microarchitectural behavior (Flushgeist), and tools for microbenchmarking (nanoBench). His research demonstrates a unique combination of formal methods expertise applied to practical problems in computer architecture. Dr. Abel has taught various courses at Saarland University including System Architecture, Design and Analysis of Real-Time Systems, and seminars on Resource Sharing in Real-Time Systems and Robustness of Hardware and Software Systems. His teaching spans both undergraduate and graduate levels, reflecting his dual expertise in theoretical computer science and practical systems engineering.

Haim AvronView profile

Professor

• Numerical Computing
• High Performance Computing
• Machine Learning
• +3 more

Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.

Alfred TaudesView profile

Professor

• Production Management
• Strategic Management in High-Tech Industries
• Real Options
• +5 more

Prof. Dr. Alfred Taudes is a faculty member at the Vienna University of Economics and Business, affiliated with the Department of Information Processing and Process Management under the Institute for Production Management. His research spans Production Management, Strategic Management in High-Tech Industries, Real Options, Supply Chain Management, and SAP R/3 integration, with significant contributions to Quantitative Models of Learning Organizations. Key research areas: Production/Operations Management, Strategic High-Tech Industry Analysis, Real Options Theory Notable collaborations with researchers like Mild, Natter, and Dorffner Focus on empirical validation of models in marketing, supply chain optimization, and software platform decisions His publications demonstrate interdisciplinary trends combining Management Science , Information Systems , and Machine Learning . Recurring subfields include Neural Network Applications , Real Options Valuation , Consumer Behavior Modeling , and Process Migration in Distributed Systems . While no explicit scientific awards are listed, his work has been disseminated through leading journals and conference proceedings.