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Fun HuView profile

Professor

• Satellite Communications
• Wireless Networks
• Artificial Intelligence
• +7 more

Prof. Fun Hu is the Inaugural Centre Director of the Bradford-Renduchintala Centre for Space AI at the University of Bradford. He holds a BSc and PhD from the same institution, with his doctoral research focused on personal mobile communications. His career spans satellite communication system engineering, satellite-terrestrial network integration, and aeronautical communications. He specializes in mobile, wireless, and satellite networks, emphasizing their applications and integration with emerging technologies like AI and big data. Education: BSc, University of Bradford PhD in Personal Mobile Communications, University of Bradford Research Interests: Integration of satellite and terrestrial networks SDN-based aeronautical communications AI-driven healthcare systems IoT and 5G applications in healthcare and telecommunications Energy-efficient network architectures Contributions to the Centre: Promoting Bradford's technological growth through interdisciplinary collaboration, course development (undergraduate/postgraduate/CPD), and community engagement in space technology. Active in advancing semantic interoperability, network security, and edge computing solutions. Labs/Teams: Leads the Bradford-Renduchintala Centre for Space AI, focusing on AI applications in space systems and healthcare.

• Cloud Computing
• Internet of Things
• Semantic Web
• +3 more

Professor Euripides G.M. Petrakis serves as a Professor and Laboratory Director at the Technical University of Crete (TUC) within the School of Electrical and Computer Engineering. He has been a faculty member since 1999 and has directed the Intelligent Systems Laboratory since 2006. As a Senior Member of IEEE, he maintains an active research profile with over 175 publications and more than 4,000 Google Scholar citations. Education: Ph.D. in Computer Science (1993), University of Crete B.Sc. in Physics (1985), National University of Athens Professor Petrakis' research spans Cloud Computing, Internet of Things, Semantic Web, and Computer Vision. His work has evolved from early computer vision and image databases to modern information systems, semantic web technologies, and most recently cloud computing and IoT applications. His research demonstrates consistent innovation across multiple computing domains with practical implementations in healthcare, security, and smart systems. His recent publications reveal a strong focus on IoT architectures, cloud/fog computing optimization, semantic service descriptions, and real-time video processing with Apache Flink. The research shows increasing integration of blockchain for security, advanced scheduling algorithms for microservices, and semantic approaches to improve interoperability in distributed systems. Scientific Recognition: Best paper award at AINA 2024 TUC Excellence Award 2022 Keynote speaker at EIDWT-2025 Ranked in top 2% of scientists worldwide (Stanford rankings 2021-2023) Professor Petrakis has supervised numerous graduate students, resulting in over 120 co-authored papers. He has successfully secured substantial research funding, attracting more than 2.5 million euros for TUC through multiple EU and Greek government projects. His grant portfolio includes leadership roles in Horizon 2020, FP7, and national research initiatives spanning border surveillance, smart farming, healthcare systems, and semantic web technologies. As Director of the Intelligent Systems Laboratory, he leads research in cloud computing, IoT, semantic web, and computer vision. The lab has produced multiple system prototypes including IntelliSearch, Semantic Similarity tools, MedSearch, and WebSummarization systems. Current research directions focus on secure federated architectures for IoT, advanced service description frameworks, and real-time video analytics in distributed environments.

• Artificial Intelligence
• Software Development
• Large Language Models
• +4 more

Kai-Kristian Kemell is a Research Fellow in the Computing Sciences department, focusing on interdisciplinary research at the intersection of Artificial Intelligence, Software Engineering, and Ethical AI. His work explores practical applications of Large Language Models (LLMs), autonomous agents in software development, and regulatory frameworks like the EU AI Act. Research Interests : His primary areas include AI-driven software development tools, ethical implementation of AI systems, multi-agent systems for code refactoring, and systematic literature reviews on trustworthiness in LLMs. He investigates how LLMs can automate tasks like microservice generation from API definitions and assist early-stage software startups through prompt engineering. Collaborations : Active collaborations include projects with researchers like Prof. Petri Abrahamsson, focusing on topics such as RAG systems, AI ethics governance, and hybrid work practices in software engineering. His work bridges theoretical AI concepts with practical industrial applications. Publications : His 2025 publications emphasize autonomous software development agents, EU AI Act critiques, and ethical alignment strategies for LLM-based systems. Earlier work (2023–2024) addresses AI ethics implementation via user stories, hybrid work impacts, and MLOps challenges. Awards : No specific scientific awards mentioned in the provided texts. Grants & Labs : No explicit details on grants or affiliated labs, but his research frequently involves collaborative projects with industry and academic partners.

• Data Management
• Big Data Processing
• Distributed Systems
• +7 more

Artur Wilczek, PhD, is a researcher in the Department of Applied Informatics at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology. His work focuses on data engineering, distributed systems, and modern data management technologies. His research interests include Big Data Processing , Data Warehousing , Stream Processing , Map-Reduce , RESTful Services , and Flash Memory Storage . These areas reflect a strong engagement with scalable and efficient data systems, particularly in distributed and real-time environments. The analysis of his publications reveals a consistent focus on data-intensive systems, including performance evaluation of database technologies, agile and flexible data warehouse design (e.g., Data Vault), and implementation of stream-based monitoring solutions. His work bridges theoretical algorithm design and practical system deployment, especially in public sector and mobile computing contexts. While no formal awards are listed, his contributions to peer-reviewed journals and conference proceedings in computer science and information systems demonstrate active scholarly engagement. Artur Wilczek has collaborated with researchers such as Maciej Penar, Adrian Najczuk, and Karol Woźniak. Although no advisees are mentioned, his technical publications suggest a role in guiding or contributing to applied research projects. No specific grants or funding sources are referenced in the provided text. No associated laboratories, research teams, or centers are explicitly mentioned in the available information.

Ilias GerostathopoulosView profile

Professor

• Software architecture
• Self-adaptive systems
• Data-driven software engineering
• +7 more

Ilias Gerostathopoulos is a Professor at the Chair for Software and Systems Engineering (I4) at the Technical University of Munich. His research focuses on software architecture, self-adaptive systems, big data analytics, and component-based development for cyber-physical systems. Education: PhD in Model-Driven Development of Software-Intensive Cyber-Physical Systems (Charles University, 2015) Research Venues: Active in PC member roles and track co-chair positions across conferences like ECSA, FAS*, SEAA, SASO, and ICSE. Organized workshops on evaluation metrics and data-driven software engineering. Scientific Contributions: Developed frameworks for dynamic component ensembles Advanced architectural homeostasis in adaptive systems Created toolchains for smart farming and traffic optimization Pioneered experiment-driven adaptation in cyber-physical systems Scientific Awards: Second place in ACM Student Research Competition (MODELS'14) Teaching: Offered graduate-level seminars on software architecture trends and IoT systems co-developed with Christian Prehofer.

• Natural Language Processing
• Semantic Web Technologies
• Machine Learning
• +3 more

Carlos Badenes-Olmedo is an Associate Professor in the Computer Systems Department at Universidad Politécnica de Madrid (UPM) . He is a member of the Ontology Engineering Group (OEG) , AI.nnovation Space , and co-founder of the spin-off company LibrAIry S.L. , which offers AI services for NLP and knowledge management. He also co-chairs the Knowledge Graph Summarization Workshop at ISWC. His research focuses on Natural Language Processing , Semantic Web Technologies , and Knowledge Graphs with applications in multilingual document similarity, topic modeling, and biomedical informatics. He has published extensively on cross-lingual search, scalable hashing algorithms, and AI in public health and procurement. Notable scientific awards include: DAAD Postdoc Funding (2021) Margarita Salas Mobility Grant (2021) ActuaUPM 3rd Prize for Best Startups (2020) Finalist for Margarita Salas Research Awards (2022) Finalist for Innovation Talent Recruitment Award (2021) He contributes to software engineering with tools like librAIry for distributed text mining, Corpus Viewer for NLP pipelines, and r4r for RESTful APIs over RDF data. His work bridges academia and industry through projects like TheyBuyForYou and TBFY Harvester for EU public procurement.

Qi XinView profile

Associate Professor

• Software Engineering
• Automated Program Repair
• Software Debugging
• +4 more

Qi Xin is an Associate Professor at the School of Computer Science, Wuhan University. He is a member of the Centre of Software Testing, Analysis and Reliability (CSTAR) and serves on program committees for major software engineering conferences including ASE, ISSTA, and ICSE. Dr. Xin received his Ph.D. from Brown University under the supervision of Dr. Steven Reiss and was previously a Postdoctoral Researcher at Georgia Institute of Technology working with Dr. Alex Orso. His research focuses on software engineering, particularly on developing automated and semi-automated approaches to improve software development processes and enhance software quality, security, and reliability. His recent work centers on automated program repair, software debugging, and program debloating. Dr. Xin has published extensively in top-tier software engineering venues, with research that frequently addresses practical challenges in real-world software systems. Dr. Xin's publication record shows a consistent focus on improving software reliability through automated techniques. His work spans from foundational program analysis to practical tool development, with several publications focusing on Android applications and addressing challenges specific to mobile software development. His recent work has begun exploring the application of large language models to software engineering problems, as evidenced by his 2025 paper on conversational LLM-based repair. ACM SIGSOFT Distinguished Paper Award for fault localization research Guest editor for Automated Software Engineering (AUSE) Special Issue Reviewer for ACM TOSEM, IEEE TSE, and EMSE journals Dr. Xin actively mentors students and serves the academic community through conference organization and journal reviewing. He teaches courses including Compiler Design and Software Testing and Practice at Wuhan University, bridging his research expertise with classroom instruction to train the next generation of software engineers.

• Software Development
• Programming Languages (C++, Java, Python, Kotlin)
• Web Development
• +4 more

Christopher Olbertz is a lecturer at the Computer Science Department within the College of Engineering at Saarland University of Applied Sciences. He teaches programming and software development courses, focusing on languages like C++, Java, Python, and Kotlin, as well as frameworks such as Spring and SpringBoot. His teaching emphasizes practical applications in software architecture, database systems, and web technologies. His research interests include: Object-Oriented Programming (C++ vs. Java) Software Design Patterns Dependency Injection and Layered Architecture Automated Testing and Performance Evaluation Cross-Platform Application Development Modern Java Features (Lambda Expressions, Collection API) He supervises bachelor theses on topics ranging from web-based applications and REST APIs to software testing and cross-platform development, often in collaboration with industry partners like Mercedes Benz Bank, ZF Friedrichshafen, and SAP Hybris.

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

Prof. Youakim Badr is a Professor of Data Analytics in the Engineering Division at Temple University's Great Valley campus. His research focuses on advancing interdisciplinary fields such as artificial intelligence, blockchain technology, cybersecurity, and Internet of Things (IoT) systems. He has contributed to over 134 publications, emphasizing trust frameworks in decentralized systems, privacy-preserving machine learning, and smart service ecosystems. Research Interests: Prof. Badr’s work spans AI-driven systems, blockchain applications for identity management, secure IoT architectures, and innovative frameworks for data analytics. He actively explores how AI can enhance service ecosystems and address societal challenges aligned with UN Sustainable Development Goals (SDGs), particularly in healthcare and smart infrastructure. Collaborations: His recent research involves international collaborations in blockchain-based trust systems, cybersecurity for AI, and IoT-enabled medical devices. These efforts have led to impactful solutions like the TrustLend platform for decentralized lending and frameworks for resilient dynamic data-driven applications. Advising & Grants: While specific student names are not listed, his research has been supported by grants focusing on AI ethics, secure smart cities, and crisis management systems. He has co-authored seminal papers on federated learning, adversarial testing for AI, and artifact-centric process modeling. Labs/Teams: His contributions include foundational work in the TrustLend platform and collaborations on resilient DDDAS (Dynamic Data-Driven Application Systems) for crisis environments. He also leads initiatives integrating business artifacts with modern IT infrastructure.

Peter DologView profile

Associate Professor

• Recommender Systems
• Web Engineering
• Machine Learning
• +5 more

Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design. His research focuses on Recommender Systems, Web Engineering, Machine Learning, and Health Informatics. He leads projects such as the MEco Medical Ecosystem and the IWIS Intelligent Web and Information Systems initiative. His work emphasizes explainable AI, knowledge graphs, and social media analytics for public health monitoring. Notable contributions include hypergraph-based recommendation models and sensitive information detection in legal documents. Projects include MEco (medical event surveillance) and IWIS (web systems personalization), funded by EU and national grants. His research spans 148 publications, with recent work on explainable recommendation systems and neural networks for medical data analysis. Collaborations include the University of Vienna and international conferences like ECIR and RecSys. Media coverage highlights his work on using social media for epidemic detection. Key research areas include recommendation systems with attention mechanisms, knowledge graph integration, and ethical AI applications in healthcare. His methodologies bridge machine learning with user-centric design, emphasizing transparency and explainability in AI-driven systems.

Eli TilevichView profile

Professor

• Software Engineering
• Distributed Systems
• Mobile Applications
• +4 more

Eli Tilevich is a Full Professor in the Department of Computer Science at Virginia Tech and Associate Department Head for Graduate Studies. His research focuses on systems aspects of software engineering, distributed systems, mobile/IoT applications, and energy-efficient software. He leads the Software Innovations Lab and has over 130 refereed publications. His work has been supported by federal grants and industry awards like Microsoft Research and IBM Faculty Awards. Education: Ph.D., Computer Science, Georgia Tech (2005) M.S., Information Systems, New York University (1999) B.A., Computer Science and Math, Pace University (1997) Research Interests: Automated software transformation Middleware and distributed systems Security and privacy in mobile systems Music informatics Computing education innovations His recent work includes advancements in edge computing, secure communication protocols, and educational platforms for secure programming. Over 30 students have graduated under his mentorship, many now in academia and industry leadership roles. Awards: Microsoft Research Software Engineering Innovation Foundation Award IBM Faculty Award Multiple best paper awards at ICDCS, ICWE, and SIGCSE Teaching spans courses like Programming Languages , Software Engineering Capstone , and Research Methods . He has served as General Chair for GPCE 2021, MOBILESoft 2019, and program chair for ManLang 2018.

• Digital Preservation
• Web Technologies
• Graph Theory
• +3 more

Charles Cartledge is an Adjunct Instructional Faculty at Old Dominion University's Continuing Education division. He holds a Ph.D. in Computer Science from Old Dominion University (2014). His research focuses on digital preservation strategies, web technologies, and graph theory applications in data archiving. Cartledge has contributed to frameworks for self-preservation of digital objects and analysis of network resilience in graph structures. His work bridges theoretical computer science with practical implementations in distributed systems and high-performance computing environments. No formal advising roles or grants are listed in the provided text.

• Artificial Intelligence
• Citizen Science
• Semantic Web
• +4 more

Chrisa Tsinaraki is a Lecturer at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete. She specializes in interdisciplinary research areas including Citizen Science, Artificial Intelligence, Semantic Web technologies, and Data Management. Her work bridges technical innovation with societal applications, particularly in environmental policy, public health, and smart spaces. Her research portfolio includes contributions to the Web of Things (WoT), analyzing mobile apps during health crises, and leveraging citizen science for biodiversity conservation. She has collaborated with institutions like the European Commission’s Joint Research Centre (JRC) on projects involving spatial data analysis, semantic interoperability, and multimedia content management. Key Projects: CoCoMA (Content & Context-Aware Multimedia Systems), MoM-NOCS (Mobile Multimedia Nature Observation Management) Technical Expertise: SPARQL/XQuery integration frameworks, OpenAPI semantics, and ontology-driven data systems Her publications emphasize practical applications of emerging technologies in public sectors, policy-making, and crisis management. She has authored over 50 peer-reviewed articles, contributing to both technical advancements and policy-oriented analyses.

Paolo BurelliView profile

Lecturer

• Game AI
• Player Modeling
• Machine Learning
• +5 more

Paolo Burelli is a Lecturer and Head of the brAIn Lab at the IT University of Copenhagen. He also serves as a Senior Data Scientist at Tactile Entertainment A/S since 2016. His research focuses on Game AI, Player Experience, Machine Learning, and Neuroscientific approaches to gaming. Key affiliations include the Creative AI Lab and The Maritime Hub. Research interests span adaptive game systems, player modeling, and the intersection of neuroscience with game design. Notable projects include the Pioneer Centre for Artificial Intelligence (2021–2034), ALGO (2019–2022), and CREATE (2023–2025). Projects emphasize creative AI applications in education, difficulty modeling in games, and maritime safety through alarm-handling practices. Publications highlight work on LLM emotion generation, EEG-based neural decoding, and player frustration tolerance. Collaborations include institutions like Springer and the Danish National Research Foundation. His datasets, such as the Uncanny Valley Face Questionnaire, contribute to facial perception studies. Labs under his leadership include the brAIn Lab, exploring AI ethics, game analytics, and human-centered computing. Projects emphasize practical applications of AI in education and industry.

• Ontology Engineering
• Machine Learning
• Information Retrieval
• +8 more

Carlos Badenes-Olmedo is a Post-Doctoral Researcher at the Polytechnic University of Madrid’s Faculty of Informatics, part of the Ontology Engineering Group (OEG). He holds a Master’s in Artificial Intelligence (2015) and a Computer Science Engineering degree (2006) from the same institution. Previously, he worked in industry for 8+ years as a software architect specializing in real-time data and M2M communications. He collaborates with the TEDECO group on data mining using Call Detail Records (CDRs). Research Focus: Ontology Engineering, Machine Learning, Information Retrieval, Exploratory Search, Recommender Systems, Multilingual Document Similarity, Public Procurement Analysis, and Clinical Data Mining (e.g., polypharmacy studies). His work includes developing cross-lingual search engines, knowledge graphs for public procurement (e.g., EU Contract Hub), and health-related ontologies (e.g., Drugs4Covid). Key Projects: Led development of the Corpus Viewer platform for analyzing research documents, created the FarolApp for light pollution monitoring using Linked Data, and contributed to the librAIry framework for distributed text mining. Active in public procurement transparency initiatives and biomedical knowledge graph construction. Skills: Natural Language Processing, Topic Modeling, REST APIs, Docker, Linked Data, and open-source tool development (e.g., TBFY Harvester). Collaborates internationally on EU-funded projects and publishes extensively in top venues like K-CAP, ISWC, and IEEE conferences. Teaching: Delivers tutorials on hybrid NLP techniques, cross-lingual document exploration, and semantic search. Involves in educational experiments like LEGO® Serious Play in software engineering education.