جست‌وجوی استاد و پژوهشگر

• Explainable AI
• Human-AI Interaction
• Audiovisual Affective Computing
• +۴ مورد دیگر

Dr. David Johnson is a Junior Independent Group Leader at Bielefeld University's Faculty of Engineering , leading the Human-Centric Explainable AI research group within CITEC. His work bridges Explainable AI with Audiovisual Affective Computing , focusing on high-stakes human-AI collaboration and industrial sound analysis. Current Position: Junior Independent Group Leader, Bielefeld University (since 2021) Previous: Postdoctoral Researcher at Fraunhofer Institute for Digital Media Technology (2019-2021) Education: PhD in Sound and Music Computing from University of Victoria (2019), MSc in Computing in the Arts from College of Charleston (2014) Research interests span Explainable AI , Human-AI Interaction , and Extended Reality applications, particularly in medical diagnostics and industrial sound processing. Recent publications highlight his work on trust dynamics in high-stakes AI and federated learning architectures . His collaborative projects include involvement in the TRR 318 'Constructing Explainability' initiative. While no formal awards are mentioned, his research has produced multiple publications and practical implementations in industrial and educational contexts.

• Smart Grids
• Blockchain Applications
• Energy Systems Optimization
• +۵ مورد دیگر

Ionut Anghel is an Associate Professor at the Department of Computer Science within the Faculty of Automation and Computer Science at Technical University of Cluj-Napoca, Romania. His research spans smart energy systems, blockchain applications, healthcare technologies, and artificial intelligence, with numerous publications in high-impact journals and conferences from 2008 to 2025. He maintains strong collaborative relationships with researchers across Europe, particularly with Tudor Cioara, Ioan Salomie, and Marcel Antal. Dr. Anghel's research interests focus on the intersection of computer science and energy systems, with particular emphasis on smart grid optimization, peer-to-peer energy trading mechanisms, blockchain applications for energy management, and AI-driven solutions for healthcare. His work demonstrates a consistent trajectory from foundational research in data center optimization to current cutting-edge work on federated learning for renewable energy prediction and AI-assisted healthcare solutions. His recent publications show increasing focus on integrating large language models with digital twin technologies for energy applications. His scholarly contributions demonstrate significant impact in both theoretical frameworks and practical implementations, with publications in IEEE Access, Sensors, Future Internet, and other reputable venues. The breadth of his work spans from theoretical game theory applications to concrete healthcare platform implementations, reflecting a versatile research portfolio that bridges multiple domains. Dr. Anghel has been actively involved in European research initiatives, particularly those focused on energy systems and healthcare technologies, with recent work examining transitional care pathways and cognitive decline management through innovative technological approaches.

• Edge Computing
• Federated Learning
• Internet of Things
• +۵ مورد دیگر

Sasu Tarkoma is a Professor at University of Helsinki specializing in next-generation computing systems with over two decades of research experience. His work bridges theoretical computer science with practical applications in smart cities, environmental monitoring, and industrial systems. His research interests focus on edge computing infrastructure , federated learning architectures , and AI-driven environmental monitoring systems . Tarkoma's work addresses critical challenges in distributed intelligence, particularly in resource-constrained environments where privacy, energy efficiency, and real-time processing are paramount. Recent work explores the integration of large language models with edge systems and novel approaches to 6G network architectures. The publication record reveals a strategic evolution from foundational mobile computing research to cutting-edge work at the intersection of AI, networking, and sustainability. His recent articles demonstrate particular strength in solving practical implementation challenges for federated learning in industrial settings and developing energy-efficient approaches to AI deployment in constrained environments. Tarkoma maintains an extensive collaborative network across European institutions, frequently partnering with researchers from Aalto University, University of Oulu, and international partners in Asia. His work shows increasing emphasis on environmental applications, particularly air quality monitoring systems using UAVs and mobile sensors.

• Cybersecurity
• Artificial Intelligence
• Internet of Things
• +۳ مورد دیگر

Vyacheslav S. Kharchenko is a researcher affiliated with the National Aerospace University in Kharkiv, Ukraine, specializing in cybersecurity, artificial intelligence, and dependable computing. His work focuses on safety and security assessment of UAVs, IoT systems, and programmable systems, with a strong emphasis on Markov models, penetration testing, and hardware-based security solutions. Recent research includes cybersecurity frameworks for unmanned aircraft, blockchain quality models , and resilience engineering for AI systems. Collaborative efforts with colleagues like Oleg Illiashenko and Herman Fesenko address AI-powered cyberattacks, LiFi network reliability, and resource-constrained AI security. His publications span journals such as IEEE Access , Sensors , and Algorithms , covering topics like stochastic modeling for data evaluation, cache memory optimization , and UAV swarm reliability . Despite no explicit awards listed, his extensive contributions to safety-critical systems and Industry 4.0 cybersecurity underscore significant academic impact.

• Computer Science
• Artificial Intelligence
• Operations Research
• +۵ مورد دیگر

Wei Cui is an active researcher with affiliations to multiple institutions, including Beijing Institute of Technology, Shandong University of Science and Technology, and others. Their work spans diverse domains such as artificial intelligence , robotics , quantum computing , and environmental engineering . Research Interests include multimodal fake news detection, vehicle routing optimization, biomedical signal processing, and remote sensing applications. Recent publications focus on machine learning for urban infrastructure , humanoid robotics , and quantum key distribution . Article Trends reveal a strong emphasis on interdisciplinary AI (e.g., healthcare waste logistics, 3D perception) and data-driven optimization . Collaborations include experts in control systems , quantum physics , and environmental science .

• History of Chinese Medicine
• Daoism and Religion
• Digital Humanities
• +۵ مورد دیگر

Michael Stanley-Baker (徐源) is an Associate Professor in the History Department at Nanyang Technological University, specializing in the history of Chinese medicine and religion, particularly Daoism. He also serves as an associate of the UCL China Centre for Health and Humanities and as treasurer on the council of the International Association for the Study of Traditional Asian Medicines. Stanley-Baker received his PhD from University College London and holds a clinical degree in Chinese medicine from Ruseto College, Boulder CO. His academic journey includes work and study at Indiana University, Bloomington; Academia Sinica, Taipei; and the Needham Institute, Cambridge. He has conducted extensive fieldwork with healers in Taiwan and China, bridging scholarly and clinical perspectives. His research focuses on the intersection of religion, medicine, and science in Chinese history, with particular emphasis on Daoist healing practices and the circulation of medical knowledge. He employs innovative digital humanities methods to analyze historical medical texts, creating tools like the Polyglot Asian Medicine Knowledge Graph System and the DAOBUDMED6D corpus for studying Six Dynasties medical, Daoist, and Buddhist texts. His recent publications reveal a growing emphasis on transnational medical exchanges, the historical geography of drugs, and the political dimensions of medical knowledge. Stanley-Baker's work challenges conventional boundaries between 'medicine' and 'religion,' arguing for more nuanced understandings of how healing knowledge was organized and transmitted in pre-modern contexts. International Association for the Study of Traditional Asian Medicines Council Member Lead developer of Polyglot Asian Medicine digital project Co-editor of the Routledge Handbook of Chinese Medicine Stanley-Baker directs the 'Drugs across Asia' digital humanities project, which creates new tools for analyzing pharmacological knowledge in Asian historical texts. His work combines traditional philological approaches with cutting-edge digital methods, facilitating large-scale analysis of drug references across religious and medical canons. His research has important implications for understanding how medical knowledge circulated across cultural and political boundaries in pre-modern Asia.

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +۵ مورد دیگر

Mohamed M. Abdallah is a researcher affiliated with Hamad Bin Khalifa University in Doha, Qatar, specifically within the College of Science and Engineering . His work focuses on advanced applications of Machine Learning , Artificial Intelligence , and Cybersecurity in domains such as Smart Grids , Internet of Things , and Wireless Communication . His recent research explores Federated Learning under adversarial conditions, optimization of Multi-Agent Systems for task offloading, and Privacy-Preserving Techniques in networked environments. Key contributions include frameworks for Deep Reinforcement Learning (DRL) in Edge Computing and 6G Networks , addressing challenges in Energy Efficiency , Latency , and Data Distribution Shifts . His publications highlight collaborations with institutions like Texas A&M at Qatar and Hamad Bin Khalifa University , emphasizing solutions for Heterogeneous Networks , Blockchain Applications , and Secure Communication in IoT and critical infrastructure.

• Clinical Natural Language Processing (NLP)
• Medical Informatics
• Machine Learning
• +۳ مورد دیگر

Jens-Michalis Papaioannou is a prominent Researcher in clinical natural language processing (NLP) and medical informatics, with extensive publications in top-tier venues like ACL, LREC, and EMNLP. His work focuses on improving clinical decision support systems through advanced machine learning techniques. 2024 : Revisiting clinical outcome prediction for MIMIC-IV with biomedical transformers 2023 : Developing MEDBERT.de for German medical NLP and MedAlpaca conversational AI 2022 : Introducing ProtoPatient for interpretable diagnosis prediction 2021 : Creating self-supervised knowledge integration frameworks for admission note analysis His research spans seven major themes : Clinical outcome prediction from admission notes Cross-lingual knowledge transfer in medical NLP Prototypical network applications Data drift analysis in longitudinal datasets Knowledge integration techniques Model optimization for healthcare LLM interpretability frameworks He has collaborated with Wolfgang Nejdl, Alexander Löser, and Betty van Aken on 13+ publications , with over 445 citations. Notable contributions include: Novel patient similarity modeling approaches ICD code hierarchy integration methods Multilingual clinical model strategies Adversarial robustness analysis Medical conversational AI frameworks

• Artificial Intelligence
• Machine Learning
• Automotive Production
• +۸ مورد دیگر

Prof. Dr. Alexander Schiendorfer is a faculty member at Technische Hochschule Ingolstadt within the Faculty of Industrial Engineering , focusing on AI-based Optimization in Automotive Production . His research bridges Artificial Intelligence with manufacturing and industrial engineering , particularly in constraint programming, self-organizing systems, and machine learning applications for composite materials. His work spans pedagogical innovation in machine learning education, real-time manufacturing analytics , and AI for energy systems . Recent publications highlight applications in gas grid management, synthetic data frameworks, and defect analysis in autonomous driving sensors. He leads teams at AImotion Bavaria , collaborating on projects like SmartManPy for synthetic manufacturing data and MORL agents for multi-objective energy optimization. His methodological contributions include certainty groups for neural network confidence estimation and hierarchical resource allocation algorithms.

Eirini Ntoutsiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Eirini Ntoutsi is an Associate Professor at the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover and a member of the L3S Research Center. Her academic journey includes a post-doctoral position at LMU Munich with an Alexander von Humboldt Foundation fellowship, and she earned her PhD from the University of Piraeus, Athens under the supervision of Y. Theodoridis. She holds a diploma and M.Sc. in Computer Engineering & Informatics from the University of Patras, Greece. Her research lies at the intersection of Artificial Intelligence and Machine Learning, focusing on two main pillars: learning over complex data and data streams (covering adaptive learning, change detection, and model stability), and responsible Artificial Intelligence (covering fairness-aware learning, data quality, and proper evaluation of AI/ML methods). Her work addresses critical societal challenges related to bias in algorithmic decision-making systems. Dr. Ntoutsi's recent publications demonstrate a strong focus on drift-aware learning for imbalanced data streams and fairness in AI systems. Her research shows a clear progression from foundational work in pattern management during her PhD to cutting-edge applications addressing real-world challenges in social streams, sensor data, and recommendation systems. Alexander von Humboldt fellowship for postdocs Best-student paper award at ICBK 2018 1st place in the 2006 innovation competition in Greece Dr. Ntoutsi actively mentors PhD and master's students while leading major research initiatives including NoBIAS (EU-funded, as Network coordinator), BIAS (Volkswagen Stiftung-funded), and OSCAR (DFG-funded). Her interdisciplinary approach combines technical AI solutions with philosophical and legal considerations to develop more equitable algorithmic systems.

• Data Stream Processing
• In-Network Computing
• Industrial Cyber-Physical Systems
• +۷ مورد دیگر

Liam Tirpitz is a researcher and PhD candidate at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University. He is affiliated with the Chair of Computer Science i5 (Information Systems and Databases) and the Data Stream Management and Analysis Group, where he has been working since February 2022. Education : Master's degree in Computer Science from RWTH Aachen University (2021). His research focuses on data stream processing, in-network computing, and FAIR data ecosystems, with applications in industrial and cyber-physical systems. He explores methods for efficient data aggregation, stability detection, and cross-organizational data sharing, often leveraging technologies like WebAssembly and knowledge graphs. His work integrates distributed systems principles with database technologies to enable scalable and secure data processing in industrial environments. The recent publications of Liam Tirpitz reflect a strong focus on industrial data management, with trends toward in-network computing, edge-based stream processing, and FAIR principles. His work spans both theoretical architectures (e.g., GALOIS) and applied implementations in domains like manufacturing and health data, demonstrating interdisciplinary collaboration and real-world impact. Liam Tirpitz is actively involved in teaching and academic supervision. He has taught courses such as Data Ecosystems Lab and Implementation of Databases, and is currently supervising multiple thesis projects on topics including WebAssembly-based query functions, anomaly detection, and hybrid stream processing. He participates in research projects like StreamFröst and the Cluster of Excellence Internet of Production, contributing to the development of digital infrastructure for sustainable industrial systems. He is based at the Chair of Computer Science i5, a leading research group in information systems and databases at RWTH Aachen, which supports his work in data-intensive applications and distributed computing environments.

• Computer Science
• Distributed Systems
• Blockchain Technology
• +۳ مورد دیگر

Hans-Arno Jacobsen is a Professor at the Faculty of Computer Science (Technische Universität München, TU Munich) and affiliated with the Department of Electrical and Computer Engineering at the University of Toronto. His work spans Computer Science , Distributed Systems , and Artificial Intelligence . Research interests include Blockchain Technology , Consensus Algorithms , Graph Neural Networks , and Quantum Computing . Recent projects focus on decentralized consensus , energy-efficient databases , and federated learning in edge environments. His 15 most recent articles (2024–2025) explore topics such as dynamic resource orchestration , CRDT-based blockchains , and multimodal depression recognition . Collaborates with researchers like Ruben Mayer , Gengrui Zhang , and Shiqiang Wang on systems for federated computing , blockchain benchmarking , and distributed GNN training .

• Cybersecurity
• Digital Rights Management
• Internet of Things Security
• +۵ مورد دیگر

Eva Rodríguez is an Associate Professor in the Department of Network Engineering at Pompeu Fabra University's School of Engineering, specializing in cybersecurity, digital rights management, and IoT security. With over 50 publications since 2003, she has established herself as a leading researcher in secure network architectures and privacy-preserving technologies. Her research focuses on applying machine learning techniques to cybersecurity challenges, particularly in mobile and IoT environments. Rodríguez has published extensively on deep learning for intrusion detection, privacy protection mechanisms, and security frameworks for next-generation networks (5G/6G). She has also made significant contributions to RISC-V processor security through the Horizon Europe Vitamin-V project. Recent work shows a strong emphasis on federated learning approaches for privacy preservation in fog computing environments and the development of security management architectures for resilient wireless ecosystems. Her publication record demonstrates consistent leadership in both theoretical frameworks and practical implementations of security solutions. Among her notable contributions are comprehensive surveys on deep learning techniques for mobile network security and machine learning methods for IoT privacy protection, which have become important references in these rapidly evolving fields. As a research supervisor, she has mentored several doctoral students including Norma Gutiérrez and Beatriz Otero, who now appear as co-authors on her recent publications. Her collaborative work extends across multiple European research projects, demonstrating strong integration within the international cybersecurity research community.

• Blockchain Technology
• Distributed Systems
• Cybersecurity
• +۳ مورد دیگر

Aad P. A. van Moorsel is a faculty member at the School of Computing Science, University of Newcastle , with a focus on blockchain technology, cybersecurity, and privacy-preserving machine learning systems. His work bridges theoretical analysis with practical implementations in decentralized systems and financial technologies. Research Interests : Blockchain systems, smart contract security, federated learning, verifiable fairness in AI, and privacy-preserving technologies. Key Contributions : Development of the BlockSim simulation framework for blockchain, quantitative analysis of Ethereum's verifier dilemma, and frameworks for fairness-as-a-service in machine learning. Collaborations : Regularly works with researchers in cybersecurity (e.g., Mhairi Aitken, Ehsan Toreini), financial technology (e.g., Karen Elliott, Kovila Coopamootoo), and distributed systems (e.g., Han Wu, Lydia Chen). Publications : Over 170 works since 1992, with recent emphasis on blockchain scalability, AI ethics, and secure financial services. Tools : Co-designed OpBench for Ethereum opcode benchmarking and ADaCS for analyzing data collection strategies in security contexts.

• Semantic Web
• Internet of Things
• Ontology Reasoning
• +۵ مورد دیگر

Floriano Scioscia is a researcher at the Polytechnic University of Bari, Department of Electrical Engineering and Information Technology, with extensive contributions to the Semantic Web, Internet of Things, and knowledge-based systems. His work focuses on developing frameworks for semantic reasoning, resource discovery, and intelligent systems in ubiquitous computing environments. His research interests span multiple domains within computer science: Semantic Web technologies and ontology reasoning Internet of Things and Cyber-Physical Systems Cloud-Edge computing architectures Knowledge representation and semantic matchmaker systems Mobile and ubiquitous computing applications Analysis of his recent publications (2023-2025) reveals a strong focus on edge-based semantic reasoning, with significant work on the Tiny-ME and Cowl frameworks for lightweight OWL reasoning on resource-constrained devices. His research has increasingly incorporated blockchain technologies into IoT systems and explored the concept of "Internet of Conscious Things" with social capabilities for smart objects. The interdisciplinary nature of his work bridges computer science with healthcare applications, particularly in clinical decision support systems. Dr. Scioscia has collaborated extensively with researchers including Michele Ruta, Eugenio Di Sciascio, Giuseppe Loseto, and Filippo Gramegna across numerous projects spanning more than 15 years of research output.