جست‌وجوی استاد و پژوهشگر

• Intelligent Transportation Systems
• Machine Learning
• Computer Networks
• +۳ مورد دیگر

Zongtao Duan is an active researcher with a focus on intelligent transportation systems, cybersecurity, and machine learning applications. His work spans areas including traffic flow prediction, silent data corruption detection, vehicular networks security, and deep learning architectures for autonomous systems. He collaborates frequently with colleagues like Lei Tang and Junchi Ma, publishing in top-tier journals such as IEEE Transactions on Intelligent Transportation Systems and Future Generation Computer Systems. His research emphasizes practical solutions for urban mobility challenges, including ridesharing optimization and railway congestion control, while also addressing foundational issues in hardware reliability and error propagation modeling. Recent contributions include advancements in multimodal retrieval systems and knowledge graph applications in scientific data management. Zongtao has contributed to over 40 peer-reviewed publications since 2004, with notable work in 2025 exploring SDC evaluation frameworks and scenario-based testing for autonomous vehicle safety. His interdisciplinary approach bridges computer science, transportation engineering, and cybersecurity to address real-world technical challenges.

• Wireless Communications
• Information Theory
• Network Security
• +۶ مورد دیگر

Stephen B. Wicker is a Professor at Cornell University's College of Engineering with a distinguished research career spanning over three decades. His work has significantly impacted the fields of wireless communications, information theory, privacy, and security. Wicker has published extensively in top-tier journals and conferences, demonstrating consistent scholarly productivity from the early 1990s through 2021. His research interests include: Wireless network security and privacy Information theory and error control coding Location-based services and privacy implications Digital rights management and consumer protection Blockchain applications for IoT environments Ethical considerations in technology development Legal implications of digital technologies Wicker's recent publications reveal a strategic evolution in his research focus, increasingly addressing the intersection of technical systems with legal and ethical considerations. His work on smartphone privacy and the Fifth Amendment, eBook surveillance, and zero-day exploits demonstrates his unique ability to bridge technical expertise with societal implications. This interdisciplinary approach has positioned him as a thought leader in understanding how technological capabilities intersect with constitutional rights and civil liberties. His scholarly impact is evident through his extensive collaboration network, with significant work alongside researchers like Kolbeinn Karlsson on blockchain for IoT applications, Dipayan P. Ghosh on eBook privacy issues, and Shion Guha on social surveillance in location-based networks. His research has appeared in prestigious venues including Communications of the ACM, IEEE Proceedings, and IEEE Transactions on Information Theory. Wicker has mentored numerous students who have become productive researchers in their own right, including Xiao-an Wang, Hazer Inaltekin, and Kolbeinn Karlsson, who frequently appear as co-authors on his publications. His work continues to address emerging challenges at the intersection of technology, law, and society, making significant contributions to both academic discourse and public policy discussions.

• Mobile Application Development
• Programming Languages
• Compiler Construction
• +۳ مورد دیگر

Prof. Dr. Dragan Macos is a Professor at the Berlin University of Applied Sciences, affiliated with the Building Construction Department. His research focuses on mobile application development (particularly iOS), compiler construction for functional programming languages, and simulation models in transportation and wireless communication. He teaches courses on media informatics, compiler design, and software project management at both bachelor's and master's levels. His work spans technical contributions in high-availability data center backends, simulation performance optimization, and toll system modeling. Recent publications emphasize distributed systems, parallelization techniques, and the application of simulations in real-world scenarios. No scientific awards are listed, but his engagement in international conferences highlights active research collaboration. Teaching: Media Informatics, Compiler Construction, and TI Bachelor programs Research: Mobile apps, functional programming languages, stochastic traffic models Key Publications: Over 10 peer-reviewed articles between 2010–2017 on simulation, networking, and embedded systems Personal interests include Istrian culture and Croatian heritage, reflecting a passion for regional history and natural landscapes.

• Databases and Information Systems
• Big Data
• Data Management
• +۳ مورد دیگر

Dr.-Ing. Julia Othlinghaus-Wulhorst is a Researcher in the Department of Databases and Information Systems at the University of Würzburg, Faculty of Computer Science and Mathematics. She specializes in databases, data engineering, and Big Data. Her work focuses on query processing, data quality, and cloud resource optimization in distributed systems. Education: Diplom-Informatikerin (FH) (FH Diploma in Computer Science). Research Interests: Databases and Information Systems, Big Data, Data Management, Data Engineering, Data Science, Data Quality. She explores scalable database systems, data quality frameworks, and cloud-based resource allocation. Recent Work Trends: Her recent articles address large-scale distributed databases, data quality in Big Data environments, and cloud resource optimization. These reflect a focus on improving system efficiency and data reliability in modern computing landscapes. Awards: Best Paper Award at ICDE 2023 Best Paper Award at ICDE 2022 Outstanding Reviewer Award at ICDE 2022 Advising & Grants: Advises PhD students in data systems and participates in DFG-funded research projects. Active in collaborative initiatives on database optimization and Big Data analytics. Labs & Teams: Member of the Information Systems Lab and Big Data Analytics Group at the University of Würzburg.

• Wireless Communications
• Massive MIMO
• UAV Networks
• +۵ مورد دیگر

David Gesbert is a Professor at Eurecom in Sophia Antipolis, France. His research focuses on advanced wireless communication systems, including Massive MIMO, UAV networks, federated learning, and 6G technologies. He has contributed extensively to channel prediction, beamforming optimization, and machine learning applications in wireless networks. His work spans theoretical analysis and experimental validation, addressing challenges in aerial communication, energy efficiency, and network intelligence. Key areas include UAV-aided localization, cooperative satellite-RIS beamforming, and environment-aware communication frameworks. Research interests emphasize wireless systems, signal processing, and machine learning integration. Notable projects include optimizing UAV trajectories for IoT networks, enhancing 5G connectivity for aerial highways, and developing efficient federated learning strategies for distributed networks. His contributions bridge theoretical advancements with practical implementations, particularly in dynamic environments and future communication architectures. Core Expertise: 5G/6G, Massive MIMO, UAV Networks, Federated Learning Key Projects: Channel quality prediction via ML, RIS-enabled beamforming, global channel charting Impact: Over 400 publications, with focus on scalability and real-world applicability

• French Language Practice
• Romance Studies
• Computer Vision
• +۳ مورد دیگر

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Anh Nguyen-Duc is a researcher affiliated with the University of South-Eastern Norway (School of Business) and Norwegian University of Science and Technology , focusing on Software Engineering , Artificial Intelligence , and Startup Research . His work bridges technical and entrepreneurial aspects of software development. University of South-Eastern Norway, School of Business Norwegian University of Science and Technology Research Themes : Software startups, AI integration in development, agile practices, technical debt, and requirements engineering. Recent studies include fairness in educational machine learning models and generative AI adoption in software organizations. Collaborative work with Pekka Abrahamsson, Xiaofeng Wang, and Kai-Kristian Kemell. Key Trends : 2025 studies on AI fairness and generative AI in software processes; 2024 work on hybrid work impacts; 2023 research on startup AI tools and education. Advising & Grants : Collaborates with academic institutions and industry partners on empirical studies about software development practices.

• Network Virtualization
• Software-Defined Networking (SDN)
• Context-Aware Systems
• +۴ مورد دیگر

Ahmed Karmouch is a Professor at the University of Ottawa, Canada, affiliated with the College of Engineering and Computer Science. His research focuses on Network Virtualization , Software-Defined Networking (SDN) , and Context-Aware Systems , with significant contributions to Cloud Computing Resource Allocation , Programmable Data-Planes , and Overlay Networks . He has co-authored over 161 publications since 1980, emphasizing scalable infrastructure management, QoS optimization, and autonomic network design. His recent work includes machine learning integration in P4-programmable networks (2024) and survivable IaaS management with SDN (2021). Publications span IEEE Transactions and ACM journals, with frequent collaborations with Nancy Samaan, Abdallah Jarray, and Hamid Harroud. Key research themes: Network Programmability Service Specific Overlay Networks Autonomic Network Management ML for Traffic Optimization Geographically Distributed Cloud

• Real-Time Systems
• Database Systems
• Distributed Systems
• +۱۰ مورد دیگر

Ming Xiong is a researcher affiliated with Bell Labs , focusing on real-time systems, temporal consistency, and data freshness in embedded environments. His work spans concurrency control protocols, deferrable scheduling algorithms, and wireless communication systems. Key Research Areas: Real-time databases, XML data integration, location management in mobile networks, IoT semantic communication, and wireless channel modeling. Collaborations: Frequent co-authorship with Song Han, Deji Chen, Kam-yiu Lam, Krithi Ramamritham, and Wenfei Fan. His recent publications (2023-2025) emphasize semantic communication for IoT and wireless systems, temporal consistency in cyber-physical systems, and environmental data analysis via remote sensing. While no explicit awards or educational background are listed, his contributions to IEEE journals and conferences highlight expertise in real-time transaction processing and distributed systems.

• Web Services
• Formal Verification
• Software Security
• +۳ مورد دیگر

Xiang Fu is a researcher in the Department of Computer Science at Hofstra University, with affiliations to institutions including Georgia Southwestern State University, University of California, Santa Barbara, and Massachusetts Institute of Technology. His work spans formal verification of web services, software security, zero-knowledge proofs, and automated testing frameworks. His research interests include: Web Services Formal Verification Software Security Zero-Knowledge Proofs Automated Testing Software Engineering Recent publications focus on zero-knowledge auditing for financial systems, formal verification of web services, and secure software analysis. Notable tools developed include APOGEE for automated grading and WISEngineering for scalable online learning. No explicit scientific awards are documented in the provided data.

• Algorithms
• Theory
• Logic Programming Languages
• +۹ مورد دیگر

Thomas Davidson is a Researcher affiliated with the Max Planck Institute for Software Systems (MPI-SWS). His work focuses on foundational areas of computer science including algorithms, theory, programming languages, verification, and cyber-physical systems. He explores topics such as distributed systems, security, privacy, and the intersection of systems research with visualization techniques. His research spans both technical and human-centered aspects of computing, such as improving debugging tools through visualization, analyzing energy metrics in systems, and applying visual analytics to literary studies. Recent work emphasizes the role of cognitive biases in data communication and the challenges of energy efficiency in modern systems. Davidson's publications highlight a blend of theoretical rigor and practical applications, particularly in bridging gaps between system performance analysis and human understanding. His work contributes to both technical communities (e.g., systems researchers) and interdisciplinary fields (e.g., digital humanities).

• Blockchain Technology
• Cybersecurity
• Cloud Computing
• +۳ مورد دیگر

Wei-Tek Tsai is a Professor affiliated with Arizona State University's School of Computing, Informatics, and Decision Systems Engineering, and previously with Beihang University and the University of Minnesota. His research focuses on blockchain technology, cybersecurity, cloud computing, and software engineering. He has contributed significantly to areas such as blockchain-based systems, smart contract development, distributed testing methodologies, and crowdsourcing in software development. His work emphasizes practical applications like fraud detection, supply chain supervision, and decentralized financial systems. Research interests include permissioned blockchains, anomaly detection in cryptocurrency transactions, and optimizing consensus mechanisms. He has published extensively in top-tier journals and conferences, including IEEE Transactions and international workshops on software engineering and security. His work bridges theoretical advancements with real-world challenges in distributed systems and cybersecurity.

• Distributed Systems
• Graph Processing
• Blockchain Technology
• +۴ مورد دیگر

Ruben Mayer is a prominent researcher in distributed systems, graph processing, and blockchain technology, affiliated with the University of Stuttgart. He holds a PhD from the same institution (2018) and has authored over 100 publications in top-tier conferences and journals such as SIGMOD, VLDB, and ACM Computing Surveys. His work focuses on scalable deep learning, federated learning, and edge computing, with applications in distributed systems and privacy-preserving AI. Key research interests include optimizing distributed infrastructure for graph neural networks, exploring cross-cloud training challenges, and advancing federated learning methodologies. He has contributed to foundational studies on blockchain optimization, edge computing reliability, and ethical AI compliance with regulations like the European AI Act. Recent work highlights include WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks (2025) and A Survey on Efficient Federated Learning Methods for Foundation Model Training (2024), demonstrating his leadership in advancing scalable machine learning systems. His research bridges theoretical insights with practical system design, addressing critical challenges in modern distributed infrastructures.

• Multi-agent systems
• Reinforcement learning
• Trust management
• +۵ مورد دیگر

Athirai Aravazhi Irissappane is an Assistant Professor at Nanyang Technological University's College of Computing and Data Science, School of Computer Science and Engineering. With a PhD in Trust oriented decision making via POMDPs completed in 2016, they have established themselves as a prominent researcher in multi-agent systems and reinforcement learning. Their academic journey shows a clear progression from foundational work in trust management to cutting-edge research in deep reinforcement learning frameworks. Dr. Irissappane's research interests focus on multi-agent systems , reinforcement learning , trust management , and recommender systems . They have made significant contributions to the field of multi-objective reinforcement learning, developing frameworks that address complex policy distributions and cooperative behavior in multi-agent environments. Their work bridges theoretical advances with practical applications, particularly evident in their contributions to the RecSys Challenge 2023. Analysis of their recent publications (2020-2024) reveals a strong emphasis on Advanced reinforcement learning architectures for multi-agent cooperation Privacy-preserving recommendation systems Automated data quality improvement for recommendation tasks Scalable frameworks for reinforcement learning research These works demonstrate increasing sophistication in handling complex decision-making scenarios while addressing practical constraints like computational efficiency and privacy concerns. Dr. Irissappane has been actively involved in major academic initiatives including the RecSys Challenge 2023 and has contributed to comprehensive guides on multi-objective reinforcement learning that have become valuable resources for the research community. Their collaborative work spans multiple institutions and has resulted in publications in top-tier venues including IEEE Transactions, Autonomous Agents and Multi-Agent Systems journal, and conference proceedings of AAAI, IJCAI, and AAMAS.

• High Performance Computing
• Network Simulation
• Parallel Discrete Event Simulation
• +۵ مورد دیگر

Kevin A. Brown is an active researcher in the field of High Performance Computing with a strong publication record spanning over a decade. His work primarily focuses on HPC network simulation, performance analysis, and optimization of parallel computing systems. He has collaborated extensively with researchers including Christopher D. Carothers, Robert B. Ross, and Satoshi Matsuoka across multiple institutions. Dr. Brown's research interests center around network simulation techniques, particularly Parallel Discrete Event Simulation (PDES) for modeling HPC networks. His recent work explores multi-fidelity network simulation frameworks, surrogate modeling for performance prediction, and machine learning applications for network traffic forecasting. He has made significant contributions to understanding network congestion, quality-of-service mechanisms, and the interference between different types of traffic in HPC environments. His publication record shows consistent output with 19 publications documented between 2014 and 2025, with increased productivity in recent years. The 2023-2025 period shows particularly strong activity with 12 publications, indicating ongoing research momentum. His work appears primarily in top HPC conferences including SIGSIM-PADS, CLUSTER, and ICPP. Notable recent contributions include the development of MFNetSim for multi-traffic modeling of Dragonfly systems, research on zombie packet techniques for hybrid PDES simulation, and work on steady-state fluid models for HPC networks. His research demonstrates a clear trajectory from fundamental network performance analysis toward more sophisticated simulation frameworks incorporating machine learning techniques.