جست‌وجوی استاد و پژوهشگر

• Algorithms
• Theoretical Computer Science
• Programming Languages
• +۶ مورد دیگر

Ben Rank is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on foundational aspects of computer science with a strong emphasis on theoretical rigor and practical applications. His work bridges multiple domains including algorithms, formal verification, and cyber-physical systems. Research interests include: Algorithmic methods in programming languages and verification Cyber-physical systems design and analysis Security and privacy in distributed systems Social and information systems integration Recent publications explore cutting-edge topics such as adaptive reinforcement learning in shifting environments and ethical considerations in AI systems. No scientific awards or grants are explicitly mentioned in the provided materials.

• Wireless Networks
• Machine Learning
• Federated Learning
• +۳ مورد دیگر

Choong Seon Hong is a Professor at Kyung Hee University's Department of Computer Science and Engineering in Yongin, South Korea. He earned his PhD in Instrumentation Engineering from Keio University, Japan, in 1997. His research focuses on next-generation wireless networks, with emphasis on 6G systems, federated learning, edge computing, and network optimization. Recent collaborative work explores semantic communication, UAV deployment, and multimodal learning frameworks. His publications highlight technical innovations in: Wireless network optimization for terrestrial, aerial, and satellite systems Federated learning architectures with knowledge distillation and prototype transfer Energy-efficient resource allocation in IoT and vehicular networks Security frameworks for EV charging stations and Open RAN systems Current trends show strong collaboration with Zhu Han, Walid Saad, and younger researchers like Apurba Adhikary and Yan Kyaw Tun. The work spans technical solutions for 6G non-terrestrial networks, holographic MIMO systems, and semantic communication frameworks.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر

David Bermbach is a Full Professor at Technische Universität Berlin , leading the Scalable Software Systems group since 2023. His research focuses on distributed systems, serverless computing, and benchmarking, with significant work on edge and fog computing architectures. He is affiliated with the Einstein Center Digital Future and co-chairs interdisciplinary projects like SimRa for bicycle traffic safety. Full Professor, Scalable Software Systems (2023–present) ECDF-Professor, Mobile Cloud Computing (2017–2023) Postdoctoral Researcher (2014–2017) Education : Diploma in Business Engineering (2010) – Karlsruhe Institute of Technology (KIT) PhD in Computer Science (2014, summa cum laude) – KIT Research Interests span distributed systems with emphasis on cloud, edge, and fog computing, serverless architectures, IoT platforms, and benchmarking frameworks. His work addresses consistency-performance trade-offs, resource placement, and interdisciplinary applications in urban mobility and satellite edge computing. Article Trends show a focus on serverless computing (12/15), edge-cloud integration (9/15), and benchmarking methodologies (7/15). Key themes include optimizing function placement, federated learning architectures, and low-earth orbit computing systems. Scientific Awards Best Paper Award – ShutPub (2024) Best Workshop Paper – A Research Perspective on Fog Computing (2017) Best Paper Runner Up – Benchmarking Eventual Consistency (2014) Summa Cum Laude PhD Thesis (2014) Advising & Grants include mentoring students like Tobias Pfandzelter and Trever Schirmer, leading funded projects through the Einstein Center Digital Future, and contributing to 6G network research. His team works on cloud federation, serverless optimization, and real-world IoT applications.

• Artificial Intelligence
• Medical Informatics
• Software Engineering
• +۴ مورد دیگر

Dr. rer. nat. Joscha Grüger is a researcher at the Experience-based Learning Systems department of the University of Trier . His work bridges artificial intelligence, medical informatics, and software engineering, focusing on AI-driven solutions for healthcare and data-intensive applications. Current research projects include: KI-AIM : AI-based anonymization in medicine KIAFlex : Interactive AI assistance for predictive and flexible control in discharge management DaTreFo : Encrypted data stewardship for medical research Pre-OnkoCase : Case-oriented decision support for skin cancer treatment His publications highlight expertise in probabilistic programming, IoT-enhanced event logs, and clinical decision support systems. Collaborations span international conferences like Petri Nets 2025, ICCBR, and RCIS. Contact: Joscha.Grueger@dfki.de .

• Satellite Communications
• 5G/6G Networks
• Non-Terrestrial Networks
• +۵ مورد دیگر

Alberto Gottardi is a Professor at the University of Genoa's Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture, with a distinguished research career spanning over two decades in satellite communications and next-generation networking technologies. His work bridges theoretical research with practical applications in telecommunications infrastructure. Dr. Gottardi's research interests focus on Satellite Communications , 5G/6G Networks , Non-Terrestrial Networks , UAV Communications , Federated Learning , and Internet of Things . His work demonstrates a consistent trajectory from traditional satellite communication protocols toward integrating AI techniques with next-generation wireless networks, particularly focusing on the convergence of terrestrial and non-terrestrial network architectures. Analysis of his recent publications (2022-2025) reveals a strong emphasis on AI-driven approaches for satellite-terrestrial network integration, with particular focus on federated learning applications, UAV communications, and 6G non-terrestrial network architectures. His research increasingly incorporates machine learning techniques to solve traditional telecommunications challenges, showing a clear evolution toward data-driven network optimization. Dr. Gottardi has maintained an exceptionally productive research output, with over 99 publications documented in the dblp database spanning from 2005 to projected 2025 publications. His work demonstrates consistent collaboration with key researchers including Pietro Cassarà (45 joint publications), Manlio Bacco (33), and Erina Ferro (23), indicating stable research partnerships and team leadership. His research has significant practical applications in maritime communications, intelligent transportation systems, and emergency response networks, with several publications addressing real-world implementation challenges in satellite-based IoT systems and vehicular communications.

• High-Performance Computing
• Parallel Programming
• Domain-Specific Languages
• +۳ مورد دیگر

Wenguang Chen is a researcher affiliated with Tsinghua University and Pengcheng Laboratory , specializing in computer science and high-performance computing . His work bridges theoretical advancements with practical applications in domain-specific languages , parallel programming , and machine learning . Research Interests include: Development of modular DSLs for numerical methods (e.g., Mat2Stencil) Performance optimization in distributed and parallel systems Compiler frameworks for privacy-preserving AI (e.g., FHE-based neural network inference) Graph algorithms scaling to trillion-edge datasets Applications of Rust in memory-safe pointer analysis Recent Publications span 2014–2025, focusing on: Parallelization strategies for supercomputing Compiler automation tools Extreme-scale data processing Performance variance diagnosis in production environments

• Machine Learning
• Feature Selection
• Internet of Things
• +۵ مورد دیگر

Liang Hu is a Professor at De Montfort University's School of Computer Science and Informatics, with extensive research in machine learning, feature selection, and Internet of Things applications. His work bridges theoretical advancements in multi-label learning with practical implementations in IoT security and edge computing. PhD from Jilin University (1999) Active researcher with 178 publications (2005-2025) Key collaborator with Hongtu Li, Feng Wang, and Wanfu Gao His research focuses on multi-label feature selection , graph neural networks , and IoT security , developing novel frameworks for heterogeneous information networks, privacy-preserving federated learning, and threat detection in smart environments. His recent work integrates large language models with trigger-action programming systems. Analysis of his 15 most recent publications reveals strong emphasis on multi-view learning (40% of papers), IoT security applications (33%), and graph-based representation learning (27%), demonstrating consistent innovation in handling complex label correlations and heterogeneous data structures. His scientific contributions include novel feature selection methodologies that balance personalized and shared features while minimizing redundancy across multiple views and labels. Liang Hu leads research in edge intelligence and secure IoT programming, with recent projects developing conflict detection frameworks (CCDF-TAP) and privacy-preserving federated graph learning for smart home ecosystems. His work bridges theoretical machine learning with practical cybersecurity implementations.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Rizwan Qureshi is an active researcher and academic specializing in artificial intelligence, machine learning, and their applications in medical imaging and bioinformatics. With a robust publication record spanning from 2017 to 2025, he has established himself as a significant contributor to the fields of computer vision and biomedical AI. His research interests focus on Artificial Intelligence , Machine Learning , Medical Imaging , Computer Vision , and Biomedical Engineering . Qureshi's work demonstrates particular expertise in object detection systems (especially YOLO variants), medical image segmentation, vision-language models, and applications of AI to healthcare problems including lung cancer research and diabetic retinopathy detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward medical applications of AI, with approximately 60% of his work focusing on healthcare-related problems. His research shows increasing emphasis on model robustness, explainability, and handling distribution shifts in real-world applications. The publications span top venues including IEEE Access, IEEE Transactions on Medical Imaging, CVPR, and BIBM. Qureshi maintains extensive collaborations with researchers across multiple institutions, with frequent co-authorship with Hong Yan, Tanvir Alam, Jia Wu, and Sheheryar Khan. His work demonstrates both technical depth in machine learning methodologies and practical application to significant healthcare challenges. While specific details about his academic advising are not evident from the publication record alone, his numerous publications with multiple co-authors suggest active participation in research teams and likely supervision of graduate students. His work shows consistent funding support through publication in reputable journals and conferences.

• Electric Vehicles
• Fuzzy Logic
• Blockchain
• +۵ مورد دیگر

Shahid Hussain is an Associate Professor at King Abdullah University of Science and Technology (KAUST) in the Computer, Electrical and Mathematical Sciences and Engineering Division. His research spans multiple domains including electric vehicle infrastructure, blockchain technology, and intelligent systems for IoT applications, with a focus on practical implementations of computational intelligence techniques. Dr. Hussain's research interests include: Electric Vehicles and Smart Grid Integration Fuzzy Logic and Intelligent Decision Systems Blockchain Applications for Security and Privacy Machine Learning for IoT and Healthcare Applications Energy Management Systems Smart City Infrastructure Development His recent publications demonstrate a strong focus on applying hybrid computational approaches to solve complex engineering problems, particularly in transportation systems and healthcare applications. Dr. Hussain has published extensively in IEEE journals, with particular emphasis on innovative approaches to electric vehicle charging infrastructure, secure IoT systems, and blockchain-enabled solutions for real-world challenges. Dr. Hussain maintains active collaborations with researchers globally, particularly with Reyazur Rashid Irshad, Young-Chon Kim, and Subhasis Thakur. His work bridges theoretical advancements with practical implementations, as evidenced by his research on fuzzy integer linear programming for EV charging stations and blockchain-enabled security frameworks for medical IoT systems.

• Wireless Sensor Networks
• Low-Power Communication
• Backscatter
• +۶ مورد دیگر

Ambuj Varshney is a researcher specializing in low-power wireless communication, visible light networking, and embedded systems. His work explores tunnel diodes for non-contact sensing, backscatter technologies, and edge-based language models. Key contributions include AudioCast for audio-broadcast connectivity, TunnelSense for tunnel diode sensing, and PiXelGen for mixed-reality cameras. Core Research Areas: Wireless Sensor Networks, Backscatter, Tunnel Diodes, IoT, Embedded Systems Recent Trends: Integration of large language models (LLMs) in edge devices, visible light communication, low-power AR networking

• Data Stream Management
• Real-time Data Analytics
• Data Quality
• +۷ مورد دیگر

Sandra Geisler is a Junior Professor for Data Stream Management and Analysis at the Department of Computer Science, RWTH Aachen University, a position she has held since September 2021. She is also the leader of the Digital Health Spaces group at the Fraunhofer Institute for Applied Information Technology (FIT) in St. Augustin, reflecting her dual expertise in academic research and applied digital health solutions. Bachelor/Master: Diploma in Computer Science, RWTH Aachen University (2008) PhD: Doctoral degree in Computer Science, RWTH Aachen University (2016) Her research focuses on data stream systems, real-time analytics, data quality, and their applications in digital health and industrial processes. She has made significant contributions to ontology-based data quality management, edge computing for stream processing, and FAIR data principles. Recent work explores the integration of large language models into data management workflows and the development of privacy-preserving platforms for industrial data exchange. Her recent publications demonstrate a strong trend in distributed and edge-based stream processing, interdisciplinary applications in healthcare and supply chains, and the use of AI for data discoverability and quality. Topics include in-network computing, simulation of edge queries, self-tonometry for glaucoma, and cross-company data sharing with privacy awareness. She has served as Associate Editor for the Data & Knowledge Engineering Journal (Elsevier), Public Relation Chair for QDB Workshop (VLDB 2016), and Workshop Chair for IMMoA and HIMoA workshops. She has also edited a special issue on Information Management in Mobile Applications in the Pervasive and Mobile Computing Journal. Geisler has supervised multiple theses on topics including LLM-based ontology integration, edge anomaly detection, and data ecosystem modeling. She has been actively involved in research grants and projects related to industrial data processing, digital health, and sustainable production. She teaches courses such as Data Stream Management and Analysis and Data Ecosystems Lab. She leads the Digital Health Spaces research group at Fraunhofer FIT, focusing on innovative solutions for health data management and patient-centric digital tools. Her work bridges computer science, healthcare, and industrial applications, promoting secure, efficient, and intelligent data ecosystems.

• Software Engineering
• Green Software Development
• Sustainable Computing
• +۶ مورد دیگر

Félix García is a prominent professor at the University of Castilla-La Mancha in Ciudad Real, Spain, with extensive contributions to software engineering, sustainable computing, and business process management. His research spans over two decades with 187 publications indexed in dblp, demonstrating consistent scholarly productivity and leadership in multiple research areas. Dr. García's research interests focus on critical contemporary challenges in software development, particularly green software engineering, energy efficiency in computing systems, and sustainable software development practices. His work bridges theoretical foundations with practical applications, addressing how software design, implementation, and maintenance impact environmental sustainability. He has pioneered research connecting software quality attributes with energy consumption, examining how design patterns, code smells, and refactoring techniques affect resource usage. His recent publications (2023-2025) reveal a strong focus on cutting-edge topics including Green AI, quantum computing sustainability, and energy-aware programming language design. These works demonstrate his ability to anticipate and address emerging challenges at the intersection of software engineering and environmental sustainability. Dr. García has received significant recognition through numerous collaborations, particularly with Mario Piattini (133 co-authored papers), Francisco Ruiz (63 papers), and María Ángeles Moraga (30 papers), establishing him as a central figure in his research community. His work has appeared in prestigious venues including IEEE Transactions on Software Engineering, Journal of Systems and Software, and ACM Computing Surveys. He has mentored numerous researchers who have become established scholars in their own right, including Javier Mancebo, Laura Sánchez-González, and César Jesús Pardo Calvache. His contributions to gamification in software engineering education through serious games like GLOBAL-MANAGER demonstrate his commitment to innovative teaching approaches.

• Human-Computer Interaction
• Social Computing
• Artificial Intelligence
• +۵ مورد دیگر

Bongwon Suh is a Professor in the Department of Computer Science at the Korea Advanced Institute of Science and Technology (KAIST), College of Computing. With a prolific publication record spanning over two decades, Suh has established himself as a leading researcher in Human-Computer Interaction, Social Computing, and Artificial Intelligence applications. His research interests focus on Human-Computer Interaction, Social Computing, Artificial Intelligence, Large Language Models, Information Visualization, Recommender Systems, Multi-Agent Systems, and Accessibility Technologies. Suh's work often explores the intersection of social dynamics and technological systems, examining how AI and interactive systems can enhance human experiences in diverse contexts including education, entertainment, communication, and accessibility. Recent publications (2023-2025) demonstrate a strong focus on Large Language Model applications, with significant contributions in social simulation for education, conversational AI systems, multi-agent coordination, and accessibility technologies. His work frequently appears in top-tier venues including CHI, CSCW, UIST, and SIGIR, reflecting the high impact of his research. Suh has collaborated extensively with researchers across KAIST and internationally, with frequent co-authors including Changhoon Oh, Kyusik Kim, Hyungwoo Song, and Jeongwoo Ryu. His research program consistently bridges theoretical insights with practical applications, particularly in developing systems that enhance human social experiences through technology.

Emmanuel Baccelliمشاهده پروفایل

استاد

• Internet of Things (IoT)
• Low-power protocols
• Network security
• +۵ مورد دیگر

Emmanuel Baccelli is a Professor for "Open and Secure IoT Ecosystem" at Freie Universität Berlin since September 2019, holding a joint position with Inria and the Einstein Center Digital Future (ECDF). He is also a scientific researcher at Inria since 2007 and co-founder/coordinator of the RIOT open source operating system for IoT devices since 2013. His research focuses on the intersection of low-power protocols, deeply embedded open source software, and security in the Internet of Things (IoT) ecosystem. Baccelli emphasizes the critical trade-off between energy efficiency and security in IoT systems, advocating for privacy-by-design principles and open specifications. His work addresses how users can maintain control over their systems and data in an increasingly connected world. Baccelli's publications demonstrate a clear progression toward secure, efficient IoT systems with recent work focusing on secure firmware updates, TinyML deployment, and privacy-preserving protocols. His research spans from foundational networking protocols to practical implementations for constrained devices, with a consistent emphasis on open source solutions and security-by-design. Baccelli completed his PhD in 2006 at École Polytechnique in Paris on "Routing and Mobility in Large Packet-Based Networks" and received his habilitation from Université Pierre et Marie Curie in 2012. He previously served as a Guest Professor at Freie Universität Berlin in 2013-2014 with a DAAD Grant. His professional activities include significant contributions to IETF standards, particularly RFCs related to routing protocols for low-power networks. Baccelli's research has practical applications across multiple domains including healthcare, smart agriculture, and industrial IoT systems, where security and energy efficiency are paramount concerns.

• Wireless Communications
• 5G/6G Networks
• Non-Terrestrial Networks
• +۶ مورد دیگر

Muhammad Zeeshan Shakir is a Professor in the Department of Electronic and Electrical Engineering at the University of Glasgow's College of Science and Engineering. With over 120 publications spanning from 2007 to present, his career demonstrates sustained research excellence in wireless communications and networking technologies. His work bridges theoretical foundations with practical applications, particularly in next-generation communication systems. Professor Shakir's research interests focus on the cutting edge of wireless technology evolution. His work spans 5G/6G networks, non-terrestrial communications, Internet of Things, and machine learning applications in networking. Early in his career, he made significant contributions to cognitive radio and device-to-device communications, which laid the foundation for his current work on 6G architectures and non-terrestrial networks. His recent publications demonstrate a strategic expansion into AI-driven networking solutions, edge computing applications, and metaverse-enabling technologies. This research trajectory reflects both technical depth in wireless communications and adaptability to emerging technological paradigms. His publication portfolio shows a consistent pattern of impactful contributions, with recent work analyzing trends in airborne networks, reconfigurable intelligent surfaces, and multi-RAT selection in 5G/6G environments. These publications frequently address critical industry challenges including coverage extension, energy efficiency, and seamless connectivity across heterogeneous network environments. His work on emotion recognition at the edge and virtual metaverse environment development demonstrates expanding interdisciplinary reach. Professor Shakir has successfully mentored numerous PhD students including Yusufu Gambo, Ifiok Anthony Umoren, and Cezar Anicai, whose research spans smart learning environments, energy trading in microgrids, and federated learning applications. His collaborative approach is evident through extensive co-authorship networks, particularly with researchers at University of Glasgow and international institutions. His research has been supported through various grants focused on next-generation wireless systems, with particular emphasis on practical implementations that address real-world connectivity challenges. Current projects appear to focus on integrating AI with wireless infrastructure to create more adaptive, efficient, and user-centric communication systems.