جست‌وجوی استاد و پژوهشگر

Jens Rettkowskiمشاهده پروفایل

استاد

• Computer Architecture
• Embedded Systems
• Real-Time Systems
• +۳ مورد دیگر

Prof. Jens Rettkowski is a leading academic specializing in advanced computing architectures, particularly focusing on multi-core systems, reconfigurable hardware (FPGAs), and networks-on-chip (NoC). His work bridges theoretical computer architecture with practical applications in embedded systems, real-time processing, and autonomous technologies. Research Interests: Multi-Core Architecture Design and Optimization Networks-on-Chip (NoC) for High-Performance Systems Reconfigurable Computing and FPGA-Based Systems Hardware-Software Co-Design for Embedded Applications Real-Time Systems and Energy Efficiency Sensor Data Fusion and Autonomous Driving Publications reflect a strong focus on optimizing communication and computation in heterogeneous systems, with recent work emphasizing power savings in multi-core architectures and event-based debugging frameworks. His contributions include frameworks like MPSoCSim for simulating reconfigurable systems, and software-defined FPGA approaches for adaptive computing. He has led research in robotics applications for assisted living environments, combining navigation algorithms with real-time sensor processing. His work also addresses security aspects of FPGA configuration and partial bitstream analysis using machine learning.

• Mobile and Ubiquitous Computing
• Internet of Things
• Context-Aware Applications
• +۵ مورد دیگر

Dr. Dirk Bade is a Researcher at the University of Hamburg , affiliated with the Department of Informatics under the Faculty of Mathematics, Informatics and Natural Sciences. His work focuses on Mobile and Ubiquitous Computing , Internet of Things , Context-Aware Systems , and Blockchain Applications . He is actively involved in projects such as SANE (Smart Networks for Urban Citizen Participation), CloudAware (Context-adaptive Middleware), and ContAgency (Context Data Brokerage). Research Guest at VSYS, University of Hamburg (since 2013) PhD in Informatics (2013), University of Hamburg Diploma (2007) and Bachelor (2003) in Informatics, University of Hamburg His research explores smart city infrastructures , context-aware computation offloading , and security implications of sensor data . Key contributions include CloudAware (middleware for mobile cloud adaptation) and Incolum (citizen-centric sensor data marketplaces). Publications highlight trends in mobile edge computing (2015-2016), smart cities (2019-2021), and context-aware middleware (2011-2013). His work bridges agent-oriented software engineering with blockchain-based information markets . Dirk has supervised over 50 theses, including studies on blockchain reputation systems , mixed-reality negotiation tools , and sensor-supported indoor localization . He teaches courses on mobile computing , smart cities , and context-aware systems , with a focus on practical software development and ubiquitous applications .

Debopam Bhattacherjeeمشاهده پروفایل

پژوهشگر

• AI Systems
• Networked Systems
• Low-Earth Orbit Satellite Networks
• +۱ مورد دیگر

Debopam Bhattacherjee is a Senior Researcher at Microsoft Research, Bangalore, focusing on AI Systems, Networked Systems, and Low-Earth Orbit (LEO) Satellite Networks. He holds a PhD in Computer Science from ETH Zürich (2021) and a dual Erasmus Mundus Master's in Security and Mobile Computing. His work has been recognized with the IRTF Applied Networking Research Prize (2020) and multiple Best Paper/Dataset Awards. Research interests include LEO satellite networks, sustainable AI systems, network topology design at hypersonic speeds (27,000 km/hour), and green computing via modular data centers. His publications span ACM SIGCOMM, USENIX ATC, IEEE COMSNETS, and arXiv, with recent patents on AI-driven LLM training and inference systems. Key awards: IRTF Applied Networking Research Prize (2020) Best Paper Award at ACM IMC 2020 Best Dataset Award at ACM IMC 2020 He has supervised numerous PhD/Master's students at ETH Zürich and Microsoft Research, including Tella Rajashekhar Reddy, Shubham Tiwari, and Saksham Bhushan. His educational background includes a B.E. in Computer Science & Engineering from Jadavpur University (2009) and a research fellowship at the Max Planck Institute (2019).

Mirco Sternمشاهده پروفایل

پژوهشگر

• Sensor Networks
• Database Systems
• Service Composition
• +۲ مورد دیگر

Mirco Stern is a researcher at the Institute of Information Management (IPD Böhm) at Karlsruhe Institute of Technology. His work focuses on sensor networks, database systems, and service composition technologies. Key research areas include distributed query processing, data quality guarantees, and optimization of join operations in resource-constrained environments. Role : Researcher, Institute of Information Management Location : Karlsruhe, Germany Research trends highlight his expertise in sensor network optimization (join processing, wavelet transforms) and service composition (semantic matchmaking, automated integration). Publications span ACM SIGMOD, VLDB, and ECOWS conferences, emphasizing algorithm design and system efficiency.

Wei Gengمشاهده پروفایل

پژوهشگر

• Edge Computing
• Fog Computing
• Networked Systems
• +۴ مورد دیگر

Wei Geng is a doctoral researcher and Ph.D. candidate at the Chair of Connected Mobility (I11) in the Department of Informatics at Technical University of Munich (TUM). He is supervised by Prof. Dr.-Ing. Jörg Ott at TUM, advised by Prof. Nitinder Mohan at TU Delft, and mentored by Prof. Dirk Kutscher at HKUST(GZ). His educational background includes: Ph.D. Student at CIT, Technical University of Munich, Munich, Germany (2024.10~) M.Phil. at The Hong Kong University of Science and Technology, Guangzhou, China (2022.9~2024.10) M.Eng. at Fudan University, Shanghai, China (2017~2020) B.Eng. at Harbin Engineering University, Harbin, China (2013~2017) Visiting Student at Politecnico di Milano, Milan, Italy (2023.3) Wei Geng's research focuses on Accelerating Critical Applications by Edge Computing and AI . His work addresses Edge and Fog Computing for latency-sensitive applications, particularly offloading deep learning inference tasks from mobile/IoT devices to edge servers. He investigates ML-enabled networked systems including ML-based task scheduling and distributed inference frameworks. His earlier work at Huawei involved programming language runtime and OS optimization, specifically Golang runtime scheduler and OS kernel improvements. Wei Geng teaches and supervises at TUM: Summer Term 2025: Seminar - Hot Topics in Edge Computing (IN2107, IN4417) Winter Term 2024/25: Master Practical Course: Edge Computing and the Internet of Things (IN2106, IN4261) He actively seeks Master's and Bachelor's thesis students to supervise and maintains regular office hours (13:00-14:00 Tue, Wed) in office 01.05.040. Prior to his Ph.D., he worked as a Full-time R&D Software Engineer at Huawei Technologies (2020-2022), with internships at Ant Group (2019) and eBay (2018).

• Wireless Communications
• UAV Communications
• Wireless Power Transfer
• +۳ مورد دیگر

Dr. Xiaopeng Yuan serves as a Researcher at the Chair of Information Theory and Data Analytics within the Faculty of Electrical Engineering and Information Technology at RWTH Aachen University, Germany. His work focuses on advancing UAV-assisted wireless communication systems and energy transfer mechanisms, with publications appearing in top-tier IEEE journals and conferences since 2019. His research centers on UAV trajectory optimization, wireless power transfer (WPT), finite blocklength (FBL) communications, and resource allocation for ultra-reliable low-latency communication (URLLC) systems. Key contributions include modeling nonlinear energy harvesting in UAV networks, developing covert communication techniques under detection constraints, and optimizing multi-UAV coordination for data collection and power transfer efficiency. His methodology frequently combines analytical optimization with deep reinforcement learning for complex network scenarios. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) UAV path planning for energy-constrained networks emphasizing 3D trajectory optimization and directional antenna integration; (2) Finite blocklength regime analysis for short-packet communications in URLLC and WPT systems; (3) Security-aware resource allocation for covert data collection and secrecy throughput maximization. His work consistently addresses practical implementation challenges like nonlinear energy harvesting and no-fly zones.

Christian Krupitzerمشاهده پروفایل

استاد

• Self-aware Computing Systems
• Self-adaptive Systems
• Autonomic Computing
• +۱۲ مورد دیگر

Christian Krupitzer is a Tenure Track Professor at the Department of Food Informatics, University of Hohenheim since October 2020. Previously, he served as a post-doctoral research group leader at the Chair of Software Engineering (Informatik II) at the University of Würzburg (2018–2020) and completed his doctoral research at the Chair of Information Systems II (University of Mannheim, 2012–2018). He holds a Master of Business Informatics from the University of Mannheim and an Erasmus stay at Vrije Universiteit Amsterdam. His research focuses on Self-aware Computing Systems, Autonomic Computing, IoT, and Cyber-physical Systems, with applications in Industry 4.0 and Intelligent Transportation Systems. He has organized multiple conferences and workshops, including the International Workshop on Self-aware Computing Systems (SeAC) and the Organic Computing Doctoral Dissertation Colloquium. His teaching roles include co-lecturing on 'Self-aware Computing Systems' and 'Advanced Programming' at the University of Würzburg. He has supervised several theses, including works on multirotor formations and platooning coordination. Notable awards include DAAD grants and Best Paper nominations. His contributions span academic leadership, with roles such as Steering Committee Secretary for the International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) and IEEE membership. Research highlights include frameworks for self-adaptive systems and infrastructure-aided platooning management in autonomous vehicles.

• High-Performance Computing
• Parallel Programming
• Task Parallelism
• +۶ مورد دیگر

Stephen L. Olivier is a prominent researcher in high-performance computing at Sandia National Laboratories, with a distinguished publication record spanning nearly two decades. His work focuses on parallel programming models, performance optimization, and energy-efficient computing across diverse architectures including CPUs, GPUs, and FPGAs. Olivier has made significant contributions to OpenMP standards and Kokkos programming model development, collaborating extensively with Department of Energy national laboratories and international research teams. Olivier's research interests center on task parallelism, memory management in distributed systems, and performance portability across heterogeneous architectures. His work addresses critical challenges in exascale computing, including efficient task scheduling for unbalanced workloads, power management in large-scale systems, and optimization of communication patterns. More recently, he has expanded his research into medical imaging applications, applying high-performance computing techniques to tuberculosis detection in rural healthcare settings. Analysis of Olivier's recent publications (2021-2024) reveals a strong focus on practical performance engineering for next-generation computing platforms. His work spans traditional HPC domains while increasingly incorporating data science applications and medical imaging analysis. The research demonstrates consistent innovation in parallel programming models, particularly around OpenMP tasking and Kokkos abstractions, with growing emphasis on energy efficiency and hardware-specific optimizations for emerging architectures. Olivier has maintained a prolific research output with numerous publications in top-tier conferences including SC, IPDPS, and IWOMP. His collaborative work extends across multiple Department of Energy laboratories and international institutions, reflecting the interdisciplinary nature of modern high-performance computing research. While specific grant information isn't detailed in the publication record, his work on DOE systems suggests significant involvement in national supercomputing initiatives.

• Cognitive Radio Networks
• Reinforcement Learning Applications
• Wireless Communications
• +۷ مورد دیگر

Professor Kok-Lim Alvin Yau is an accomplished academic at Taylor's University in Malaysia, specializing in cognitive radio networks, reinforcement learning applications, and intelligent communications systems. With over 75 publications spanning from 2011 to 2025, he has established himself as a leading researcher in wireless networking and artificial intelligence applications in communications. His research interests encompass cognitive radio networks, reinforcement learning for networking applications, vehicular communications, edge computing, and the integration of AI in communication systems. Professor Yau's work demonstrates a consistent focus on applying machine learning techniques to solve complex networking challenges, particularly in dynamic spectrum access and resource allocation problems. Analysis of his recent publications (2023-2025) reveals a strategic shift toward more interdisciplinary research, combining reinforcement learning with generative AI techniques, exploring semantic communications, and addressing challenges in 6G-enabled vehicular networks. His work shows increasing emphasis on practical implementations and real-world applications of theoretical concepts, particularly in intelligent transportation systems and edge computing environments. Professor Yau has made significant contributions through numerous survey papers that have helped shape research directions in cognitive radio networks and reinforcement learning applications. His editorial work for special issues in IEEE Access and other journals demonstrates his standing in the academic community. He has successfully supervised multiple PhD students including Geong Sen Poh, Mee Hong Ling, and Aqeel Raza Syed, with whom he continues to collaborate on research projects. His international collaborations span institutions in China, Pakistan, and the UK, reflecting the global recognition of his research expertise.

• Networking
• Edge Computing
• 5G/6G Technologies
• +۵ مورد دیگر

Dr. Giang T. Nguyen is an academic researcher affiliated with Dresden University of Technology (Germany), specializing in advanced networking technologies. His primary affiliation is with the College of Computer Science and the Department of Networking and Communication Systems. He collaborates extensively with Prof. Frank H. P. Fitzek and other researchers on cutting-edge projects. Research Interests: Giang's work focuses on Network Functions Virtualization (NFV) , Edge Computing , Time-Sensitive Networking (TSN) , 5G/6G Technologies , and In-Network Computing (COIN) . He explores applications in industrial automation, tactile internet, and immersive media delivery, with a strong emphasis on latency reduction and network reliability. Publications: His recent work includes advancements in programmable network coding, TSN testbed development, and latency-optimized architectures for XR and IoT systems. Over 100+ publications since 2013 reflect his contributions to edge computing frameworks, network simulation tools (e.g., ns-3, OMNeT++), and collaborative SLAM systems. Key Projects: He leads initiatives like the TSN-FlexTest measurement testbed and the NET Playground heterogeneous network lab. His research bridges theoretical networking concepts with practical implementations in industrial robotics and emergency response systems.

• Artificial Intelligence
• Machine Learning
• Edge Computing
• +۷ مورد دیگر

Huan Liu is a Professor at Arizona State University's School of Computing, Informatics, and Decision Systems Engineering, with research spanning artificial intelligence, edge computing, and interdisciplinary applications. His work bridges theoretical advances with real-world implementations across healthcare, transportation, and environmental systems. His primary research interests include: Artificial Intelligence and Machine Learning for complex system optimization Edge-Cloud Computing architectures for IoT and vehicular networks Medical image analysis and clinical decision support systems Graph neural networks and multimodal learning Privacy-preserving federated learning frameworks Recent publications demonstrate strong emphasis on applied AI, with 15+ 2025 papers addressing UAV swarm intelligence, medical diagnostics, and sustainable transportation through novel algorithmic approaches. Publication trends reveal consistent leadership in: Developing efficient edge computing solutions for dynamic environments Creating medical AI tools with clinical validation Innovating in graph-based and multimodal learning Applying optimization techniques to logistics and environmental challenges His work frequently appears in top venues including IEEE Transactions, Bioinformatics, and AAAI.

• Mobile Cloud Computing
• Internet of Things Security
• Blockchain Applications
• +۶ مورد دیگر

Atta ur Rehman Khan is an Associate Professor in the Department of Computer Science at the College of Electrical and Computer Engineering, COMSATS Institute of Information Technology, with additional affiliation at University of Malaya, Kuala Lumpur, Malaysia. His research spans multiple cutting-edge domains in computer science, with particular focus on mobile cloud computing, IoT security, and blockchain applications. Dr. Khan's research interests center around secure and efficient computing systems, with emphasis on mobile cloud environments, IoT security frameworks, and blockchain-based solutions for various applications. His work demonstrates a strong interdisciplinary approach, bridging theoretical computer science with practical applications in cybersecurity, healthcare, environmental monitoring, and smart city infrastructure. His research methodology often combines traditional computer science techniques with emerging AI and machine learning approaches to solve complex problems in distributed systems. Analysis of Dr. Khan's publication trends reveals a consistent research trajectory evolving from foundational work in mobile cloud computing and wireless networks toward more specialized applications in blockchain, IoT security, and AI-enhanced cybersecurity solutions. His recent work shows increasing interdisciplinary collaboration, particularly with healthcare researchers on medical diagnostics applications and environmental scientists on sustainability projects. The thematic progression demonstrates how his core expertise in distributed systems has expanded to address emerging challenges in secure computing across multiple domains. Dr. Khan has maintained active research supervision, mentoring numerous students who have contributed to publications across his research areas. His collaborative work spans multiple international institutions, reflecting a strong network of academic partnerships particularly with researchers in Malaysia, Pakistan, and other international collaborators. His laboratory work appears focused on applied research in secure distributed systems, with particular emphasis on blockchain-enabled security frameworks for IoT environments, mobile cloud computing architectures, and AI-enhanced cybersecurity solutions. The research group maintains strong industry connections, particularly with technology companies working on IoT and blockchain applications.

• Neural Networks
• Medical Image Analysis
• Computer Vision
• +۵ مورد دیگر

Zhigang Li is a Professor in the Department of Computer Science at South China University of Technology's School of Computer Science and Engineering. His research spans multiple technical domains with significant contributions to neural networks, medical imaging, computer vision, and sensor technologies. Recent collaborations include work with Northwestern Polytechnical University, Hong Kong University of Science and Technology, and various medical research institutions. Dr. Li's research interests focus on neural network architectures, particularly small-world and feedforward networks for system modeling and medical applications. His work bridges computer science with practical applications in healthcare (EEG analysis, schizophrenia detection, liver transplant allocation), environmental monitoring (wastewater treatment), and engineering systems (CMOS image sensors, UAV networks). His research demonstrates strong interdisciplinary connections between theoretical computer science and real-world problem solving. Analysis of his recent publication trends shows increasing focus on medical applications of AI, with significant work in brain functional network analysis, depression recognition, and schizophrenia detection. His technical contributions include novel neural network architectures, efficient sensor systems, and advanced signal processing techniques. The publications reveal a consistent pattern of high-quality output in top-tier journals across multiple disciplines. Dr. Li has received recognition through publications in prestigious venues including IEEE Transactions, Medical Image Analysis, and Expert Systems with Applications, though specific awards aren't documented in the provided bibliography. His work demonstrates significant impact across multiple fields, particularly in applying computational methods to healthcare challenges. His research program includes collaborations with medical researchers for brain imaging applications, electrical engineers for sensor development, and computer scientists for network architecture design. Current projects appear focused on multi-view brain network analysis, energy-efficient sensor systems, and medical AI applications with potential clinical impact.

• Mobile Edge Computing
• Cloud Computing
• Internet of Things
• +۱۲ مورد دیگر

Junaid Shuja is a researcher with significant contributions to Mobile Edge Computing , Cloud Environments , and IoT Systems . Collaborating with scholars like Kashif Bilal , Abdullah Gani , and Ehzaz Mustafa , his work spans computation offloading, resource allocation, and security frameworks. Research Highlights 2017: Analysis of Vector Code Offloading in Heterogeneous Architectures 2021: Survey on Machine Learning for Edge Caching 2023: Reinforcement Learning for Computation Offloading in Vehicular Networks 2024: Blockchain Applications in Land Lease Systems and Employee Transfers 2025: Deep Reinforcement Learning for Resource Optimization His recent work focuses on Deep Learning and Blockchain for latency-sensitive applications in IoT and vehicular networks, published in IEEE Access , Cluster Computing , and Telecommunication Systems . Key co-authors include Faisal Rehman , Abdallah Namoun , and Muhammad Bilal .

• Computer Science
• Networking
• Wireless Communications
• +۶ مورد دیگر

Tasneem S. J. Darwish is a researcher affiliated with the University of Technology, Malaysia , specifically within the Faculty of Computing and the Pervasive Computing Research Group . Her work spans multiple domains in Computer Science and Networking , with a focus on Satellite Communications , Wireless Sensor Networks , and Intelligent Transportation Systems . She has contributed extensively to standardization frameworks for LEO satellites in 5G/Beyond networks and developed innovative routing protocols for urban vehicular networks. Notable research trends include: Advancements in LEO Satellite Networking (2021-2025) Integration of Graph Theory for handover frameworks (2022) Applications of Big Data in transportation safety (2016-2019) Energy-efficient WSN Clustering (2015-2017) Emerging Space Network Architectures (2023) Her work addresses critical challenges in Network Management , IP-based Satellite Systems , and HAPS (High Altitude Platform Station) networks, with a strong emphasis on Standardization and Future Communications infrastructure.