Find Academic Supervisors

• Cybersecurity
• Internet of Things (IoT)
• Blockchain Technology
• +3 more

Yuan Liu is a faculty member affiliated with Guangzhou University's Cyberspace Institute of Advanced Technology. Their research focuses on cybersecurity, blockchain technology, federated learning, and IoT systems. They have held roles at multiple institutions, including Northeastern University (Software College) and Nanyang Technological University (PhD in Computer Engineering). Liu's work emphasizes secure communication, edge computing, and distributed systems, with contributions to protocols like blockchain-based redactable systems and quantum federated learning frameworks. They have collaborated extensively on projects addressing IoT security, smart healthcare, and privacy-preserving technologies. Key research trends include leveraging AI for enhanced security (e.g., watermarking frameworks, attack detection) and optimizing resource allocation in edge computing environments. Their publications span journals like IEEE Communications Surveys & Tutorials and conferences such as GLOBECOM.

Heiko BornholdtView profile

Researcher

• Distributed Communication Middleware
• Overlay Networking
• P2P Networks
• +3 more

Dr. Heiko Bornholdt is a Researcher in the Computer Networks group at the Department of Informatics, University of Hamburg. He is affiliated with the Faculty of Mathematics, Informatics, and Natural Sciences. His research focuses on distributed communication middleware, overlay networking, and edge computing. He holds a doctoral degree and has been with the university since 2016. Position: Research Associate / Postdoc Office: Room F-622, Vogt-Kölln-Str. 30 Contact: heiko.bornholdt@uni-hamburg.de | +49 40 42883-2332 Research Interests Dr. Bornholdt’s work emphasizes distributed systems , particularly in the context of smart cities and IoT. Key areas include: Overlay networks for edge computing Decentralized privacy-preserving systems Efficient data discovery in IoT Latency-aware network protocols Publications Overview His recent work spans 20+ publications, with significant contributions to: Edge computing middleware (e.g., software-defined overlay networks) Secure communication in untrusted environments Smart urban data spaces Awards & Grants No specific awards mentioned, but active in funded projects including the EU-GDPR compliant blockchain research. Labs & Teams Part of the Computer Networks Team led by Prof. Mathias Fischer, collaborating with researchers like Kevin Röbert and Jeetesh Gupta.

• Intelligent Transportation Systems
• Connected and Autonomous Vehicles
• Vehicular Networks
• +6 more

Daxin Tian is a prominent professor at Beihang University's School of Transportation Science and Engineering, specializing in intelligent transportation systems and vehicular networks. With over 170 publications spanning from 2006 to 2025, his research has significantly contributed to the advancement of connected and autonomous vehicle technologies. His work appears consistently in top-tier IEEE journals including IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles, and IEEE Internet of Things Journal, establishing him as a leading authority in the field. Professor Tian's research interests encompass several critical areas in modern transportation technology: Connected and Autonomous Vehicle Systems Vehicular Networking and Communication Protocols Vehicle Platooning and Cooperative Driving Algorithms Edge Computing Applications for Transportation Computer Vision for Autonomous Driving Perception Traffic Flow Optimization and Prediction Models Resource Allocation in Vehicular Networks His recent publications demonstrate an increasing sophistication in addressing complex multi-vehicle scenarios while maintaining practical considerations like communication reliability, energy efficiency, and safety constraints. The research trajectory shows a clear evolution from foundational networking and control problems toward more integrated AI-driven solutions that combine computer vision, natural language processing, and advanced control theory for next-generation transportation systems. Professor Tian maintains extensive international collaborations, particularly with researchers at Canadian institutions including Victor C. M. Leung's group, while leading a substantial research team at Beihang University. His work frequently bridges theoretical advances with practical transportation challenges, resulting in numerous high-impact publications that address real-world implementation barriers in intelligent transportation systems.

Phillip RaffeckView profile

Researcher

• Real-Time Systems
• Embedded Systems
• Energy-Aware Computing
• +7 more

Phillip Raffeck is a researcher at the Department of Computer Science (INF) within the Chair of Computer Science 4 (System Software) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His work focuses on real-time systems, embedded systems, and energy-aware computing, with a particular emphasis on worst-case execution time (WCET) and energy consumption (WCEC) analysis. He has contributed to projects like the Invasive Run-Time Support System (iRTSS) and tools for energy-neutral system design. Department: Department of Computer Science (INF) University: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Position: Researcher His research spans real-time systems , multi-core optimization , and power-aware computing . Key contributions include migrative synchronization protocols , energy-neutral operating systems , and code metrics for timing analysis . Recent work explores carbon-aware co-design and predictable migration in embedded environments. Publications highlight collaborations with teams across Germany and international symposia like RTSS, WCET, and EMSOFT. His article trends reflect advancements in intermittent execution models , transactional networking , and static analysis toolchains . He serves as a secondary reviewer for conferences including RTAS and ISORC. Phillip supervises graduate theses on topics such as DMA-based OS offloading , dynamic migration , and interrupt latency analysis . He contributes to teaching courses like Betriebsystemtechnik and Systemnahe Programmierung in C at FAU.

Michael CoblenzView profile

Assistant Professor

• Usability of programming languages
• User-centered programming language design
• Type systems for safety
• +6 more

Michael Coblenz is an Assistant Professor in the Computer Science Department at University of California, San Diego. His research focuses on integrating user-centered design into programming language development, creating safer languages for blockchain and scientific computing. He completed his Ph.D. at Carnegie Mellon University under Jonathan Aldrich and Brad A. Myers, followed by a Basili postdoctoral fellowship at University of Maryland. Specializes in usability of programming languages Created Obsidian language for safer smart contracts Developed PLIERS methodology for iterative language design Active in Rust usability and REST API quality research His work bridges programming languages and human-computer interaction, with recent projects including Kale (safer spreadsheets) and climate change-focused programming tools. He has served on program committees for SPLASH, VL/HCC, and HATRA conferences. 2025: Graduate Consortium Co-chair at VL/HCC 2024: HATRA Organizing Chair 2023: Doctoral Symposium Chair at SPLASH

• High-Performance Computing
• Parallel Programming
• Computer Architecture
• +3 more

Prof. Dr. rer. nat. Matthias S. Müller is a Universitätsprofessor and Director of the IT Center at RWTH Aachen University. His research focuses on High-Performance Computing (HPC), parallel programming models, correctness verification, energy-aware computing, and tools for distributed systems. He leads the High-Performance Computing group, contributing to advancements in HPC resource management, runtime systems, and sustainable computing practices. Key areas of expertise include MPI and OpenMP correctness checking, static and dynamic analysis techniques, performance optimization for heterogeneous architectures, and energy footprint modeling. Müller has extensively collaborated on projects like MUST (MPI correctness tool), OMPT tools, and frameworks for analyzing hybrid parallel applications. His work bridges theoretical computer science with practical implementation challenges in large-scale computing environments. Notable contributions include developing methods for data race detection in Remote Memory Access (RMA) programs, latency-aware power management models, and educational frameworks for HPC lab courses. His research often emphasizes tool development, runtime systems, and interdisciplinary applications of HPC across engineering domains. Müller's lab is part of RWTH Aachen's IT Center, which provides infrastructure and expertise for computational research. He actively publishes in top-tier conferences and journals, addressing challenges in parallel programming, energy efficiency, and distributed computing systems.

• High-performance computing
• Simulation software development
• Parallel algorithms
• +3 more

Michael Bader is a Professor in the Department of Computer Science at the Technical University of Munich (TUM), part of the TUM School of CIT. He leads the research group on hardware-aware algorithms and software for high-performance computing at the Leibniz Supercomputing Center. His work focuses on developing efficient algorithms and software for supercomputing platforms, particularly in geosciences and simulation of earthquakes and tsunamis. His research interests include high-performance computing, simulation software development (e.g., SeisSol and ExaHyPE), parallel numerical algorithms, adaptive mesh refinement, and large-scale geophysical simulations such as earthquake dynamics and tsunami modeling. He emphasizes optimizing algorithms for modern supercomputing architectures to handle complex computational challenges. Professor Bader has supervised numerous PhD students, including Lukas Krenz, Ravil Dorozhinskii, and Sebastian Wolf, among others. His research has been supported by grants from the EuroHPC JU, BMBF, DFG, and other institutions. Notable projects include ChEESE-2P for exascale computing in solid earth sciences and the targetDART project for adaptive task distribution on exascale systems. He is actively involved in teaching, offering courses such as Numerical Algorithms for High Performance Computing and Scientific Computing 1 . His group collaborates extensively with institutions like the Leibniz Supercomputing Center to advance computational methods for simulating natural disasters and geophysical phenomena.

• Resilience in 5G
• Distributed Artificial Intelligence (Edge AI)
• Information assessment
• +3 more

Tobias Meuser is a Researcher at the Multimedia Communications Lab of Technische Universität Darmstadt, leading the "Adaptive Communication Systems" group since 2020. He holds a PhD (2019) focused on vehicular network data management and has been a central figure in the third phase of the Collaborative Research Center (CRC) MAKI as a principal investigator in subproject B1. His work emphasizes resilient 5G networks, edge AI, and distributed systems. Education: B.Sc. Business Informatics (Fernuniversität Hagen) M.Sc. Informatics (TU Darmstadt) Research Interests: Resilience in 5G and beyond Edge AI and distributed machine learning Information assessment in vehicular networks Collaborative perception systems Hardware acceleration for network functions Key Projects: Principal Investigator in CRC MAKI's B1 (Monitoring and Analysis) Collaborations with Opel (cooperative maneuvering) and Deutsche Bahn (5G resilience) Labs/Teams: Head of Adaptive Communication Systems group at Multimedia Communications Lab Member of Distributed Sensing Systems group (2016–2020)

Stefan SmolnikView profile

Professor

• Knowledge Management
• Collaboration Systems
• Social Software
• +4 more

Stefan Smolnik is a University Professor at the FernUniversität in Hagen since 2013, affiliated with the Faculty of Business Administration and Economics . His academic career includes roles as Track Chair at the Hawaii International Conference on System Sciences (2025), Vice Rector for Research and Digitalization (2022-2025), and Head of the Institute of Research on Information Systems at EBS Business School (2012-2013). Education: Doctorate in Knowledge Management with Topic Maps (2005, University of Paderborn); Diploma in Computer Science (1998, University of Paderborn) Stefan's research focuses on Knowledge Management , Collaboration Systems , eLearning , and the Use and Success of Information Systems . His recent work explores digital health applications, smart city technologies, and AI-driven knowledge flows, with publications analyzing telemedicine adoption, gamified mobility systems, and neurophysiological impacts on digital work. Key trends in his 15 most recent publications (2023-2025) include: Integration of AI and NeuroIS in workplace design Smart city solutions for mobility , waste management , and sustainability Knowledge sharing in healthcare teleconsultations and professional services Maturity models for small/medium smart cities Emotional dimensions in knowledge management research Scientific recognition includes the Best Paper Award at the 4th Conference on Professional Knowledge Management (2007) and frequent participation in top-tier conferences like ICIS and HICSS . His work bridges information systems , organizational behavior , and urban digitalization .

• Dependability Engineering
• Performability Analysis
• Cloud Computing
• +7 more

Ermeson Carneiro de Andrade is a Professor at the Department of Systems and Computer Engineering within the Center of Informatics at the Federal University of Pernambuco (UFPE) in Brazil. His research focuses on dependability engineering, performability analysis, and fault tolerance in distributed and embedded systems. Over his career spanning more than 15 years, he has established himself as a prominent researcher in the field of system reliability through numerous publications in top-tier journals and conferences. Dr. Andrade's research interests primarily center on the analysis and modeling of system dependability, with particular expertise in UAV-based monitoring systems, cloud computing environments, and IoT architectures. His work bridges theoretical modeling with practical applications, particularly in environmental monitoring, disaster recovery solutions, and mission-critical systems. He has made significant contributions to understanding software aging phenomena in various computing environments and developing performability-aware solutions for real-time systems. The analysis of his recent publications reveals a strong focus on UAV systems for environmental monitoring, particularly deforestation detection, with increasing attention to weather impacts and vehicle density-aware traffic monitoring. His research demonstrates a consistent pattern of applying stochastic modeling techniques to solve practical problems in distributed systems, with recent work expanding into NoSQL database performance, satellite constellation dependability, and the performance-interpretability trade-offs in machine learning models. This evolution shows his ability to adapt to emerging technologies while maintaining core expertise in system reliability. Dr. Andrade has been actively involved in mentoring students and collaborating with researchers across Brazil and internationally. His work often involves interdisciplinary teams addressing complex system challenges. While specific awards aren't detailed in the available publication records, his consistent output in high-impact venues demonstrates recognition within the dependability engineering community. His laboratory work appears to focus on system modeling and analysis, with particular emphasis on experimental validation through simulation and real-world testing. Current projects suggest involvement in UAV-based monitoring systems for environmental applications, with strong connections to public sector institutions in Pernambuco state.

• Wireless Communications
• Information Theory
• UAV Networks
• +3 more

Dr. Yulin Hu serves as a Visiting Professor at RWTH Aachen University, holding the Chair of Information Theory and Data Analytics. His research program bridges theoretical foundations with practical implementations in next-generation wireless systems, with particular emphasis on UAV-aided networks and information-theoretic approaches to communication challenges. His core research interests span multiple interconnected domains: Wireless Communications (especially finite blocklength regimes) Information Theory applications in network design UAV trajectory optimization and network integration Wireless power transfer with nonlinear energy harvesting Edge computing and distributed learning systems Data analytics for network performance optimization Analysis of Dr. Hu's 2025 publication record reveals a concentrated research thrust on UAV trajectory design, where he develops joint optimization frameworks addressing energy efficiency, security, and reliability constraints. His work consistently integrates information-theoretic principles—particularly finite blocklength analysis—to solve practical challenges in ultra-reliable low-latency communications (URLLC) and wireless power transfer. A distinctive feature of his approach is the fusion of deep reinforcement learning with traditional optimization methods for dynamic network scenarios, including no-fly zone constraints and covert operations. While no specific scientific awards are documented in the available materials, his prolific output across top-tier venues demonstrates significant scholarly impact. Details regarding graduate student mentoring and research funding mechanisms remain unspecified in the current documentation. The Chair of Information Theory and Data Analytics, which Dr. Hu leads, functions as a specialized research unit focused on theoretical rigor and algorithmic innovation for wireless systems, though specific laboratory infrastructure or team composition details are not provided.

Setareh MaghsudiView profile

Professor

• Machine Learning
• Wireless Communications
• Edge Computing
• +7 more

Dr. Setareh Maghsudi is a Professor in the Learning Technical Systems group at the Faculty of Electrical Engineering and Information Technology at Ruhr-University Bochum. She joined Ruhr-University Bochum in August 2023 after serving as an Assistant Professor at the University of Tübingen (2020-2023) and at the Technical University of Berlin (2017-2020). Her academic journey began with an M.Sc. from Kiel University (2008-2010), followed by her Ph.D. and postdoctoral work at Technical University of Berlin (2011-2015), Yale University (2016-2017), University of Manitoba (2015-2016), and Kyushu University (2019). Dr. Maghsudi's research focuses on the application of machine learning to communication networks and distributed systems, with particular emphasis on bandit algorithms, federated learning, and resource allocation in dynamic environments. Her work bridges theoretical machine learning with practical networking challenges, developing algorithms that can adapt to non-stationary environments with partial information. She has made significant contributions to multi-armed bandit frameworks for wireless communications, edge computing, and network optimization. Her recent publications (2023-2025) demonstrate a strong trend toward addressing challenges in integrated sensing and communication (ISAC), federated learning for edge networks, and non-stationary decision-making problems. The publications show expertise spanning theoretical machine learning foundations, wireless communications engineering, and practical implementation for real-world networked systems. Her work increasingly incorporates causal reasoning and robustness considerations into learning frameworks for communication systems. Dr. Maghsudi leads the Learning Technical Systems research group at Ruhr-University Bochum, where she supervises PhD students and postdoctoral researchers working at the intersection of machine learning and communication systems. Her research is supported by various grants focusing on AI for future communication networks. Current projects include developing AI-driven solutions for next-generation communication systems with emphasis on robustness, efficiency, and adaptability in dynamic environments.

Kevin RöbertView profile

Researcher

• Privacy-Preserving Techniques
• Decentralized Systems
• P2P Networks
• +4 more

Kevin Röbert is a Researcher and PhD Student in the Computer Networks research group at the University of Hamburg's Department of Informatics, within the Faculty of Mathematics, Informatics and Natural Sciences. He holds a Master's degree (2022) and a Bachelor's degree (2019), both from the University of Hamburg. His research focuses on privacy-preserving techniques in decentralized systems, P2P networks, and edge computing. Notable contributions include work on untrustworthy environments, encrypted endpoint security, and latency-aware edge computing protocols. Education: Master's degree in Informatics, University of Hamburg, 2022 Bachelor's degree in Informatics, University of Hamburg, 2019 Research Interests: Kevin's work spans privacy-enhancing technologies, secure edge computing systems, and decentralized network architectures. He explores topics such as data anonymization in P2P networks, real-time scheduling for edge applications, and mitigating bot automation threats in online services. Publications: His recent work includes advancements in overlay networking middleware for edge applications, dynamic access control for data sharing, and latency-optimized TLS protocols. His 2024 paper on educational reform highlights innovative pedagogical approaches in computer science education. Lab/Team: Kevin is part of the Computer Networks group led by Prof. Mathias Fischer, collaborating with researchers like Dr. Heiko Bornholdt on projects involving network security and decentralized systems.

• Social Network Analysis
• Misinformation Detection
• Human Mobility Modeling
• +4 more

Jussara M. Almeida is an established computer science researcher specializing in social network analysis, misinformation detection, and human mobility modeling. Her extensive publication record (1996–2025) demonstrates active research in web science, political communication on messaging platforms (WhatsApp/Telegram), and cloud systems. She frequently collaborates with Brazilian institutions and international partners on large-scale data projects. Research Focus: Her core interests include: Modeling information diffusion in encrypted messaging apps (WhatsApp/Telegram) Predicting human mobility patterns and privacy implications Analyzing political discourse and election-related coordination online Developing computational methods for misinformation detection Optimizing cloud/edge computing performance Publication Trends: Recent work (2021-2025) shows intensified focus on: Telegram's role in political mobilization and information dissemination Advanced techniques for identifying fake news websites and image-based misinformation Privacy-preserving mobility analysis and edge computing Child safety in live-streaming platforms Collaborations & Impact: Key collaborators include Marcos André Gonçalves, Fabrício Benevenuto, and Marco Mellia. Her research provides critical insights into real-world problems like election integrity, platform governance, and user privacy.

Leonardo BanhView profile

Researcher

• Generative AI
• Artificial Intelligence
• Machine Learning
• +6 more

Leonardo Banh is a Researcher at the University of Duisburg-Essen within the Faculty of Computer Science and its Chair of Business Information Systems and Software Engineering . M.Sc. in Business Information Systems (University of Duisburg-Essen, 2022) B.Sc. in Business Information Systems (University of Duisburg-Essen, 2020) Semester abroad at Instituto Superior Técnico, Lisbon (2021) His research focuses on Generative AI and its socio-technical implications, particularly in Machine Learning and Deep Learning applications. He explores intersections with NeuroIS , Smart Tourism , and E-commerce Ecosystems , emphasizing sustainability and digital transformation. Recent publications analyze Generative AI in Software Engineering , AI in Music Sentiment Analysis , and AI-Based Sign Language Translation . His work often involves design science research and grounded theory frameworks. Best Paper in Track Award (ICIS 2024) Nominated for Best Paper Award (ICIS 2024) Outstanding Reviewer (ICIS 2024) As advisor, he supervises theses on topics including AI-Based Mental Health Chatbots , Generative AI in HR , and Smart Tourism Applications . He also contributes to the Institute of Computer Science and Information Systems and serves on appointment/habilitation committees.