Find Academic Supervisors

Stefan SchulteView profile

Professor

• Data Engineering
• Blockchain Technologies
• Internet of Things (IoT)
• +4 more

Prof. Dr.-Ing. Stefan Schulte is a Full Professor at Hamburg University of Technology, leading the Institute for Data Engineering and the Christian Doppler Laboratory Blockchain Technologies for the Internet of Things (CDL-BOT). He holds a diploma in Economics and a Bachelor's in Computer Science from the University of Oldenburg, followed by a Master's in Information Technology (with Merit) from the University of Newcastle. After completing his PhD at TU Darmstadt in 2010, he held roles as Postdoctoral Researcher at TU Wien, Assistant Professor (tenure-track), and eventually Associate Professor before joining TU Hamburg in 2021. His research focuses on data engineering, blockchain technologies applied to IoT, elastic computing, and quality-of-service (QoS) aspects in smart systems. Notable contributions include work on fog computing, federated learning, and cross-blockchain interoperability. He has published over 140 papers in top-tier venues like IEEE Transactions on Services Computing and ACM Computing Surveys. Key awards include Best Paper Awards at the IEEE International Conference on Blockchain (2020) and the European Conference on Service-Oriented and Cloud Computing (2023). Prof. Schulte chairs major conferences such as the IEEE International Conference on Fog and Edge Computing (ICFEC 2025) and serves on editorial boards for journals like IEEE Transactions on Services Computing. He leads CDL-BOT, a lab exploring blockchain applications in IoT and manufacturing. His industrial collaborations include projects like SIMPLI-CITY (smart mobility) and CREMA (cloud-based manufacturing). Current research emphasizes blockchain interoperability, federated learning frameworks, and edge-AI systems. He actively reviews proposals for the German Research Foundation, EU programs, and industry initiatives.

Jason JueView profile

Professor

• Optical Networking
• Network Survivability
• Elastic Network Slicing
• +3 more

Dr. Jason Jue is Professor of Computer Science at UT Dallas' Erik Jonsson School of Engineering and Computer Science. He chairs the IEEE Communications Society Technical Committee on Optical Networking and leads research in optical networking, 5G systems, and network virtualization. His substantial publication record (2022-2025) demonstrates evolving focus on network slicing for 5G, reinforcement learning applications in network management, and quantum-secured optical networks. Recent articles increasingly integrate machine learning with networking challenges, particularly for resource allocation and slice provisioning in multi-domain environments. Significant awards include: NSF CAREER Award (2002) Best Paper Award, Optical Network Design and Modeling (2010) Best Paper Award, IEEE Globecom (2005) Research funding highlights: NSF: $449,651 for resilient network slicing (2021) Multiple NSF grants exceeding $800,000 total Industry funding from Fujitsu

Kostas MagoutisView profile

Professor

• Distributed computer systems
• Scalable data stream processing
• Internet of Things
• +3 more

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete and collaborating researcher with FORTH-ICS. His research focuses on distributed systems, scalable data processing, IoT, and cloud computing, with projects including GreenInCities for urban regeneration and STREAMSTORE for stateful stream processing systems. Research interests include: Distributed computer systems architecture Elastic stream processing platforms Quantum-enhanced computing applications Multi-cloud application lifecycle management Recent publications demonstrate strong focus on federated data systems, quantum computing applications, and IoT-enhanced infrastructure, with consistent output in high-impact conferences and journals. Awards and distinctions: Multiple best paper awards from USENIX conferences Grand Challenge Audience Award at DEBS 2022 Marie Curie Fellowship and IBM Research awards Advises over 20 PhD and MSc students in distributed systems research. Leads multiple EU-funded projects and serves on program committees for top conferences including SOSP, EuroSys, and IEEE BigData. Directs research groups in distributed systems and cloud computing at FORTH-ICS.

Yvo G. DesmedtView profile

Professor

• Cryptography
• Quantum Computing
• Network Security
• +2 more

Yvo Desmedt is the Jonsson Distinguished Professor in Computer Science at the University of Texas at Dallas and Director of the Cyber Security Research and Education Institute. An IACR Fellow and member of the Belgium Academy of Science, he invented e-Passports and e-Visas in 1988. His research spans cryptography, quantum computing, network security, and critical infrastructure protection. Desmedt pioneered techniques in binary software hardening including control-flow integrity and object flow integrity protections. Recent innovations include crook-sourcing for intrusion detection improvement and confidential computing for deep learning inference. His work bridges theoretical cryptography with practical security applications, earning recognition including the NSF IUCRC Technology Breakthrough Award. With over 200 publications, he has chaired major conferences including Crypto and Public Key Cryptography. Current projects examine vulnerability detection using graph learning and renewable control-flow integrity mechanisms for software security.

• Cybersecurity
• Machine Learning Applications in Security
• Data Provenance Analysis
• +7 more

Kangkook Jee is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His work focuses on cybersecurity, machine learning applications in security, data provenance analysis, and graph neural networks. He has developed systems like ProvIoT for IoT security and UTrack for enterprise user tracking. His research addresses challenges in adversarial machine learning, malware detection, and robust graph classification under adversarial conditions. He also explores federated learning, confidential computing, and blockchain-based secure data sharing. Key contributions include techniques for detecting stealthy attacks in IoT, improving graph neural network robustness against adversarial node modifications, and enhancing intrusion detection through provenance-based analysis. His work bridges theoretical advancements in machine learning with practical enterprise security solutions. Notable systems include AIQL for efficient attack investigation and SEAL for storage-efficient causality analysis in enterprise logs. Research trends across his publications emphasize combining provenance tracking with modern ML techniques to address evolving cybersecurity threats. Work in 2024-2025 focuses on decompilation challenges, federated edge-cloud security, and graph abstraction methods for robust classification. No scientific awards are explicitly listed in the provided texts. His grants and advising activities are not detailed here, though his extensive publication record suggests active collaborative research. His lab works on tools like Nodoze for automated threat triage and APTrace for agile causality analysis in enterprise systems.

Nirwan AnsariView profile

Professor

• 6G Networks
• Wireless Charging
• Machine Learning
• +5 more

Nirwan Ansari is a Distinguished Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT). His research focuses on cutting-edge advancements in 6G networks , wireless charging , machine learning , and Internet of Things (IoT) . He has contributed extensively to AI-driven network optimization and sustainable energy solutions for next-generation communication systems. Ph.D., Electrical Engineering, Purdue University (1988) M.S., Electrical Engineering, University of Michigan-Ann Arbor (1983) B.S., Electrical Engineering, NJIT (1982) His recent work explores AI-native network slicing , UAV-assisted edge computing , and holographic communication . Publications highlight synergies between terrestrial and non-terrestrial networks , digital twin integration, and energy-efficient IoT systems . He serves as an Honorary Chair and contributes to major conferences like IEEE INFOCOM and IWCMC.

Wei ShiView profile

Professor

• Distributed computing
• Cloud computing
• Data center networks
• +6 more

Professor Wei Shi is a faculty member at the School of Computer Science, Carleton University. His research focuses on distributed computing, cloud networks, algorithm design for sensor/actuator systems, big data analytics, and data privacy. Notable projects include federated learning optimizations, blockchain-enabled edge intelligence for IoT/vehicle networks, and AI-driven cybersecurity solutions. His work addresses challenges in dynamic resource allocation, anomaly detection, and privacy-preserving techniques. Research Interests: Distributed Computing: Optimizing federated learning and client selection algorithms for wireless networks. Blockchain & Edge Intelligence: Developing decentralized systems for IoT and vehicular networks using AI large models. Security & Privacy: Innovating methods to detect AI-generated content, combat cyber-physical attacks, and protect user data privacy. Publications: Recent works emphasize federated learning applications, blockchain integration with edge computing, and cybersecurity for vehicular/IoT ecosystems. Key themes include energy-efficient algorithms, dynamic resource allocation, and intrusion detection in constrained environments. Email: wei.shi@carleton.ca

Xin FuView profile

Professor

• Computer Architecture
• Machine Learning
• Energy-Efficient Computing
• +2 more

Dr. Xin Fu is a Professor in Electrical and Computer Engineering at the University of Houston's Cullen College of Engineering, holding a PhD from the University of Florida. His research spans computer architecture, energy-efficient systems, machine learning acceleration, and hardware reliability, with applications in edge computing and quantum systems. Awarded the NSF CAREER Award and named Miller Scholar, he leads innovations in heterogeneous computing architectures. Research focuses on optimizing hardware-software co-design for AI workloads, with current projects in federated learning optimization, quantum computing reliability, and neural network acceleration. Recent publications demonstrate cross-cutting work in mobile AI deployment, adversarial defense mechanisms, and quantum error correction. Honors include: NSF Faculty Early CAREER Award (2014) Four-time recipient of competitive NSF research grants Miller Scholar recognition for teaching and research excellence

Miao PanView profile

Associate Professor

• Privacy Preserving Deep Learning
• Wireless Networking
• Internet of Things
• +3 more

Dr. Miao Pan is an Associate Professor in the Department of Electrical and Computer Engineering at the Cullen College of Engineering, University of Houston. He directs the PAN Lab (panlab.ece.uh.edu) focusing on wireless networking, security, and IoT applications. His educational background includes a B.S. in Electrical Engineering from Dalian University of Technology (2004), M.S. from Beijing University of Posts and Telecommunications (2007), and Ph.D. from the University of Florida (2012). Dr. Pan's research spans privacy-preserving deep learning, wireless networking, machine learning applications in communications, underwater systems, and cognitive radio networks. His interdisciplinary approach combines theoretical foundations with practical implementations in emerging technologies. Recent publications demonstrate strong focus on federated learning optimizations, wireless sensing innovations, and security mechanisms for next-generation systems. Key trends include energy-efficient mobile AI, robust authentication methods, and adaptive underwater networking solutions. Honors include: NSF CAREER Award (2014) 5 IEEE Best Paper Awards (2015-2019) University of Florida Graduate Fellowship (2007) He leads multiple federally funded projects and advises graduate researchers in wireless systems and security. The PAN Lab collaborates with industry partners to translate research into practical solutions for IoT and 5G/6G networks.

Francesc Moll EchetoView profile

Associate Professor

• Energy Harvesting
• Integrated Circuit Design
• Low Power Electronics
• +5 more

Francesc Moll Echeto is an Associate Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Departament d'Enginyeria Electrònica and the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona. He leads the HIPICS research group focused on high-performance integrated circuits and systems design. His expertise spans energy harvesting, low-power electronics, process variability management, and secure circuit design. He coordinates the Doctorat en Enginyeria Electrònica program and has coordinated EU-funded projects like the European Processor Initiative (EPI). Education: M.S. in Physics, Universitat de les Illes Balears, 1991 Ph.D. in Electronic Engineering, Universitat Politècnica de Catalunya, 1995 Research Focus: His work addresses energy-efficient computing, including: - Design of circuits tolerant to manufacturing variability - Energy harvesting from mechanical and RF sources - Secure hardware countermeasures against side-channel attacks - RISC-V architecture implementations in advanced technologies - Edge computing and autonomous sensor systems Grants & Collaborations: Coordinator of R&D projects like 'ARQUITECTURA DE COMPUTADORES DE ALTAS PRESTACIONES' (PID2023-146511NB-I00) Part of the Barcelona Zettascale Lab consortium Collaborations with Barcelona Supercomputing Center and industry partners Awards: HiPEAC Paper Award (2023, 2024) for innovations in vector processing and DNN acceleration Labs/Teams: Leads the HIPICS group and the EFRICS subgroup, collaborating on EU-funded initiatives like the European Processor Initiative. Active in open-source silicon projects (e.g., Sargantana RISC-V processor).

• Autonomous Driving
• Machine Learning
• Wireless Communication
• +11 more

Dr. Tao (Kevin) Huang is a researcher at James Cook University's College of Science and Engineering, with expertise spanning autonomous driving, wireless communication systems, and medical imaging applications. His work integrates machine learning, sensor fusion, and multimodal data analysis to address complex challenges in vehicular networks, environmental monitoring, and healthcare technology. Research Interests: Dr. Huang's research focuses on Autonomous driving perception systems IoT-enabled vehicular networks AI for medical diagnostics and environmental sensing Signal processing and privacy-preserving communication protocols Recent Publications: His 2025 work emphasizes advancements in V2X cooperative perception, radar-LiDAR-camera fusion, and diffusion models for medical imaging. Key trends include cross-modal robustness, real-time processing for autonomous systems, and AI applications in sustainability.

Maria K. MichaelView profile

Associate Professor

• Cyber-Physical Systems
• Embedded Systems Security
• AI/ML Edge Intelligence
• +5 more

Maria K. Michael is an Associate Professor at the Electrical and Computer Engineering Department (ECE), University of Cyprus, and a cofounding faculty member of the KIOS Center of Excellence. She leads the Center’s Education and Training activities and coordinates the MSc program in Intelligent Critical Infrastructure Systems, a collaboration between UCY, KIOS CoE, and Imperial College London. Her expertise spans dependability, security, and reliability in cyber-physical systems, embedded systems, and AI/ML optimization for edge intelligence. Education: BSc in Computer Science MSc in Computer Science Ph.D. in Engineering Sciences (Computer Engineering), Southern Illinois University, USA Research Focus: Hardware-enabled security, cyber-security in intelligent embedded systems, reliability of edge-based accelerators, and applications in smart grids, UAVs, robotics, and autonomous vehicles. Her work emphasizes safety-critical systems and resource-constrained environments. Grants & Team: Leads a 15-member research team (postdocs, PhD/MSc/BSc students). Funded by EU FP7, H2020, Horizon Europe, NSF, Intel Corp., and the Cyprus Research and Innovation Foundation. No scientific awards listed explicitly in provided text. Labs & Initiatives: Director of Education/Training at KIOS CoE; core contributor to the MSc program in Intelligent Critical Infrastructure Systems.

Muhammad Intizar AliView profile

Assistant Professor

• Data Analytics
• Internet of Things (IoT)
• Machine Learning & AI for IoT
• +8 more

Dr. Muhammad Intizar Ali is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU). He holds a PhD (with distinction) from Vienna University of Technology, Austria (2011) and has held roles including Adjunct Lecturer and Research Fellow at the Insight Centre for Data Analytics, NUI Galway. His primary research focuses on IoT, Data Analytics, Machine Learning, and Knowledge Graphs with applications in Smart Cities, Manufacturing, Farming, and Healthcare. Education: PhD in Computer Science, Vienna University of Technology (2007-2011) Research Interests: IoT and Edge Analytics Federated and Distributed Machine Learning Semantic Web and Knowledge Graphs Smart Manufacturing and Industry 4.0 Stream Processing and Real-Time Systems Recent Work Trends: His publications emphasize federated learning frameworks, IoT-enabled adaptive intelligence, and knowledge graph applications in industrial contexts. Recent projects include digital twin systems for predictive maintenance and ontology-driven manufacturing solutions. Grants & Projects: Lead Investigator in SFI-funded projects like MultiRoof (2025-2029) and Neuro-Symbolic AI for Building Management EU/Industry collaborations including Terrain-AI and Bentley-funded initiatives Labs & Teams: Active in DCU's Data Analysis and Machine Learning research groups, leading projects like Smart DCU Digital Twin for campus optimization.

• Wireless Communications
• 6G Networks
• Reconfigurable Intelligent Surfaces
• +2 more

Symeon Chatzinotas is a Full Professor at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), leading research in Signal Processing and Communications. His work advances 6G networks, quantum communications, reconfigurable intelligent surfaces, and integrated satellite-terrestrial systems. Recent publications explore quantum MIMO channels, digital twins for 6G ecosystems, and STAR-RIS-enhanced satellite networks. Research shows strong emphasis on next-generation wireless technologies and cross-domain network integration.

Xiao LiView profile

Assistant Professor

• Blockchain Technology
• Distributed Systems
• Machine Learning
• +7 more

Dr. Xiao Li is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University, School of Engineering. His research focuses on blockchain technology applications in distributed systems (Edge Computing, IoT, Federated Learning), machine learning, and privacy-preserving frameworks. He has a Ph.D. in Computer Science from The University of Texas at Dallas (2024) and was awarded the Jan P. Van der Ziel Engineering Fellowship there. Education: Ph.D. in Computer Science, The University of Texas at Dallas, 2024 Research interests include: Blockchain architecture optimization in resource-constrained environments Machine learning model development for cryptocurrency analysis Cybersecurity disclosure sentiment analysis using unsupervised techniques Low-resource data challenges in psychiatric clinics Professional activities: Program Committee Member, IEEE International Workshop on Blockchain and Smart Contracts (IEEE BSC 2024) Recruitment of PhD/Master’s students specializing in blockchain, distributed machine learning, and edge computing Office Location: Bergin 206 Office Hours: Mon/Wed 4pm-5pm (Winter 2025)