Find Academic Supervisors

• Distributed Signal Processing
• Wireless Communications
• Machine Learning
• +3 more

Dr. Erma Perenda serves as Professor and Chair of Distributed Signal Processing at RWTH Aachen University, Germany, leading research within the Department of Distributed Signal Processing. Her contact details include email perenda@dsp.rwth-aachen.de and phone +49 241 80-27879, with office location at Kopernikusstraße 16, 52074 Aachen in the ICT Cubes facility. Her research spans: Distributed Signal Processing Wireless Communications Machine Learning (Deep Reinforcement Learning, Federated Learning) Modulation Classification AI-driven Network Optimization She focuses on solving real-world challenges in wireless systems including hardware impairments, channel variations, and energy efficiency through advanced AI techniques. Analysis of her 2018-2024 publications reveals consistent innovation in applying multi-agent deep reinforcement learning to wireless power allocation, developing robust modulation classification methods resilient to channel impairments, and implementing federated learning for industrial edge computing. Her work bridges theoretical machine learning with practical wireless communication constraints. Scientific Awards: No awards documented in available sources Advising and Grants: No student advisees or grant information provided Labs and Teams: Leads Distributed Signal Processing research group at RWTH Aachen University Based in ICT Cubes building focusing on wireless AI systems

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

• 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.

• Computer Networks
• Telecommunications
• Cloud Computing
• +7 more

Raouf Boutaba is a Professor at the University of Waterloo, Canada, with a focus on networking and telecommunications research. His work spans computer networks, cloud computing, network security, and software-defined networking (SDN). He has contributed to advancements in 5G/6G networks, network slicing, energy-efficient architectures, and machine learning applications in networking. His research addresses challenges in network monitoring, resilient communication systems, and optimizing network performance through reinforcement learning and intelligent algorithms. He collaborates extensively with researchers globally, publishing in top-tier journals and conferences such as IEEE Transactions on Networking and NOMS. Boutaba’s work often bridges theoretical contributions with practical implementations, emphasizing real-world applicability in telecommunications and distributed systems. His recent focus includes open RAN architectures, edge computing, and securing encrypted traffic in evolving network environments.

• Edge Computing
• 5G/6G Networks
• Multi-access Edge Computing (MEC)
• +3 more

Estefanía Coronado is a researcher at the i2CAT Foundation in Spain, specializing in edge computing, 5G/6G networks, and AI-driven networking. Her work focuses on optimizing resource allocation, energy efficiency, and multi-agent collaboration in edge environments. She has published extensively in top-tier journals and conferences, including IEEE Communications Magazine and NOMS. Key contributions include frameworks like MEO (MEC Orchestrator) and MintEDGE (energy-aware edge simulator). Collaborations with institutions like Universitat Politècnica de Catalunya and industry partners highlight her impact in both academia and industry. Her research addresses challenges in industrial IoT, autonomous systems, and network automation.

• Integrated Sensing and Communication (ISAC)
• Vehicular Networks
• Cognitive Radio Networks
• +5 more

Wei Liang is a researcher affiliated with Northwestern Polytechnical University , Lancaster University , and University of Southampton . His work spans wireless communication systems, with a focus on integrated sensing and communication (ISAC) , NOMA (Non-Orthogonal Multiple Access) , and UAV-assisted mobile networks . Research Interests : Designing intelligent resource allocation algorithms, secure transmission mechanisms, and cooperative communication frameworks for next-generation networks. Collaborative Focus : Frequent collaborations with Zhu Han , Soon Xin Ng , and Ang Gao on 5G/6G-enabled systems. Recent publications (2024-2025) emphasize predictive beamforming in ISAC systems, multi-UAV optimization , and deep reinforcement learning for spectrum sharing. Trends include OTFS modulation for Doppler mitigation, heterogeneous multi-agent learning , and optimal transport theory for UAV networks.