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Çağatay EdemenView profile

Assistant Professor

• Communication Theory
• Information Theory
• Visible Light Communication
• +5 more

Çağatay Edemen is an Assistant Professor in the Department of Electrical and Electronics Engineering at Özyeğin University's Faculty of Engineering since 2013. His research spans communication theory, optical wireless systems, and next-generation mobile networks with significant contributions to international standards. Education: PhD in Electrical Engineering, Işık University (2014) Master's in Electrical Engineering, Işık University (2006) Bachelor's in Electrical Engineering, Marmara University (2003) Dr. Edemen's research focuses on information-theoretic aspects of multi-user cooperative communication and visible light communication (VLC) applications . His work bridges theoretical analysis with practical implementations, particularly in optical wireless systems for underwater, terrestrial, and vehicular environments. Recent projects explore reconfigurable intelligent surfaces (RIS) for underwater acoustic communication and VLC for electric vehicle charging infrastructure. His publication portfolio shows strong emphasis on optical wireless communication (35%), multi-user cooperative systems (30%), and 5G/6G mobile technologies (25%), with growing interest in underwater and vehicular applications. The most cited works include his IEEE WCNC 2008 Best Paper Award-winning research on three-user cooperative channels. Awards: IEEE WCNC Best Paper Award (2008) IEEE SIU Communication Society Best Paper Award (2024) IEEE Senior Member (2022) MEVICO Silver Award (2013) Dr. Edemen has secured multiple TÜBİTAK and European research grants totaling over $2M, including projects on UAV-based optical wireless backhaul and underwater VLC networks. He currently supervises 7 graduate students across PhD and MS programs, with research focusing on RIS performance, optical channel modeling, and vehicular networking. As executive director of OKATEM (Centre of Excellence in Optical Wireless Communication Technologies), he leads an international research team developing next-generation communication systems for terrestrial, non-terrestrial, and underwater environments. His CT&T Research Group actively collaborates with NYU Abu Dhabi and industry partners on optical wireless communication standards development, with recent patents pending in electric vehicle communication systems and accident notification technologies.

• Signal Processing
• Machine Learning
• Statistical Signal Processing
• +11 more

Saikat Chatterjee is a Professor in the Department of Information Science and Engineering at the School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH). He is also a Fellow of Digital Futures and maintains visiting researcher positions at Karolinska Institute, Karolinska Hospital (specializing in 'AI for Health Care'), and Oslo University Hospital in Norway. His primary research interests span Signal Processing and Machine Learning, with specific focus on signal modeling (sparsity, compressive sensing, dynamical systems), statistical signal processing, statistical machine learning, deep learning, speech/audio/image processing, medical data analytics, life science data analysis, perception for autonomous systems, distributed machine learning, and explainable AI (XAI). He particularly emphasizes explainable machine learning, having a strong background in signal processing and statistical machine learning, with growing passion for medical data analysis due to its societal importance. SSF - Swedish Foundation for Strategic Research Region Stockholm European Union Digital Futures Vinnova WASP Companies: Ericsson, Scania, Saab Professor Chatterjee is actively involved in teaching, serving as examiner and course responsible for various degree projects and courses including Machine Learning and Data Science, Pattern Recognition and Machine Learning, and Speech and Audio Processing. His research group has produced significant work across multiple domains, with notable publications in Bioinformatics and smart city applications, demonstrating the breadth of his research impact from healthcare to urban systems.

Rodrigo C. de LamareView profile

Associate Professor

• Signal Processing
• Wireless Communications
• MIMO Systems
• +7 more

Rodrigo C. de Lamare is a Reader in the Department of Electronics at the University of York, United Kingdom. He holds a Diploma from UFRJ, an MSc and PhD from PUC-RIO, and is a Member of the Institution of Engineering and Technology (MIET) and Senior Member of IEEE (SMIEEE). His educational background includes a Postgraduate Certificate of Academic Practice from the University of York (2008), a PhD in Structures and algorithms for multiuser detection and interference suppression in DS-CDMA systems from PUC-RIO (2004), a Master's in Speech coding at rates below 2 kb/s from PUC-RIO (2001), and a Diploma in A CELP speech coder for Brazilian Portuguese from UFRJ (1998). Dr. de Lamare's research spans multiple areas of signal processing and communications, with a strong focus on adaptive algorithms for wireless systems. His work addresses fundamental challenges in multiuser detection, interference suppression, beamforming, and space-time processing. He has made significant contributions to reduced-rank adaptive filtering techniques that balance performance with computational complexity, which is crucial for practical implementation in modern communication systems. His publication record shows a consistent trajectory of high-impact research in IEEE journals and conferences, with recent work focusing on large-scale MIMO systems, cooperative communications, and advanced signal processing techniques for next-generation wireless networks. The research demonstrates increasing complexity in addressing interference management in dense wireless environments while maintaining computational feasibility. As an educator, Dr. de Lamare teaches courses in Error Control Coding, Detection and Estimation Theory, Digital Signal Processing, Information Theory, Engineering Mathematics, and Emerging Technologies, reflecting the breadth of his expertise. His academic supervision includes work with PhD students and visiting academics, though specific names are not provided in the available information. His professional activities encompass extensive conference participation and contributions to the academic community through technical program committees and editorial work.

• Network Security
• Decentralized Systems
• Privacy-Preserving Technologies
• +7 more

Prof. Björn Scheuermann is a Professor in the Department of Computer Science at Technische Universität Kaiserslautern, Germany. His research focuses on network security, decentralized systems, privacy-preserving technologies, and distributed algorithms. He has contributed to foundational work on anonymization protocols (e.g., Tor improvements), blockchain systems, and vehicular networks. His work often bridges theoretical computer science with practical implementations in domains like satellite communication and industrial IoT. Key areas of research include: Decentralized Systems: Analysis of IPFS, Bitcoin, and blockchain architectures. Anonymity Networks: Improving Tor's fairness and privacy guarantees. Network Security: Collaborative intrusion detection in SDN, firewall acceleration via FPGA. Wireless & Satellite: Optimizing data transmission for low-earth-orbit satellites and industrial sensors. Recent trends in his publications emphasize: Integration of machine learning for adaptive network security (e.g., COML-IDS framework). Dynamic routing protocols for fault tolerance (e.g., OD³R). Privacy in emerging technologies like mobile data donation apps and decentralized storage. His work has been published in top-tier venues like IEEE/ACM Transactions, IFIP Networking, and ACM SIGCOMM workshops. Collaborations span academia and industry, focusing on practical applications of secure and efficient network architectures.

Kathleen CullenView profile

Professor

• Neural Prosthetics
• Computational Neuroscience
• Multi-sensory Integration
• +3 more

Kathleen Cullen, PhD, is the Raj and Neera Singh Professor of Biomedical Engineering at Johns Hopkins University, with joint appointments in Neuroscience and Otolaryngology. She directs the Johns Hopkins Center for Hearing and Balance and founded the Systems Neuroscience and Neuroengineering Laboratory (SNNL). Her research focuses on understanding how the brain processes self-motion information through computational and neurophysiological methods, with translational applications in neural prosthetics and rehabilitation. Dr. Cullen received her bachelor’s degree in Biomedical Engineering and Neuroscience from Brown University, and her PhD in Neuroscience from the University of Chicago. Her postdoctoral training was at the Montreal Neurological Institute. She has held leadership roles including President of the Society for the Neural Control of Movement and membership on NASA’s Decadal Survey steering committee. She has authored over 250 lectures and serves on editorial boards of prestigious journals. Her lab’s three core research areas include: (1) fundamental neuroscience of self-motion prediction, (2) development of biomimetic neural prosthetics for vestibular dysfunction, and (3) advanced neurophysiological techniques to study vestibular pathways. Key achievements include demonstrating the cerebellum’s role in predictive motor control and advancing clinical applications of vestibular prosthetics. Dr. Cullen’s work bridges molecular neuroscience to clinical translation, with a focus on enhancing sensory-motor function through innovative technologies and rehabilitation strategies. Her contributions have been recognized through the William Dawson Chair in Systems Neuroscience and other accolades.

Mohammad BokaeiView profile

Research Fellow

• Wireless Communications
• Deep Learning
• Speech Processing
• +3 more

Mohammad Bokaei is a PhD Fellow at the Department of Electronic Systems within The Technical Faculty of IT and Design at Aalborg University, Denmark. His research integrates deep learning with wireless communication systems, focusing on speech transmission under channel constraints. His core research interests include: Wireless Communications: Channel modeling and adaptive transmission techniques Deep Learning: Neural network applications for communication systems Speech Processing: Real-time transmission and enhancement algorithms Signal Processing: Theoretical frameworks for low-latency systems Joint Source-Channel Coding: End-to-end optimization approaches Matrix Completion: Low-rank recovery for harmonic signal analysis Analysis of his 2024 publications reveals concentrated innovation in deep learning-driven wireless speech systems. Key trends include channel-configurable architectures, Gaussian channel optimization, and latency-constrained joint transmission-enhancement frameworks. His work bridges theoretical signal processing with practical wireless applications, particularly for assistive communication devices. No scientific awards were documented in the source material. Details regarding student supervision or research grants were not provided in the available information. No specific laboratory affiliations or research teams were referenced in the scraped content.

Krishna NarayananView profile

Professor

• Wireless Communications
• Large Language Models
• Information Theory
• +3 more

Dr. Krishna Narayanan is Professor and Edgar and Yolanda Sanchez Endowed Chair in Electrical & Computer Engineering at Texas A&M University. He directs research in AI-native wireless communications, large language models for communication systems, and massive multiple access networks. His research integrates information theory with deep learning to develop novel communication paradigms. Current work focuses on Transformers for wireless estimation, LLM-based text compression, and efficient coding for massive IoT connectivity. Narayanan bridges theoretical foundations with practical implementations across wireless, storage, and computing domains. Publications demonstrate consistent innovation in coding theory applications, from compressed sensing for massive access to analog joint source-channel coding. Recent work explores LLM-powered text compression and Transformers for optimal wireless estimation, establishing new intersections between deep learning and communication theory. Honors include IEEE Communications/Information Theory Societies Best Paper Award (2022) and university-level teaching awards. Narayanan serves as Associate Director for Educational Initiatives at Texas A&M Institute for Data Science, developing curriculum integrating communication theory with AI methodologies.

Han WuView profile

Researcher

• Information Theory
• Communication Systems
• Data Compression
• +3 more

Han Wu is a postdoctoral researcher at the Information and Communication Theory Lab, Eindhoven University of Technology, Netherlands. He holds an MSc in Electrical Engineering from Chalmers University of Technology, Sweden (2020), and completed his PhD candidacy in Electrical Engineering at Eindhoven University from 2021 to 2025. Master of Science (MSc) in Electrical Engineering, Chalmers University of Technology (2020) PhD Candidate in Electrical Engineering, Eindhoven University of Technology (2021–2025) Han’s research focuses on information theory and its applications, particularly in communication systems and data compression. His work explores advanced topics such as error exponents, logarithmic loss in source coding, and joint communication and sensing. He investigates theoretical challenges in discrete memoryless channels, distortion measures, side information, and decoding error analysis. Recent trends in his publications include theoretical frameworks for information bottleneck channels, optimal rate design in noisy environments, and asymptotic analysis of logarithmic loss in variable-length coding. His research emphasizes the integration of signal processing and information theory to address challenges in communication protocols and structural components. Han is a project member of IT-JCAS (2024–2028), focusing on information-theoretic foundations of joint communication and sensing. Collaborators include Joudeh, Lampel, and Yılmaz. His work has been cited 16 times in Scopus and received 200+ downloads from institutional repositories. He is affiliated with the Information and Communication Theory Lab at Eindhoven University, contributing to cutting-edge research in communication systems and theoretical signal processing.

Sergio Antonio Cruces ÁlvarezView profile

Professor

• Signal Processing
• Communications
• Brain-Computer Interfaces (BCI)
• +3 more

Sergio Antonio Cruces Álvarez is a Professor of Signal Processing and Communications at the University of Seville, Spain, affiliated with the Signal Theory and Communications Department and leading the TIC-246 Research Group (Machine Learning Technologies and Digital Information Processing). His work bridges statistical signal modeling, data-driven learning, and applications in brain-machine interfaces and nonlinear behavioral modeling .

• Signal Processing
• Communication Systems
• Machine Learning algorithms and systems
• +7 more

Andrew C. Singer is a Professor at the University of Illinois at Urbana-Champaign , with joint appointments in the College of Engineering (Electrical and Computer Engineering, Industrial and Enterprise Systems Engineering) and the College of Business (Business Administration). Since 1998, he has held roles including the Fox Family Endowed Professorship , Associate Dean for Innovation and Entrepreneurship (2018–present), and Director of the Technology Entrepreneur Center (2005–2017). He co-founded Intersymbol Communications, Inc. (2000, acquired by Finisar) and OceanComm (2015, underwater video communication). Education: Ph.D. in Electrical Engineering and Computer Science, MIT (1996). Research Interests span statistical signal processing , acoustic communication systems , machine learning , and low-power VLSI design , with applications in underwater acoustics, through-tissue communication, and augmented listening. His work integrates theoretical signal processing with practical hardware implementations. Recent Publications focus on acoustic communication under nonlinear conditions , low-complexity ADC design , and biomedical applications of signal processing . Articles from 2023–2020 highlight innovations in underwater navigation, face mask acoustics, and subsurface data transmission. Scientific Awards include: Hughes Aircraft Masters Fellowship Harold L. Hazen Memorial Award (1991) NSF CAREER Award (2000) Xerox Outstanding Faculty Award (2001) IEEE Fellow IEEE Signal Processing Society Distinguished Lecturer (2014) Best Paper Awards (2006, 2008) Leadership roles include Associate Editor for IEEE Transactions on Signal Processing and service on the MIT Educational Council . He directs the Coordinated Science Lab and advises on expert witness cases in audio and communication industries.

Werner HenkelView profile

Professor

• Physical-layer security
• Source and channel coding
• Multi-carrier modulation
• +7 more

Prof. Werner Henkel is a Professor of Electrical Engineering at Jacobs University Bremen. He holds a Dr.-Ing. (Ph.D.) from Technische Universität Darmstadt (1989), with research focused on coding theory, communications, and signal processing. His roles include chairing the Electrical and Computer Engineering (ECE) program and serving as Dean for Engineering and Mathematical Sciences (2012–2014). Affiliations: Jacobs University Bremen: School of Engineering and Science Constructor University Bremen (prior affiliation) Education: Ph.D. (Dr.-Ing.) in Electrical Engineering, TU Darmstadt (1989) Diploma in Electrical Engineering, TU Darmstadt (1984) Research Interests: Prof. Henkel’s work spans physical-layer security , LDPC codes , neural networks , impulse noise treatment , and DNA analysis . His recent focus includes secure key generation in FDD systems, joint source-channel coding, and applications in IoT and power-line communications. He has contributed to DSL standardization and industrial projects in satellite communication and cybersecurity. Grants & Projects: Research on impulse noise mitigation in wireless/wireline channels Collaborations with industry on satellite communication and PLC systems Labs & Teams: Active in multidisciplinary teams at Jacobs University, focusing on communications, coding, and bioinformatics.

• Wireless Communication
• Software-Defined Radio
• 5G
• +9 more

Xianjun Jiao is a Researcher at the Department of Information Technology within the Faculty of Engineering and Architecture at Ghent University . His work focuses on wireless communication systems, particularly in the domains of Software-Defined Radio (SDR) , Wi-Fi , 5G , Radio Virtualization , and Network Densification . As a PhD supervisor, he has guided doctoral researchers including Felipe Augusto Pereira de Figueiredo Thijs Havinga Muhammad Aslam Baiheng Chen Robbe Gaeremynck on topics such as Next-Generation Wi-Fi Chip Design , Cell-Free Massive MIMO , and Hardware-Efficient Wireless Systems . His recent publications (2025) explore advanced Wi-Fi architectures, including OFDMA-based interference mitigation , joint transmission systems , and machine learning for handover optimization . Earlier works (2020-2024) address hardware efficiency in transceivers , time-sensitive networking , and NOMA schemes for concurrent transmission .

• Federated Learning
• Distributed Machine Learning
• Wireless Networks
• +2 more

Seyedmohammad Azimi Abarghouei is a Senior Researcher at the Division of Network and Systems Engineering, KTH Royal Institute of Technology, collaborating with Prof. Carlo Fischione. Previously, he served as a Postdoctoral Fellow in the same division from 2022 to 2024 under Prof. Viktoria Fodor. He completed his Ph.D. at Sharif University of Technology in Iran in November 2020, where his research on wireless networks earned the national Best PhD Thesis Award in electrical engineering and information technology. His educational background includes: Ph.D. in Electrical Engineering and Information Technology, Sharif University of Technology, Iran (2020) Dr. Azimi Abarghouei's research centers on distributed machine learning systems, particularly federated learning architectures for wireless networks. His work develops hierarchical aggregation methods, over-the-air computation techniques, and lattice coding approaches to address communication efficiency and data heterogeneity. He also contributes to molecular communication and stochastic geometry for nanonetwork modeling, bridging theoretical foundations with practical wireless applications. Analysis of his recent publications reveals a dominant focus on scalable federated learning systems, with significant innovation in hierarchical structures, quantization techniques, and interference-aware aggregation. His work consistently targets real-world challenges in edge intelligence, including bandwidth constraints, non-IID data distributions, and massive device coordination in IoT environments. His scientific recognition includes: Best PhD Thesis Award in Electrical Engineering and Information Technology (National Level, Iran) Jubilee Appropriation Scholarship from Knut and Alice Wallenberg Foundation (2023) Exemplary Reviewer of IEEE Wireless Communications Letters (2022, 2023, 2025) Exemplary Reviewer of IEEE Communications Letters (2023) Dr. Azimi Abarghouei has secured competitive research funding including the Knut and Alice Wallenberg Foundation's Jubilee Appropriation Scholarship. His active peer review contributions and conference tutorial leadership (e.g., IEEE ICMLCN 2025) demonstrate significant community engagement. While specific grant details beyond the scholarship aren't fully documented, his publication record indicates sustained support for federated learning research through institutional and foundation channels. He operates within Prof. Carlo Fischione's research group at KTH's Division of Network and Systems Engineering, focusing on networked machine learning systems. His collaborative work spans international conferences and journals, with recent emphasis on federated learning standardization through tutorials and preprints that shape emerging research directions in distributed AI.

Ramesh PyndiahView profile

Professor

• Digital Communications
• Channel Coding
• Turbo Codes
• +7 more

Ramesh Pyndiah is a Professor at IMT Atlantique's Microwave Optics (OPT) Department, a leading institution in digital communications research. With over 30 journal papers and 100 conference publications, his work focuses on advanced channel coding techniques, particularly block turbo codes, Reed-Solomon codes, and joint source-channel coding. Research Interests : Digital communications, turbo coding, joint source-channel coding, OFDM, wireless sensor networks, optical wireless systems. Leadership : Director of Research & Innovation at IMT Atlantique, Deputy Director of COMIN Labs (2014-2017), Chair of IEEE ComSoc France Chapter (2000-2014). His research trends show a strong emphasis on error correction for high-efficiency wireless and optical systems, with recent work on LDPC codes , network coding , and low-complexity decoding . He has supervised 17 PhD students in areas like video coding, UWB systems, and DNA-inspired coding. Awards : 2001 SEE Blondel Medal, IEEE Senior Membership (2001), leadership roles in ICC, Globecom, and Turbo Code symposia. Patents : 17 innovations in high-frequency mixers, phase shifters, and error correction coding.

Katerina PandremmenouView profile

Researcher

• EEG Processing
• Video Quality Assessment
• Resource Allocation
• +5 more

Katerina Pandremmenou is an academic affiliated with the Department of Computer Science and Engineering at the University of Ioannina, Greece. Her research focuses on signal processing, communications, and EEG/video quality assessment. She holds a PhD from the University of Ioannina (2015), an MSc (2011) and BSc (2008) from the same university and the University of Crete, respectively. Her research interests include EEG signal quality for wearable devices, video transmission optimization under noisy conditions, and resource allocation in sensor networks. Recent work explores neural oscillations in relaxation states and perceptual video quality metrics. Earlier contributions address cross-layer design, game-theoretic resource management, and reinforcement learning frameworks for visual sensor networks. Publications span IEEE journals/conferences and SPIE proceedings, emphasizing multimedia quality metrics, network optimization, and signal analysis. No scientific awards are explicitly listed, though her work has been published in reputable venues. She advises no students in listed records, and no grants or lab affiliations are detailed.