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Mohammed El-HajjarView profile

Professor

• Millimeter wave wireless communications
• Energy efficient transceiver design
• MIMO systems
• +7 more

Professor Mohammed El-Hajjar is a faculty member at the University of Southampton , affiliated with the Next Generation Wireless research group. His work spans advanced wireless technologies and their applications in emerging communication paradigms. Research areas include Millimeter wave (mMWave) wireless communications, Energy efficient transceiver design, MIMO systems, V2X networking, and Machine learning integration in wireless domains. Current projects involve PlaTform DrIving The UltImAte CoNnectivity (EPSRC-funded) and Affine Frequency Division Multiplexing for 6G (collaborative with Professor Hanzo). His publications focus on holographic MIMO, orbital angular momentum, and AI-driven wireless systems. He has supervised over 10 PhD students in electronic and electrical engineering. Scientific Awards Royal Academy of Engineering Industrial Fellowship Secured funding from UKRI and industry partners As a prolific researcher, he has contributed to over 100 peer-reviewed papers and holds multiple patents in wireless communication technologies.

Müjdat ÇetinView profile

Professor

• Signal Processing
• Image Processing
• Data Science
• +12 more

Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.

Marc AdratView profile

Professor

• Channel Coding
• Modulation Techniques
• Iterative Source-Channel Decoding
• +7 more

Marc Adrat is an Honorary Professor at RWTH Aachen University and Head of the Software Defined Radio research group at Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE). His dual role combines academic teaching with cutting-edge industrial research in communications engineering. Education: Diplom-Ingenieur in Electrical Engineering (1997), RWTH Aachen University Dr.-Ing. (PhD) in 2003 from Institute of Communication Systems and Data Processing (IND), RWTH Aachen Research Focus: Prof. Adrat specializes in channel coding , modulation techniques , and iterative decoding with particular emphasis on polar codes , BICM-ID systems , and EXIT chart analysis . His work bridges theoretical foundations with practical implementations in software-defined radio systems. His recent research directions include applying machine learning techniques (particularly genetic algorithms) to optimize communication systems, developing autoencoder-based signal enhancement methods, and advancing spectrum monitoring technologies for cognitive radio applications. Awards & Recognition: Best Paper Award at ICMCIS 2022 for work on spectrum monitoring techniques Appointed Honorary Professor by RWTH Aachen University in June 2024 Teaching & Supervision: Since 2009, he has taught courses on Modern Channel Coding for Wireless Communications and Advanced Coding and Modulation at RWTH Aachen. His teaching covers both theoretical foundations and practical implementations of modern communication systems. Laboratories & Teams: At Fraunhofer FKIE, he leads the Software Defined Radio research group, focusing on developing flexible, reconfigurable radio systems for military and civilian applications. The group works extensively on real-time implementations of advanced coding and modulation schemes.

Cédric HERZETView profile

Research Fellow

• Statistical Learning Theory
• Optimization
• Operational Research
• +3 more

Cédric HERZET is a Permanent Member of CREST at INRIA Rennes, focusing on statistical learning theory, optimization, and inverse problems. His work bridges theoretical analysis with practical algorithm development. Primary affiliation: INRIA Rennes (French National Institute for Research in Digital Science) Research Interests: Statistical Learning Theory, Optimization algorithms, Operational Research, Inverse Problems. His work particularly addresses sparse approximation, compressed sensing, and iterative thresholding methods. Key Contributions: Development of Bayesian pursuit algorithms, geometric analysis of subspace clustering with outliers, and analysis of state evolution in dense graph message passing. His theoretical work has direct applications in signal/image processing and machine learning. Software: Created MATLAB implementations of sparse approximation algorithms and Bernoulli-Gaussian Lab toolbox for Bayesian pursuit simulation.

John R. BarryView profile

Professor

• Communication Theory
• Wireless Communications
• Optical Communications
• +4 more

John R. Barry is a Professor at the School of Electrical and Computer Engineering, Georgia Institute of Technology. He holds a Ph.D. in Electrical Engineering from UC Berkeley (1992) and previously worked at Bell Communications Research, IBM, Hughes Aircraft, and General Dynamics. His research spans communication theory , wireless systems , optical communications , and magnetic recording , with a focus on modulation, coding, synchronization, and equalization . Education : B.S., State University of New York at Buffalo (1986) M.S., University of California at Berkeley (1987) Ph.D., University of California at Berkeley (1992) His recent journal and conference publications address multi-cell MIMO , polar codes for recording , two-dimensional magnetic recording , and joint radar-communication systems . Articles span IEEE Transactions on Magnetics , IEEE Transactions on Communications , and Globecom conferences, covering topics like timing recovery at low SNR , iterative equalization , and space-time coding . Scientific Awards : David J. Griep Memorial Prize (1992) Eliahu Jury Award (1993) NSF Research Initiation Award (1993) IBM Faculty Development Award (1993) Senior Member, IEEE He has advised 15 Ph.D. students and 2 M.S. students, including Hyuncheol Park , Chen-Chu Yeh , and Elnaz Sadeghian , all pursuing careers in academia and industry. His lab has received grants for iterative timing recovery , multi-input multi-output (MIMO) systems , and optical wireless communication . Professor Barry's research group has contributed to patents in timing recovery and MIMO detection, with applications in wireless and optical systems. He served as Guest Editor for the IEEE Journal on Selected Areas in Communications and Technical Program Chair for IEEE Globecom 2013 .

Kees GoossensView profile

Professor

• Embedded Systems
• Real-Time Systems
• Networks-on-Chip
• +3 more

Kees Goossens is Full Professor of Real-time Embedded Systems in the Electronic Systems group at Eindhoven University of Technology (TU/e). He leads the CompSOC Lab focused on predictable and composable embedded systems. His research spans composable virtualization, real-time systems, low-power design, and FPGA-based dynamic partial reconfiguration. Previously at NXP Semiconductors, he pioneered networks-on-chip research including the Aethereal NoC architecture. His research interests include: Composable and predictable embedded systems Real-time networks-on-chip (NoC) Memory management and controllers FPGA reconfiguration Hardware verification Low-power electronic design Publication analysis shows consistent focus on real-time systems, networks-on-chip, memory controllers, and embedded systems design, with recent expansion into machine learning applications for networking and error-correction coding. His work frequently addresses automotive and industrial control applications. Editorial contributions include: ACM TODAES editorial board (since 2009) Associate editor for Springer DAEM journal (since 2006) Guest editor for multiple NoC special issues He leads the CompSOC laboratory developing virtualized execution platforms for mixed-criticality systems. Current educational activities include courses on Systems-on-Chip, Embedded Systems, and Computer Architecture.