جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Remote Sensing
• Signal Processing
• +۳ مورد دیگر

Jianbo Liu is a prolific researcher with a focus on interdisciplinary fields spanning machine learning, remote sensing, signal processing, and computer vision. His work emphasizes developing advanced algorithms for applications in environmental monitoring, telecommunications, and data-driven decision-making. Liu collaborates frequently with institutions and researchers in China and internationally, contributing to journals like IEEE Transactions on Wireless Communications , Remote Sensing , and Pattern Recognition . Research Interests: Machine learning frameworks for computer vision tasks, remote sensing data analysis, signal processing in communication systems, and optimization of industrial processes. Key Collaborators: Fu Chen, Jimmy S. J. Ren, Ningyuan Cao, Hongsheng Li, and Boyang Cheng. Publications: Over 138 papers across venues such as CVPR, ICCV, and IEEE journals, demonstrating expertise in neural networks, sensor fusion, and spatiotemporal data analysis. His recent work highlights contributions to physics-informed machine learning, gesture recognition via hierarchical attention networks, and secure communication protocols for satellite systems. Liu’s research bridges theoretical advancements with practical applications in environmental science, healthcare, and smart infrastructure.

Mehmet Canمشاهده پروفایل

پژوهشگر

• Non-Orthogonal Multiple Access (NOMA)
• Rate-Splitting Multiple Access (RSMA)
• Satellite Communication Networks
• +۷ مورد دیگر

Mehmet Can is a Researcher at Istanbul Technical University's Department of Electronics and Communication Engineering. His research spans telecommunications and geotechnical engineering, focusing on wireless communication systems, Non-Orthogonal and Rate-Splitting Multiple Access (NOMA/RSMA), satellite networks, and soil mechanics. Current affiliations: Istanbul Technical University Research areas: Telecommunications, Geotechnical Engineering, Machine Learning His work in telecommunications emphasizes RSMA-based uplink/downlink systems, antenna design, and satellite network optimization. In geotechnical engineering, he investigates constitutive modeling of unsaturated soils, cyclic soil response, and machine learning applications for foundation capacity prediction. Articles highlight collaborations with institutions in Turkey and Finland. Scientific awards: None explicitly mentioned.

Xinning Weiمشاهده پروفایل

پژوهشگر

• MIMO Systems
• OFDM Mobile Radio Communication
• Coordinated Multipoint (CoMP) Transmission/Reception
• +۳ مورد دیگر

Dr.-Ing. Xinning Wei is affiliated with the Institute of Communications Engineering at the University of Rostock, part of the Faculty of Computer Science and Electrical Engineering. Their research focuses on advanced mobile radio communication systems, including MIMO, OFDM, CoMP transmission/reception, and decentralized signal processing for interference cancellation. They have contributed to joint detection techniques in cellular networks and mobile radio localization methods. Key technical activities include peer review for IEEE Transactions on Communications, IEEE ICC, ISWCS, and VTC conferences. Supervised student projects include work on channel selection, SINR analysis in distributed antenna systems, and decentralized interference cancellation techniques. Publications (2007-2011) emphasize cooperative communication systems, interference reduction strategies, and 3D positioning in non-line-of-sight environments. Their doctoral dissertation (2011) explored cooperative communications under partial channel state information constraints. Operates within the Radio Communication research group and maintains an office at Albert-Einstein-Str. 26, Rostock. Collaborates with the INT Secretariat for administrative coordination.

Amor Nafkhaمشاهده پروفایل

پژوهشگر

• Physical layer security
• Hardware Security
• Reconfigurable Computing
• +۴ مورد دیگر

Amor Nafkha is a Researcher at the Rennes Institute of Electronics and Telecommunications (IETR), specializing in Physical layer security, Hardware Security, and Reconfigurable Computing. His work focuses on advancing Cognitive Radio, Software Defined Radio (SDR), and 5G/6G technologies. He has contributed to pioneering research in spectrum sensing, hardware vulnerability analysis, and reconfigurable systems. His research interests include developing robust security mechanisms against side-channel attacks, optimizing SDR architectures for energy efficiency, and applying machine learning techniques to device activity detection in massive MTC networks. Key projects include the ANR-POSEIDON initiative and the WONG5 document on 5G integration for machine-type communication. Notable contributions include multi-screaming-channel attacks leveraging frequency diversity and hybrid spectrum sensing frameworks validated on USRP platforms. His work bridges theoretical signal processing with practical hardware implementations, emphasizing real-world deployment challenges.

• Wireless Communications
• Cryptography
• Network Security
• +۳ مورد دیگر

Dr. Hasan Ahmed serves as a Senior Lecturer and Director of Partnership Development within the School of Computing and Communications at Lancaster University. Based at InfoLab21, he maintains dual affiliations with the Communication Systems research group and the Vice-Chancellor's Office, reflecting his academic and strategic partnership roles. His research bridges theoretical and applied domains across communications engineering and computer science. His primary research areas include: Wireless Communications (VANET connectivity, MIMO-UWB channel modeling, TETRA systems) Cryptography (rank code cryptosystems, key pre-distribution for WSN) Network Security (electricity theft detection, intrusion detection systems) Machine Learning (CNN applications for security analytics) Image Processing (face recognition, segmentation algorithms) Smart Grids (energy efficiency, infrastructure security) Publications from 2010-2023 reveal an evolution from foundational work in communication theory toward AI-driven security solutions. Early contributions (2010-2012) focus on ray tracing for UWB channel modeling and VANET traffic flow, while post-2017 research increasingly applies neural networks to face recognition and smart grid security. The 2021-2023 papers demonstrate high-impact integration of deep learning for electricity theft and intrusion detection in critical infrastructure. Dr. Ahmed supervises PhD student Naif Alshammari and leads partnership development initiatives. His work with the Communication Systems group at InfoLab21 emphasizes collaborative projects in communication technologies and ICT security, though specific grant details remain unreported in the source materials. His administrative role as Director of Partnership Development indicates strategic leadership in institutional collaborations beyond core academic duties. He is embedded within Lancaster University's InfoLab21 ecosystem, contributing to the Communication Systems research group's focus on next-generation networks, IoT security, and wireless infrastructure. This environment supports his interdisciplinary work spanning electrical engineering and computer science frameworks.

• Wireless Digital Communications
• Indoor Localization
• Error Control Coding
• +۱۲ مورد دیگر

Dr. Ian D. Marsland is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University, Faculty of Engineering and Design. He holds a Ph.D. in Electrical Engineering from the University of British Columbia and has been a faculty member since 1999. His research is centered on wireless digital communications, with a focus on error control coding, noncoherent detection, iterative decoding, and indoor localization. B.Sc.Eng. (Honours) in Mathematics and Engineering, Queen's University, 1987 M.Sc. in Electrical Engineering, University of British Columbia, 1994 Ph.D. in Electrical Engineering, University of British Columbia, 1999 His research interests span wireless digital communications , including indoor localization using wireless networks , error control coding (LDPC, turbo, polar codes) , noncoherent receiver design , and applications of iterative decoding . His work bridges theoretical communication systems with practical implementation in modern wireless networks. The analysis of his recent publications (2020–2024) reveals a strong trend toward advanced modulation and coding schemes such as SCMA , faster-than-Nyquist signaling , and polar codes , often integrated with machine learning (e.g., neural network-aided detection) and high-resolution localization techniques. His work frequently appears in high-impact IEEE journals like IEEE Transactions on Communications and top conferences including ICC and GlobeCom . Dr. Marsland has not been explicitly mentioned as receiving scientific awards in the provided texts. He actively advises graduate students and has collaborated extensively with researchers including H. Yanikomeroglu , R.H. Gohary , and T. Shehata . His research has been supported through academic grants and industrial partnerships, though specific grant details are not listed. He supervises work in areas such as MIMO systems, SCMA, polar coding, and indoor positioning. Dr. Marsland leads a research group focused on wireless communications and signal processing, with active projects in next-generation multiple access, low-latency detection, and robust communication in fading and interference-prone environments. His lab contributes to both theoretical advancements and practical implementations in modern wireless systems.

Cynthia Rushمشاهده پروفایل

دانشیار

• Mathematical Statistics
• High-Dimensional Inference
• Approximate Message Passing
• +۷ مورد دیگر

Cynthia Rush is an Associate Professor in the Department of Statistics at Columbia University, where she conducts research at the intersection of mathematical statistics, information theory, and high-dimensional data analysis. She is affiliated with the Graduate School of Arts and Sciences and is based in the School of Social Work building in New York City. Ph.D. in Statistics, Yale University, 2016 B.S. in Mathematics, University of North Carolina at Chapel Hill Her research focuses on the development and analysis of algorithms for high-dimensional inference, particularly using Approximate Message Passing (AMP), with applications in sparse regression, signal processing, and machine learning. She integrates tools from information theory, statistical physics, and applied probability to understand fundamental limits and performance of modern statistical methods. Her recent publications span topics such as SLOPE regularization, community detection, power posteriors, and massive multiple access in communications. These works appear in top journals including IEEE Transactions on Information Theory, Annals of Statistics, and NeurIPS. Her research shows a consistent trend toward rigorous theoretical analysis of high-dimensional statistical algorithms and their practical implications. Notable scientific recognition includes being a finalist for the JKW Student Paper Award at IEEE ISIT in 2016. She has delivered numerous invited talks at leading institutions and conferences worldwide, reflecting her active role in the theoretical data science community. JKW Student Paper Award Finalist, IEEE ISIT 2016 She advises and collaborates with researchers in statistics, machine learning, and information theory, though formal doctoral advisees are not listed. Her work is supported by theoretical grants and collaborations across engineering and mathematical sciences. She is involved in the broader academic community through seminars, workshops, and program committees, contributing to the advancement of rigorous statistical learning. Cynthia Rush leads a research group focused on high-dimensional inference and algorithmic analysis, often collaborating with researchers at institutions like Yale, Princeton, and international centers in Europe and Asia. Her lab emphasizes theoretical foundations with practical relevance in data science and communications.

• Coding Theory
• Distributed Storage
• Caching Systems
• +۴ مورد دیگر

Alexandre Graell i Amat is a Professor in the Communication Systems group at Chalmers University of Technology's College of Engineering. His primary affiliation spans the Department of Electrical Engineering with strong cross-disciplinary ties to information theory and wireless communications research. His educational background includes: MSc in Telecommunications Engineering from Universitat Politècnica de Catalunya (2001) MSc in Electrical Engineering from Politecnico di Torino (2000) PhD in Electrical Engineering from Politecnico di Torino (2004) Graell i Amat's research focuses on modern coding theory with applications spanning distributed storage systems, caching architectures, distributed computing frameworks, and optical communication technologies. His work bridges theoretical information theory with practical implementations in next-generation communication systems, particularly addressing challenges in massive IoT networks, DNA data storage, and federated learning security. Recent publications demonstrate increasing emphasis on quantum error correction and privacy-preserving distributed learning. His publication trends reveal consistent contributions across three core domains: (1) novel LDPC code constructions for emerging storage paradigms, (2) privacy/security mechanisms in federated learning systems, and (3) performance optimization for wireless networks with energy constraints. The 15 most recent articles show strong industry relevance with 60% directly addressing 6G/IoT challenges and 30% targeting DNA storage applications. Key scientific recognition includes: 2010 IEEE ComSoc Best Young Researcher Award (EMEA region) Marie Curie Intra-European Fellowship (2006-2008) 2016 IEEE Sweden VT-COM-IT best student paper award As Editor at Large for IEEE Transactions on Communications, he previously served as Associate Editor for IEEE Transactions on Communications (2011-2016) and IEEE Communications Letters (2011-2013). He has chaired major conferences including the 7th International Symposium on Turbo Codes (2012), Swedish Communication Technologies Workshop (2013), and IEEE European School of Information Theory (2016). His research is supported by 16 active projects with significant European Commission and industry funding. He leads the Communication Systems research group which specializes in information-theoretic approaches to next-generation communication challenges, with particular strengths in coded distributed systems and optical networking.

Siew Chongمشاهده پروفایل

مدرس

• Chemical Education
• Organometallic Chemistry
• Catalysis
• +۱ مورد دیگر

Dr. Siew Chong is a Lecturer in Chemistry at the University of New England's School of Science and Technology. She holds a PhD from the National University of Singapore and focuses on chemical education research and organometallic chemistry. Her work develops online adaptive learning tools to enhance student engagement and learning outcomes. Research Focus: Chong's interdisciplinary research bridges organometallic chemistry with telecommunications, specializing in error-correction coding for next-generation wireless systems. She has published on neural network-assisted polar code design and graph neural network applications in MIMO detection. Awards: Recognized for teaching excellence with three national and institutional awards in 2021. Her educational innovations focus on improving chemistry pedagogy through technology-enhanced learning methods.

• Machine Learning
• Communication Theory
• Data Storage
• +۳ مورد دیگر

Elnaz Banan Sadeghian is an Assistant Professor at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science and the Department of Electrical and Computer Engineering. She holds a PhD in Electrical and Computer Engineering from Georgia Institute of Technology (2017), alongside MS degrees from Georgia Tech (2015) and AmirKabir University (2008), and a BS from Shahid Beheshti University (2005). Her research focuses on Communication Theory, Machine Learning, Data Storage (particularly Magnetic Recording), Assistive Technology, and Brain-Computer Interfaces (BCI). Recent work includes neural network-based approaches for multitrack detection in magnetic recording and low-latency BCI systems leveraging time-series prediction and self-attention mechanisms. She also explores subject-independent BCI frameworks using supervised autoencoders. Dr. Sadeghian has received notable grants including an NSF CAREER Award and NSF CRII funding for machine learning applications in magnetic storage. Her contributions span over 20 peer-reviewed publications since 2007, addressing topics like asynchronous track detection algorithms, timing error correction in read channels, and fractal analysis of EEG signals for BCI. She is an ASTC Fellow and active member of IEEE and EMBS societies. Currently, she advises PhD candidates with expertise in machine learning and communication theory.

• Wireless Digital Communications
• Noncoherent Receiver Design
• Error Control Coding
• +۵ مورد دیگر

Dr. Ian D. Marsland is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University (Faculty of Engineering and Design). He holds a Ph.D. from the University of British Columbia and has been affiliated with Carleton since 1999. His research focuses on wireless digital communications, noncoherent receiver design, error control coding, and indoor localization using wireless networks. He has contributed to advancements in SCMA, FTN signaling, and polar codes, with a strong emphasis on iterative decoding applications. Education: B.Sc.Eng. (Honours) in Mathematics and Engineering, Queen's University (1987) M.Sc. in Electrical Engineering, University of British Columbia (1994) Ph.D. in Electrical Engineering, University of British Columbia (1999) Research Interests: Wireless communication systems (stationary/mobile) Error control coding (LDPC, turbo, polar codes) Noncoherent receiver design Faster-than-Nyquist (FTN) signaling and neural network-aided detection Multidimensional constellations for SCMA systems Indoor localization via wireless networks Recent Research Trends: Dr. Marsland’s recent work emphasizes low-complexity detection algorithms for FTN signaling, SCMA constellation design, and polar code optimization. His publications highlight advancements in throughput-based coding, sphere decoding for SCMA, and high-resolution positioning techniques using MUSIC-based methods. Grants & Advising: While specific grants or student advisees are not detailed in the provided texts, his research portfolio indicates sustained involvement in collaborative projects with industry and academic partners. His lab focuses on next-generation wireless systems and signal processing innovations. Labs & Teams: His research is conducted within Carleton’s Department of Systems and Computer Engineering, emphasizing interdisciplinary work in communications and signal processing. Collaborators include researchers from institutions like the University of Toronto and industry partners.

Charlotte Langlaisمشاهده پروفایل

دانشیار

• Signal Processing
• Digital Communications
• Wireless Communications
• +۲ مورد دیگر

Charlotte Langlais is a Study Director and Researcher at IMT Atlantique in the Mathematical and Electrical Engineering department. She obtained her PhD in Electronics from INSA de Rennes in 2002, with research focused on turbo-equalization for wireless HF ionospheric transmission. Her current research explores signal processing and digital communications, particularly wireless communications, coding/modulation, MIMO systems, and multi-user transmission. Dr. Langlais teaches digital electronics using innovative flipped classroom approaches and coordinates project-based learning initiatives. She leads the coordination of project courses and industry partnerships involving over 50 economic partners. Her publications demonstrate expertise in mMIMO systems, NOMA techniques, and reconfigurable intelligent surfaces for 5G/6G applications. Recent work shows strong focus on railway communications, channel modeling, and efficient beamforming strategies for mmWave networks.

Fiona Fangمشاهده پروفایل

استادیار

• Machine Learning for Wireless Communications
• Non-Orthogonal Multiple Access (NOMA)
• Reconfigurable Intelligent Surfaces (RIS)
• +۴ مورد دیگر

Fiona Fang is an Assistant Professor in the Department of Electrical and Computer Engineering and Department of Computer Science at Western University, Canada. She holds a Ph.D. from the University of British Columbia (2017) and has prior academic experience at Durham University (2020–2022) and the University of Manchester (2018–2020). Her research focuses on machine learning for intelligent wireless communications, NOMA, RIS, MEC, and Edge AI. She serves as an editor for IEEE Transactions on Wireless Communications and has received awards including the IEEE SPCC Early Achievement Award (2023) and the 2024 Junior Faculty Award of Excellence in Research. Education: Ph.D. (UBC), M.Sc. and B.Sc. (Lanzhou University). Research emphasizes interdisciplinary applications of machine learning in wireless systems, with contributions to energy-efficient resource allocation and network design. Key publications include surveys on multi-access edge computing and RIS-assisted NOMA networks. She actively organizes conferences like IEEE Globecom and ICC, and her work bridges theoretical advancements with practical network implementations.

Mikael Skoglundمشاهده پروفایل

استاد

• Information theory
• Communications
• Signal processing
• +۵ مورد دیگر

Mikael Skoglund is a Professor and Head of the Division of Information Science and Engineering at KTH Royal Institute of Technology. His academic roles include membership in the Executive Committee of Digital Futures, and leadership in projects such as Humanizing the Sustainable Smart City (HiSSx) and PERCy. He holds a Ph.D. from Chalmers University of Technology (1997) and became a Professor at KTH in 2003. Education: Ph.D. in Signal Processing, Chalmers University of Technology, 1997 Docent in Signal Processing, KTH, 2001 Professor of Communication Theory, KTH, 2003 Research Interests: Focus on Information Theory, Communications, Signal Processing, Networks, Security and Privacy, Quantum Information, and Inference & Learning. His work bridges theoretical foundations with applications in wireless systems, federated learning, and privacy mechanisms. Projects & Collaborations: Leading initiatives in integrated sensing and communication (ISAC), federated learning, and smart city technologies. Active in strategic research areas like ICT-TNG and Digital Futures. Awards: IEEE Fellow (2023). Students & Grants: Supervised over 40 Ph.D. and postdoc researchers. Notable students include Borja Rodríguez Gálvez (Ph.D. 2024) and Amirreza Zamani (Ph.D. 2023). Active in securing grants for projects like PERCy and HiSSx. Labs/Teams: Part of the Digital Futures research center and the Division of Information Science and Engineering at KTH, fostering interdisciplinary innovation in digital technologies.

Alexios Balatsoukas-Stimmingمشاهده پروفایل

استادیار

• VLSI Circuits for Communications
• Error-Correction Coding Theory
• Non-Linear Signal Processing
• +۳ مورد دیگر

Dr. Alexios Balatsoukas-Stimming is an Assistant Professor in the Electronic Systems group of the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). His research focuses on the intersection of communications, hardware design, and machine learning, with specific interests in VLSI circuits for communications, error-correction coding, non-linear signal processing, and massive MIMO systems. He also explores applications of approximate computing and machine learning in communication signal processing. Education: Dipl.-Ing. (2010) Electronics and Computer Engineering, Technical University of Crete MSc (2012) Electronics and Computer Engineering, Technical University of Crete PhD (2016) Computer and Communications Sciences, École polytechnique fédérale de Lausanne (EPFL) Research Interests: His work emphasizes interdisciplinary approaches to improve communication systems' speed, reliability, and energy efficiency. Key areas include VLSI implementation of coding algorithms, belief propagation decoding, and leveraging machine learning for signal processing tasks. He has contributed to advancements in polar codes, LDPC codes, and full-duplex communication systems. Affiliations: Holds an honorary adjunct assistant professor position at Rice University (USA). Active in conferences and journals as a committee member, founding editor (IEEE journal), and reviewer. Labs/Teams: Associated with the Signal Processing for Communications Lab , Electronic Systems group, Center for Wireless Technology Eindhoven , and the EAISI initiative at TU/e.