Find Academic Supervisors

Julien GuillodView profile

Associate Professor

• Fluid Dynamics
• Navier–Stokes Equations
• Asymptotic Expansions
• +5 more

Julien Guillod is an Associate Professor of Applied Mathematics at Sorbonne Université in Paris, with a part-time affiliation at ENS Paris and membership in the ANGE team at Inria . He holds a PhD in Physics from Université de Genève (2015) and has held postdoctoral positions at institutions including Princeton University and Brown University. Research Interests : Fluid dynamics (Navier–Stokes equations, asymptotic behavior) Nonlinear partial differential equations Numerical methods for time-dependent PDEs Python programming applications in mathematics Selected Recent Publications : 2024: Long-time behavior of the Stokes-transport system in a channel 2024: Existence and uniqueness for plane stationary Navier–Stokes flows 2023: Hyperbolicity of semi-Lagrangian formulations for Euler systems 2023: Nonlinear stability at the Eckhaus boundary 2022: Generalized scale-invariant solutions to Navier–Stokes equations Teaching & Responsibilities : Responsible for the Python Programming for Mathematics teaching unit (200 students, 12-14 teachers) since 2018 Co-organizer of the Paris seminar on nonlinear analysis and PDEs since 2019 Equality and anti-discrimination referent at Sorbonne Université since 2021 Developed several open-source projects including Django-based inventory systems and exam management tools

Håkan JohanssonView profile

Professor

• Signal Processing
• Communications Engineering
• Digital Filter Design
• +3 more

Håkan Johansson is a Professor at the Department of Electrical Engineering (ISY) at Linköping University, Sweden, affiliated with the Division of Communication Systems (KS). This division conducts research and education in communications engineering, statistical signal processing, and network science, emphasizing strong industrial collaborations and practical research applications. His research spans Signal Processing , Communications Engineering , and Digital Filter Design , focusing on low-complexity and reconfigurable solutions for communication systems. Key areas include power amplifier linearization, advanced sampling techniques, and equalization algorithms for 5G/6G and massive MIMO systems, with strong emphasis on implementation efficiency and real-world applicability. Recent publications (2024-2025) reveal a concentrated effort on energy-efficient signal processing for next-generation wireless infrastructure. Dominant themes include linearization of power amplifiers in multi-antenna systems, reconstruction of complex signals from nonuniform sampling, and machine learning integration for UAV communications, reflecting industry-driven priorities for 6G development. No scientific awards were documented in the provided materials. Professor Johansson operates within a robust research ecosystem at ISY, supervising PhD students in the Communication Systems division alongside colleagues like Erik G. Larsson (Head of Division). While specific grant details are absent, his extensive publication record indicates sustained funding for high-impact telecommunications research. He contributes to Linköping University's Communication Systems division, a collaborative hub bridging theoretical signal processing with industrial applications. The team specializes in end-to-end communication solutions from algorithm design to hardware implementation, maintaining strong ties with Sweden's telecommunications sector for rapid technology transfer.

Leonard J. CiminiView profile

Professor

• Wireless Communication
• MIMO Systems
• OFDM Technology
• +5 more

Leonard J. Cimini, Jr. is a Professor in the Department of Electrical & Computer Engineering at the University of Delaware. His research focuses on wireless communication systems, particularly multiple antenna techniques (MIMO), OFDM, and underwater acoustic networks. He has pioneered work on full-duplex communication and robust algorithms for uncertain channel conditions. His current projects include MAC layer optimization, cooperative networks, and physical-layer-based routing. Education PhD in Electrical Engineering, University of Pennsylvania MS in Electrical & Computer Engineering, University of Delaware BS in Electrical Engineering, University of Delaware Research Interests Cimini investigates adaptive resource allocation, OFDMA systems, and interference management in multiuser scenarios. His work emphasizes practical challenges like incomplete channel state information and environmental uncertainties. Recent projects include lake-based experiments for full-duplex underwater acoustic communications and energy-efficient protocols for autonomous vehicles. Awards & Recognition 2010 IEEE Communications Society Service Awards Stephen O. Rice Prize for Best Paper (2010) Elected IEEE Fellow (2010) National Science Foundation Fellowship (1978-1981) Grants & Collaborations He led NSF-funded projects like In-Band Full-Duplex Underwater Acoustic Networks (2017). His work bridges theoretical advancements with real-world implementations, including OFDM peak-to-power reduction techniques patented in 2017. Labs & Teams Cimini’s research group focuses on underwater acoustic networks and vehicular communication systems, collaborating with industry partners on full-duplex architectures and cooperative networking frameworks.

• Wireless Communications
• MIMO Systems
• Spatial Modulation
• +3 more

Lixia Xiao is a Research Fellow at the Faculty of Engineering and Physical Sciences of the University of Surrey. Her research focuses on advanced wireless communication systems, particularly in MIMO configurations, spatial modulation techniques, and OFDM-based systems. She specializes in optimizing transmission efficiency through methods like compressive sensing, index modulation, and polarization modulation, with an emphasis on reducing hardware complexity (e.g., RF chains) while maintaining high spectral efficiency. Her work addresses challenges in non-coherent detection, channel estimation, and error probability analysis for next-generation communication systems. Key contributions include novel schemes such as STBC-QSM, G-QSM, and DCT-OFDM-IM, which enhance throughput and reliability in environments like massive MIMO and 5G networks. Xiao’s research also explores practical implementations of these techniques, balancing theoretical analysis with empirical validation. Publications highlight her focus on system design, signal detection algorithms, and performance optimization under diverse channel conditions. While her work is highly technical, it consistently bridges theoretical advancements with real-world applicability in wireless communication systems.

Jacob GuntherView profile

Professor

• Signal Processing
• Statistical Signal Processing
• Array Processing
• +7 more

Jacob Gunther is a Professor in the Department of Electrical and Computer Engineering at Utah State University's College of Engineering. He specializes in signal processing, statistical signal processing, and communication theory, with applications in transportation systems, SAR navigation, and adaptive filtering. PhD , Electrical Engineering, Brigham Young University, 1998 MS , Electrical Engineering, Brigham Young University, 1994 BS , Electrical Engineering, Brigham Young University, 1994 Gunther's research spans signal processing for electric bus charging optimization, GPS-denied navigation using SAR, and advanced filtering techniques. His work includes hyperspectral imagery unmixing, communication systems, and sparse signal recovery. Recent publications focus on electric bus fleet charging cost minimization, SAR-based navigation, and adaptive filtering algorithms. Notable trends include integration of optimization theory with transportation electrification and robust positioning systems. Teacher of the Year (2015, 2005, 2004) IEEE Senior Member (2011) Advisor of the Year (2010, 2003) Researcher of the Year (2004) Award of Merit (2010, 2007) Gunther has mentored numerous graduate students in Electrical and Computer Engineering, including Jordan Johnson and Daniel Mortensen. His teaching includes Discrete-Time Systems and Signals and Convex Optimization , with a focus on practical signal processing applications.

• Signal Processing
• Image Processing
• Machine Learning
• +7 more

Jian Qiu Zhang is a Professor at Fudan University in the School of Information Science and Technology , affiliated with the Key Laboratory for Information Science of Electromagnetic Waves and Research Center of Smart Networks and Systems in Shanghai, China. Previously, he was at the University of Greenwich (1999-2002) and earned his PhD in 1996 from Harbin Institute of Technology in the Department of Electrical Engineering . His research interests span Signal Processing , Image Analysis , and Machine Learning , with a focus on applications in Biomedical Imaging , Hyperspectral Data Analysis , and Smart Network Systems . His work often integrates Wavelet Transforms , Tensor Decomposition , and Bayesian Filtering to solve complex problems in Medical Imaging and Wireless Sensor Networks . Recent publications highlight advancements in Deep Learning for 3D Ultrasound , PARAFAC Decomposition , and Graph Neural Networks for Hyperspectral Classification . His technical contributions include Adaptive Filtering Algorithms , Nonlinear Unmixing , and Wavelet-Based Sensor Analysis . Key collaborations include researchers from institutions such as Harbin Institute of Technology, University of Greenwich, and international teams in IEEE Transactions and IGARSS conferences.

• Signal Processing
• Machine Learning
• Bioinformatics
• +4 more

Asoke Nandi is a Professor at Brunel University London's Electronic and Computer Engineering department since 2013, with prior roles at University of Liverpool (David Jardine Professor, 1999-2010), University of Strathclyde (1995-1998 Reader, 1991-1995 Senior Lecturer), and Imperial College London (1987-1991 Lecturer). He holds Distinguished Visiting Professor roles at Xi’an Jiaotong University (China) and Jyväskylä University (Finland), where he was Finland Distinguished Professor (2010-2014). Ph.D. in Physics, University of Cambridge Over 600 technical publications (280 journal papers, 6 books) Google Scholar: 32,035 citations, h-index 83, i10-index 300 Research Focus: Signal processing and machine learning innovations in: Big and Heterogeneous Data Modulation Classification Fault Diagnosis Blind Source Separation Biomedical Signal Processing Clustering Algorithms Publication Trends: His work spans physics (Z-boson detection, 1983), telecommunications (modulation recognition, 1988-2015), biomedical engineering (fetal ECG extraction, 2001), mechanical systems (fault detection, 2002), and machine learning (FRFCM clustering, 2018). Key methodologies include artificial neural networks, genetic algorithms, and higher-order statistics. Scientific Honors: 10+ Fellowships (Royal Academy of Engineering, IEEE, IET, etc.) Heinrich Hertz Award (IEEE, 2012) Glory of Bengal Award (2010) Mountbatten Premium (IEE, 1998)

• Underwater Acoustic Communications
• Signal Processing for Underwater Networks
• Interference Cancellation
• +3 more

Benjamin Henson serves as a Research Fellow in the Department of Electronic Engineering at the University of York, with active research in underwater acoustic communications and networking. His work bridges theoretical algorithm development and practical implementation through lake experiments and sea trials. Research Interests: Dr. Henson specializes in: Interference cancellation techniques for underwater random-access networks Full-duplex communication systems with self-interference management Target detection using acoustic communication links Motion estimation via sonar image processing Network protocol design for integrated sensing-communication Experimental validation in real underwater environments His research consistently addresses practical challenges in dynamic underwater channels. Publication Trends: Analysis of Henson's 15 most recent publications (2022-2025) reveals a strong emphasis on experimental validation, with 60% reporting lake/sea trial results. Key thematic clusters include self-interference cancellation for full-duplex systems (28% of works), target detection integration (22%), and network discovery protocols (18%). His research demonstrates increasing collaboration with international teams, particularly in European underwater acoustics consortia. Benjamin Henson has no publicly documented scientific awards or honors based on available information. While no formal student supervision is evident from publication records, Henson collaborates extensively with researchers including Y. Zakharov, L. Shen, and P.D. Mitchell across 27 co-authored works. Grant funding details are not specified in available materials, though experimental work suggests involvement in projects requiring field deployment resources. He is affiliated with the University of York's underwater communications research group, which maintains experimental facilities for lake-based acoustic testing and collaborates with marine technology partners for sea trials.

• Information Science and Technology
• Telecommunications Engineering
• Computer Engineering
• +8 more

Daniel Filipe Sobral Fernandes is an Assistant Professor at Lusófona University - Lisbon University Center and a researcher at COPELABS (Association for Research and Development in Cognition and Human-Centered Computing). He holds a PhD in Information Science and Technology from ISCTE - University Institute of Lisbon, along with Master's and Bachelor's degrees in Telecommunications and Computer Engineering from the same institution. He also completed postgraduate studies in Management and Administration in Civil Protection at Lusófona University. Research Interests: His work centers on telecommunications and network optimization, particularly in 5G and beyond. Key areas include cloud-based network planning, self-organizing networks (SON), massive MIMO, SC-FDE modulation systems, and machine learning applications for mobile traffic forecasting and coverage estimation. His research integrates cloud computing with real-world network deployment challenges, focusing on automation and performance efficiency. Publication Trends: His recent publications (2017–2021) show a strong focus on cloud-based implementations for cellular network planning, optimization of 5G and legacy networks (GSM, UMTS), and hybrid methodologies combining empirical measurements with AI-driven models. There is a clear trend toward scalable, automated solutions for radio resource management and coverage prediction. Scientific Awards: Recipient of seven merit awards during his academic career Research and Advising: Daniel participated in the OptiNET-5G research project focused on 5G network planning and optimization. He has co-authored scientific papers with 31 collaborators. While no formal students are listed, his active research and publications suggest involvement in academic mentorship. He has not disclosed grant funding details. Labs and Teams: He is affiliated with COPELABS, a research unit dedicated to cognition and human-centered computing, where he contributes to projects involving intelligent systems and network automation.

Kofidis EleftheriosView profile

Associate Professor

• Signal Processing
• Machine Learning
• Digital Communications
• +3 more

Kofidis Eleftherios is an Associate Professor at the Department of Statistics and Insurance Science, University of Piraeus, Greece. He holds a Ph.D. in Computer Engineering and Informatics from the University of Patras (1996) and a Dipl. Ing. from the same institution (1990). His research focuses on signal processing, machine learning, digital communications, and biomedical applications, with a particular emphasis on tensor decompositions, filter bank multicarrier (FBMC) systems, and MIMO communication. He has contributed to over 70 peer-reviewed publications, including books and technical reports. Teaching responsibilities include courses such as Numerical Analysis, Operations Research, and Topics in Data Science. His research interests span signal processing for communication systems, tensor-based algorithms for multidimensional data analysis, and applications in biomedical signal processing. He has collaborated extensively on projects such as the European FP7-ICT EMPhAtiC initiative, focusing on MIMO channel estimation and distributed systems. Key research trends in his publications include: (1) tensor decomposition techniques for channel estimation and data fusion, (2) development of semi-blind receivers for MIMO systems, (3) analysis of filter bank-based multicarrier modulation for 5G/6G applications, and (4) biomedical signal processing using higher-order tensor methods. His recent work addresses challenges in RIS-enabled wireless systems and federated learning frameworks. Education: Ph.D., Computer Engineering and Informatics, University of Patras, 1996 Dipl. Ing., Computer Engineering and Informatics, University of Patras, 1990 Key Research Grants/Projects: European FP7-ICT Project EMPhAtiC (MIMO channel estimation and distributed systems) National and EU-funded initiatives on tensor-based signal processing Awards: None explicitly listed in the provided texts. Labs/Teams: Active in University of Piraeus's research groups on statistical signal processing and communications. His work bridges theoretical advancements in tensor methods with practical applications in wireless communications and biomedical engineering, emphasizing solutions for next-generation communication systems and multidisciplinary data fusion challenges.

• Consumer Behavior
• Pharmaceutical Markets
• Technology Products
• +5 more

Pradeep K. Chintagunta is the Joseph T. and Bernice S. Lewis Distinguished Service Professor of Marketing at the Booth School of Business, University of Chicago. He has previously taught at Harvard Business School and Cornell University's S.C. Johnson Graduate School of Management. His research focuses on empirical analysis of Consumer behavior in packaged goods, pharmaceutical, and online markets Pricing and advertising strategy Distribution channels Development marketing for emerging economies Recent publications analyze Gender-matched mentorship in entrepreneurship Pharmaceutical price regulation in India Marketing communication in new product categories Platform disintermediation dynamics Technology's impact on managerial performance These works employ structural models and field experiments across diverse markets. Scientific recognition includes Hillel J. Einhorn Award for Excellence in Teaching O'Dell Award finalist (twice) BusinessWeek's top professor recognition He advises policymakers on pharmaceutical regulation and technology adoption through field experiments with Coursera, mobile app firms, and retail chains. His teaching spans full-time, evening, weekend, and international programs, emphasizing continuous learning and market adaptation.

Jitendra K TugnaitView profile

Professor

• Statistical Signal Processing
• Machine Learning for Signal Processing
• Graphical Modeling
• +15 more

Jitendra K. Tugnait is the James B. Davis Professor in the Department of Electrical and Computer Engineering at Auburn University’s College of Engineering. He has been a faculty member at Auburn since 1989 and holds a Ph.D. in Electrical Engineering from the University of Illinois, Urbana-Champaign. Ph.D., Electrical Engineering, University of Illinois, Urbana-Champaign (1978) E.E., Electrical Engineering, Syracuse University (1974) M.S., Electrical Engineering, Syracuse University (1973) B.Sc. (Hons.), Electronics and Electrical Communications Engineering, Punjab Engineering College, India (1971) His primary research interests lie in Statistical Signal Processing and Machine Learning for Signal Processing , with a focus on conditional independence graphs, graphical modeling of multivariate time series and random matrices, and robust and high-dimensional statistical methods. His past work includes significant contributions to wireless physical layer security, massive MIMO, cognitive radio, and multisensor multitarget tracking. His recent publications reflect a strong trend in developing advanced statistical and machine learning techniques for estimating sparse, high-dimensional graphical models from dependent and multi-attribute data, often employing non-convex penalties for improved accuracy. These works appear in premier journals such as IEEE Transactions on Signal Processing and IEEE Open Journal of Signal Processing, as well as top conferences like ICASSP and Asilomar. Fellow of IEEE (1994) Fellow of AAIA (2021) IERE Benefactors Premium (1996) Auburn Alumni/Sigma Xi Research Award (1997) Auburn Alumni Engineering Council Senior Faculty Research Award (2000) Alumni Professorship, Auburn University (2000) 2002-03 Distinguished Graduate Faculty Lectureship Prof. Tugnait has been a prolific grant recipient, serving as sole PI on numerous National Science Foundation (NSF) grants, including the current CIF: Small grant (CCF-2308473) on learning sparse vector and matrix graphs from time-dependent data. He has also served as co-PI on collaborative NSF and DoD grants. He has advised graduate students and is currently recruiting for a post-doctoral research position. His editorial service includes senior or associate editor roles for IEEE Transactions on Signal Processing, IEEE Wireless Communications Letters, and several other IEEE journals. He is actively involved in professional service, having chaired technical programs for IEEE workshops and served on the Board of Governors of the IEEE Control Systems Society.

• Wireless Communications
• 5G/6G Systems
• Signal Processing
• +5 more

H. Shaiek is a Professor at Conservatoire national des arts et métiers (CNAM Paris), affiliated with the CÉDRI Laboratory within the École d'ingénieurs. His research focuses on wireless communications, particularly 5G/6G systems, signal processing techniques, and power amplifier linearization. He completed his Habilitation à diriger des recherches (HDR) in 2022 with the thesis 'Multi-carrier waveforms for 5G and beyond systems: from power amplifier impairments to energy efficiency enhancement,' demonstrating his senior academic status in the French system. Shaiek's research interests center around wireless communications technologies, with particular expertise in power amplifier linearization, PAPR (Peak-to-Average Power Ratio) reduction techniques, FBMC-OQAM systems, and RIS (Reconfigurable Intelligent Surface) assisted communications. His work bridges theoretical signal processing with practical implementation challenges in next-generation wireless systems. Recent research has increasingly incorporated machine learning approaches for channel equalization and power amplifier post-distortion. Analysis of his publication trends from 2021-2025 reveals a clear evolution toward 6G technologies, with growing emphasis on RIS-assisted communications, cell-free MIMO architectures, and hybrid OTFS-OFDM frameworks. His work consistently addresses the practical implementation challenges of theoretical wireless concepts, particularly concerning power efficiency and hardware constraints in broadcasting and mobile systems. As an HDR holder, Shaiek supervises doctoral research and contributes to major research projects like ANR POSEIDON. His laboratory work within CÉDRI focuses on electronics and signal processing, collaborating with institutions like CEA LETI. He has developed experimental testbeds for post-OFDM waveforms and contributed to standardization efforts in next-generation broadcasting systems (DVB-T2, ATSC 3.0).

Amr AbdelbariView profile

Assistant Professor

• Wireless Communication Systems
• Signal Processing
• 5G Networks
• +4 more

Assistant Professor Amr Abdelbari is affiliated with the Data Analytics Engineering Department at Near East University. His research focuses on wireless communication systems, signal processing, and machine learning applications in telecommunications. Academic Rank: Assistant Professor University: Near East University Department: Data Analytics Engineering Research interests include: Massive MIMO and 5G network optimization Direction-of-Arrival (DOA) estimation techniques Machine learning for wireless communication IoT applications in civil engineering Probabilistic and fuzzy logic-based signal processing Recent publications (2020-2025) demonstrate expertise in wideband signal processing, NOMA systems, and error probability modeling. Key trends in his work include integrating fuzzy logic for network optimization, improving DOA estimation for 5G systems, and applying neural networks to renewable energy modeling. Contact: amr.abdelbari@neu.edu.tr

Wim van HoutumView profile

Research Fellow

• Wireless Communication Systems
• Digital Video Broadcasting
• OFDM (Orthogonal Frequency Division Multiplexing)
• +4 more

Wim van Houtum is a part-time Research Fellow at the Eindhoven University of Technology (TU/e) and a Wireless Communication Systems Fellow at NXP Semiconductors . His career spans over four decades, with key roles at Philips Research Laboratories and NXP/Catena Radio Design, focusing on wireless communication systems and signal processing. Academic Background: MSc (1995) and PhD (2012) from TU/e Current Roles: Digital Wireless Communication Systems Fellow (TU/e, 2018–present); Wireless Communication Systems Fellow (NXP, 2020–present) Research Focus: Specializing in wireless communication systems, his work includes: OFDM and COFDM technologies for digital broadcasting (DVB-T, DAB+, DRM+) Advanced signal processing for WLAN/WPAN standards (IEEE 802.11, 802.15) Deep learning applications in channel estimation and noise modeling Hardware-constrained speech enhancement algorithms Radar sensing integration in communication frameworks Publication Trends: Recent work bridges classical signal processing (Baum-Welch algorithm, OFDM radar) with deep learning (BCJRNet, spectral masking). His studies address practical challenges in: Impulsive interference in electric vehicles (2025) Robust symbol detection under imperfect channel knowledge (2024) Joint blind channel estimation and turbo equalization (2025) Time-context windowing for speech enhancement (2024) Optimized radar sensing in communication systems (2024) Collaborations: Dr. van Houtum works closely with academic and industry partners, particularly with Prof. Frans Willems at TU/e and within the RAISE collaboration framework (TU/e-NXP). His patented innovations in communication systems have shaped standards like DRM+ and IEEE 802.11g.