Find Academic Supervisors

Eddie BallView profile

Professor

• RF Engineering
• Wireless Systems Design
• Millimeter-Wave Communications
• +5 more

Professor Eddie Ball is a Professor of Radio Frequency Engineering at the University of Sheffield's School of Electrical and Electronic Engineering, and a UKRI Future Leaders Fellow (2021-2028). He leads the Electromagnetics, Wireless Hardware & RF Devices research theme and directs the EPSRC Millimetre Wave Measurement Laboratory. His expertise spans RF circuit/system design, SDR, and millimeter-wave technologies, with a focus on IoT applications and hardware manufacturing. Qualifications: Ph.D., University of Sheffield (2024) M.Eng (1st class), University of York (1996) Chartered Engineer Research Interests: Novel RF circuit/system design Millimeter-wave transceivers and antennas RF-MMIC and SiGe design IoT radio systems and blockchain integration Low-cost, high-performance wireless protocols Teaching: Creator and instructor for EEE6239: Radio Transceiver System & Circuit Design 2nd-year course leader for VHF Synthesiser for Wireless Communications Labs/Teams: EPSRC Millimetre Wave Measurement Laboratory Future Millimetre Wave RF Transceiver Architectures Project

• Electromagnetic Compatibility
• Power Electronics
• Signal Processing
• +3 more

Markeljan Fishta is a Researcher at the Department of Electronics and Telecommunications (DET) of the Polytechnic University of Turin. His roles include teaching assistance in courses such as PCB Design for PhD students, Analog Electronics for Master’s students, and Foundations of Electronics for Aerospace Engineering undergraduates. His research focuses on electromagnetic compatibility (EMC), power electronics, and wireless communication systems, particularly in reducing electromagnetic interference (EMI) in power converters and developing acoustic-based communication for smart water networks. His recent work emphasizes EMI mitigation techniques in wide bandgap (WBG) inverters, GaN-based DC-DC converters, and sigma-delta modulators. Additionally, he explores in-pipe acoustic communication for urban water infrastructure, addressing challenges in channel modeling and data transmission reliability. Fishta collaborates with researchers like Franco Fiori and Erica Raviola on these topics, contributing to both theoretical advancements and applied solutions. His publications reflect a multidisciplinary approach, spanning IEEE Transactions on Electromagnetic Compatibility and international workshops. His research trends show a strong emphasis on source-based EMI reduction strategies, robust modulator design, and innovative communication methods for infrastructure monitoring. Fishta’s teaching contributions span multiple engineering programs, demonstrating his commitment to both academic instruction and cutting-edge research in electronic systems and communication technologies.

• Artificial Intelligence
• Machine Vision
• Distributed Computing
• +11 more

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

Suruz MiahView profile

Associate Professor

• Cyber-Physical Systems
• Optimal Control and Estimation
• Multi-Agent Systems
• +3 more

Suruz Miah is an Associate Professor in the Department of Electrical and Computer Engineering at Bradley University and an Adjunct Professor at the University of Ottawa's School of Electrical Engineering and Computer Science. He holds a B.Sc. from Khulna University of Engineering & Technology (Bangladesh) and M.A.Sc./Ph.D. degrees from the University of Ottawa. His research focuses on Cyber-Physical Systems, Optimal Control, Multi-Agent Systems, and RFID Technology. He has held roles including Visiting Associate Professor at Hosei University (Japan), Visiting Research Fellow at DRDC Canada, and Part-Time Professorships at multiple institutions. Education: B.Sc., Computer Science and Engineering, Khulna University of Engineering & Technology (2004) M.A.Sc. and Ph.D., Electrical and Computer Engineering, University of Ottawa (2007, 2012) Research Interests: Dr. Miah specializes in Cyber-Physical Systems, including autonomous robotics, multi-agent coordination, optimal control, and machine learning applications. He leads projects on mobile robot navigation, energy management systems, and RFID-based localization. His work bridges theoretical control systems with practical implementations in robotics and industrial automation. Advising & Collaborations: Advised/co-authored over 20+ student research projects, focusing on reinforcement learning, multi-agent systems, and robotics. Collaborates with institutions like DRDC Canada, Bradley University's Cyber-Physical Systems Lab, and University of Ottawa's MIRaM Lab. Developed open-source frameworks like MAFOSS (Multi-Agent Framework using Open-Source Software) and BEMOSS (Building Energy Management System). Labs & Teams: Principal Investigator at Bradley's Cyber-Physical Systems Lab. Research member of the Machine Intelligence, Robotics, and Mechatronics (MIRaM) Lab at the University of Ottawa.

Vitaly PetrovView profile

Assistant Professor

• Terahertz Communications
• 6G and Beyond Networks
• Satellite Communications
• +5 more

Vitaly Petrov is an Assistant Professor at the Division of Communication Systems within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. He leads the TERANET@KTH Research Lab and focuses on terahertz (THz) communications, 6G/7G networks, and non-terrestrial networks (NTNs). His research bridges theoretical frameworks with experimental validations, addressing challenges such as beamforming, blockage mitigation, and physical layer security in high-frequency systems. Affiliations: KTH Royal Institute of Technology (2024–present), Northeastern University (2022–2024), Nokia Bell Labs (2020–2022) Education: PhD in Communications Engineering (Tampere University, 2020), M.Sc. in IT & Security (SUAI University, 2011), M.Sc. in Communications Engineering (Tampere University of Technology, 2014) Research interests span THz band communications, satellite integration in 6G+, and innovative beam management techniques. He has pioneered work on near-field THz systems and wavefront hopping for security. His contributions include experimental platforms for beam realignment algorithms and CubeSat missions for 7G THz exploration. Awards: Best Student Paper (IEEE VTC 2015), Best Student Poster (IEEE WCNC 2017), IEEE Finland Best Journal Paper (2019), Runner-Up Best Paper (IEEE CCNC 2025) Teaching includes courses on mobile networks, scientific writing, and research methodology. He actively contributes to IEEE journals as an editor and has advised Ph.D. students through open positions (e.g., deadline March 2024). Labs and projects: TERANET@KTH, 7G THz CubeSat mission, seamless satellite integration with 6G+ networks, re-programmable THz surfaces for sensing/communications.

Luca ManneschiView profile

Lecturer

• Machine Learning
• Neuromorphic Computing
• Biological Networks
• +3 more

Dr Luca Manneschi is a Lecturer in Machine Learning at the School of Computer Science, University of Sheffield, with an IBM Liaison role. He holds a PhD in Physics from the University of Sheffield (2021) and completed a PostDoc there before his current position since March 2022. His research focuses on designing learning algorithms inspired by biological networks for physically defined systems, emphasizing neuromorphic computing, reservoir computing, and stochastic environments. Education: Bachelor's in Physics: University of Padua (Italy) Master's in Physics: Sapienza University of Rome (Italy) PhD in Physics: University of Sheffield (2021) Research Interests: Dr. Manneschi explores algorithms for physically defined networks, leveraging biological network principles to enhance computation in dynamic environments. His work bridges machine learning with neuromorphic hardware, emphasizing adaptability and energy efficiency. Key areas include reservoir computing, magnetic metamaterials, and multi-timescale learning strategies. Grants & Funding: "Real-time Reservoir Computing on Prosthetic Devices" (2023–2025, Royal Society, PI) "MARCH: Magnetic Architectures for Reservoir Computing Hardware" (2021–2025, EPSRC, Co-PI) "CausalXRL: Causal Explanations in Reinforcement Learning" (2021–2024, EPSRC, Co-PI) "ActiveAI" (2019–2024, EPSRC, Co-PI) Lab/Team Affiliation: Machine Learning Research Group, School of Computer Science.

H. Metin AktulgaView profile

Associate Professor

• High Performance Computing
• Parallel Computing
• Big Data Analytics
• +3 more

H. Metin Aktulga is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. His research focuses on high-performance computing, parallel algorithms, and numerical methods for large-scale scientific applications. He leads interdisciplinary projects involving collaborations with computational physicists and materials scientists to develop scalable software systems. His work includes the development of PuReMD, a reactive molecular dynamics code, and DOoC+LAF, a task-based middleware for data analytics. He explores parallel computing on emerging architectures, emphasizing energy efficiency and performance optimization. His research spans applications in molecular modeling, nuclear physics, and computational biology. Awards and grants are not explicitly listed, but his contributions are highlighted through collaborations with projects like MFDn (nuclear structure) and SHINES (electronic structure computations). He advises students in computational methods and leads efforts to automate force field optimization using machine learning and big data analytics. Labs and teams include the High-Performance Computing group at MSU, with active participation in interdisciplinary initiatives to bridge simulation and data-driven discovery in materials science and quantum systems.

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +5 more

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Rabah AoufiView profile

Senior Lecturer

• Digital Signal Processing
• Microcontroller Systems
• Control Systems
• +3 more

Rabah Aoufi is a Senior Lecturer in the Department of Engineering Technology & Industrial Distribution at the College of Engineering, Texas A&M University. His primary affiliation includes the Electronic Systems Engineering Technology program. He can be reached at raoufi@tamu.edu and is located in FERM 304AA. His work focuses on digital signal processing, microcontroller systems, control systems, and engineering entrepreneurship. Research interests span embedded systems design, real-time data processing, and innovative educational projects. His publications reflect expertise in both foundational engineering principles (e.g., signal processing fundamentals) and applied technologies (e.g., microcontroller programming and big data frameworks). His articles demonstrate a career-long engagement with evolving technologies—from foundational 1980s multiprocessor architectures to modern big data systems. No scientific awards or grants are explicitly mentioned in the provided materials.

• High-performance computing
• Modelling of Acoustic Wave devices
• Embedded system design
• +3 more

Dr. Glenn Matthews is a Senior Lecturer in the School of Engineering at RMIT University, specialising in Electrical and Computer Engineering. He holds positions in both academic and research capacities, including Principal Investigator roles at CSIRO and Smart Services CRC. His teaching responsibilities include coordinating undergraduate courses such as Introduction to Engineering Computing and Engineering Design modules. Dr. Matthews' research focuses on high-performance computing, acoustic wave device modeling using Finite Element Method (FEM), and embedded system design. Notable projects include developing FEM software for SAW device analysis and investigating asynchronous computing architectures for high-throughput systems. He has supervised numerous research projects spanning machine learning applications in clinical analysis, gas sensing technologies, and neuromorphic learning. His work integrates hardware-software co-design principles, with contributions to radar SLAM systems, mercury vapor sensors, and neural network frameworks like SwiftSpike. Dr. Matthews collaborates with industry partners through ARC Linkage Grants and maintains affiliations with IEEE and DSP/Embedded Systems groups. His research outputs include over 20 peer-reviewed articles, with impactful contributions to sensor technology, circuit design, and biomedical applications.

• Telecommunications
• Wireless Communication Systems
• Radio Localization
• +5 more

Henk Wymeersch is a Professor at Chalmers University of Technology's Communication Systems department, part of the School of Electrical Engineering. He leads the Radio Localization and Sensing research area and is affiliated with the FORCE Research Center on optical communication. His work focuses on algorithm design and system analysis for 5G/6G systems, emphasizing integrated communication, localization, and sensing. Notable contributions include RIS-aided positioning, bistatic/multistatic sensing, and joint communication-sensing frameworks. Research Interests: His primary areas include Reconfigurable Intelligent Surfaces (RIS), 6G positioning/sensing, integrated ISAC systems, mmWave/V2X positioning, and distributed radar networks. Recent work explores challenges in near-field localization, OFDM-based ISAC under hardware impairments, and privacy-preserving techniques for localization systems. Publications: Over 485 publications (2025) highlight his expertise in mmWave SLAM, RIS applications, and 6G enablers. Recent trends focus on 6G sustainability, distributed ISAC architectures, and leveraging RIS for robust localization in cluttered environments. Affiliations: Active in the Communication Systems Group and FORCE Research Center. Collaborates internationally on Hexa-X 6G projects and standards development.

Ana RusuView profile

Professor

• Energy-Efficient Circuits
• Biomedical Applications
• Wireless Communications
• +6 more

Ana Rusu is a Professor and leader of the Mixed-Signal Integrated Circuits and Systems Group at the Division of Electronics and Embedded Systems, Department of Electrical Engineering, KTH Royal Institute of Technology. She holds a Docent title from KTH and has been at the university since 2001. Her research focuses on energy-efficient circuits, biomedical applications, and emerging technologies like graphene and SiC. She has supervised numerous PhD students and led major projects such as AIsing (VR-funded) and Implantable Bioimpedance Spectrometer (SSF-funded). Education: Diploma Engineer (M.S.) from Technical University of Iasi (1983), PhD from Technical University of Cluj-Napoca (1998), Docent in Circuit Theory (KTH, 2006). She previously served as Director of Doctoral Education at the School of ICT (2013–2017) and Vice-Dean at EECS (2018–2022). Research interests include energy harvesting, spintronics-CMOS integration, and Ising Machines. Her work spans over 150 peer-reviewed publications and includes awards like the T-NANO Best Paper (2020) and ISCAS Best Paper (2023). She has advised 16+ PhD graduates and actively contributes to academic governance and education.

• Cellular Automata
• Quantum Mechanics
• Computational Theory
• +3 more

Tommaso Toffoli serves as an Adjunct Professor at Boston University, focusing on foundational aspects of computation, quantum mechanics, and cellular automata. His research bridges theoretical computer science and physics, exploring how computational principles manifest in natural and artificial systems. His work emphasizes the interplay between computation and physical laws, particularly in areas like quantum information theory, thermodynamics of computation, and emergent phenomena. He has pioneered the development of cellular automata machines as tools for simulating complex systems, with applications ranging from artificial life to quantum computing. Key research interests include the theoretical limits of computation, information dynamics, and the design of substrate-universal computational models. His publications span topics such as quantum foundations, reversible computing, and the thermodynamic cost of information processing. No specific scientific awards or grants are mentioned in the provided text. His advising record is currently unspecified.

• Wireless Communications
• Information Theory
• Physical Layer Security
• +7 more

Eduard A. Jorswieck is a University Professor of Communication Systems at the Institute of Communications Engineering , Technical University of Braunschweig, since 2019. He has previously held a professorship at TU Dresden (2008–2019) and has been a lecturer at TU Berlin (2005–2008). Currently, he serves as the Geschäftsführender Leiter (Managing Director) of the Institute of Communications Engineering. Education: Dipl.-Ing. in Computer Engineering (2000), Doctorate (2004) from TU Berlin. Research Interests: His work bridges information theory and wireless communications, focusing on physical layer security , energy efficiency , reconfigurable intelligent surfaces (RIS) , NOMA , MIMO systems , and machine learning for network optimization . He explores stochastic orders, game theory, and fractional programming for resource allocation in 5G/6G networks. Publications & Trends: Recent articles emphasize RIS-aided URLLC , STAR-RIS , 6G security , and AI-driven resource management . His studies span multi-antenna systems , terahertz ISAC , and multi-agent reinforcement learning for UAV networks. Awards: IEEE Signal Processing Society Best Paper Award (2006) IEEE Fellow (2020) Fellow of Industry Academy (AIIA, 2024) Best Paper Award at IEEE ICC 2024 Students & Collaborations: He has mentored researchers like K.-L. Besser, P.-H. Lin, and M. Mross. His lab collaborates on projects involving quantum communication , industrial IoT , and multi-agent systems .

Dimo IvanovView profile

Assistant Professor

• Ultra-high field MRI
• Quantitative MRI techniques
• Brain microstructure imaging
• +7 more

Dr. Dimo Ivanov is an Assistant Professor at the Department of Cognitive Neuroscience at Maastricht University in the Netherlands. Since 2025, he has also served as Head of the 7 Tesla MR Core structure at the Cooperative Brain Imaging Center and as an Independent Research Group Leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt, Germany. His work bridges physics, neuroscience, and advanced imaging technology. Dr. Ivanov earned his PhD (Dr. rer. nat.) in Physics from the University of Leipzig/Max Planck Institute for Human Cognitive and Brain Sciences in 2012, followed by an M.Sc. in Physics from the International Physics Studies Program at the University of Leipzig in 2007. His educational foundation was completed with studies at the Foreign Language High School in Pleven, Bulgaria. Dr. Ivanov specializes in high-resolution structural, perfusion, and functional mapping of the human cortex and subcortical regions. His research focuses on quantitative MRI techniques to assess tissue vascularization, microstructure, myelination, and iron content. He develops advanced MRI sequences and analysis pipelines for both 7T and 3T systems, with translational applications in neurodegenerative disorders, psychiatric conditions, and neurodevelopmental studies. His work on resting-state and task-based fMRI provides critical insights into neural connectivity and brain dynamics. His publication record demonstrates consistent contributions to ultra-high field MRI methodology, with emphasis on 7T and 9.4T systems. His research spans technical MRI development, quantitative imaging methods, and clinical applications across multiple neurological conditions. Recent work shows increasing focus on translational applications connecting advanced imaging techniques with clinical outcomes. Dr. Ivanov has received numerous scientific awards and successfully secured competitive funding: ERC Marie Curie Program (2023) - 2-year postdoc position Maastricht University-Chinese Science Council PhD Program (2022) Maastricht University Center for Integrative Neuroscience PhD Program (2022) Magnetic Resonance in Medicine issue cover (2017) ISMRM merit award magna cum laude (2013) Trainee abstract travel award from the Organization for Human Brain Mapping (2011) Educational Stipends from the International Society for Magnetic Resonance in Medicine (2009-2011) Dr. Ivanov has successfully mentored postdoctoral researchers and PhD students through competitive funding mechanisms. His research group at the Max Planck Institute for Empirical Aesthetics and his position at Maastricht University support a collaborative environment that bridges physics, neuroscience, and clinical applications. His work with the 7 Tesla MR Core structure provides critical infrastructure for advanced neuroimaging research in Frankfurt. Dr. Ivanov leads the Structural and Physiological Imaging with High-Field MRI research group at the Max Planck Institute for Empirical Aesthetics. His team specializes in pushing the boundaries of ultra-high field MRI technology, developing novel acquisition and analysis methods that enable unprecedented insights into brain structure and function. The group maintains strong collaborations with multiple institutions across Europe and beyond.