Dr. Sándor Roland Major is an Assistant Professor at the University of Debrecen , Faculty of Informatics, Department of Information Technology. His office is located at room I129 on the 1st floor of the Faculty of Informatics building at 4028 Debrecen, Kassai Street 26., Hungary. Email: major.sandor@inf.unideb.hu Research Interests Optimization Non-classical architectures FPGAs (Field-Programmable Gate Arrays) Automata theory
Ana Isabel Silva Martins is a Doctoral Research Fellow at the University of Oslo's Faculty of Mathematics and Natural Sciences , affiliated with the Department of Astrophysics. Her research focuses on cosmology, gravitational waves, and machine learning applications in high-performance computing. Education: MSc in Experimental Physics (2022-2024), Utrecht University BSc in Engineering Physics (2019-2022), Instituto Superior Técnico, University of Lisbon Research Interests: She explores early detection mechanisms for gravitational wave signals from binary neutron star coalescence, leveraging convolutional neural networks (CNNs) and field-programmable gate arrays (FPGAs). Her work bridges astrophysics, machine learning, and computational hardware optimization. Projects & Groups: Active in the CMB&CO group , Cosmoglobe , and CosmoglobeHD . Collaborates on cosmology and extragalactic astronomy research.
Tawachi Nyasulu is a Research Associate in the StrathSDR laboratory within the Department of Electronic and Electrical Engineering at the University of Strathclyde, United Kingdom. He holds a PhD in Electronic and Electrical Engineering from the same institution, awarded in 2022, and has a strong background in spectrum research and wireless communications. His educational background includes: PhD in Electronic and Electrical Engineering, University of Strathclyde (2016–2022) MSc (Eng) in Digital Communications Networks, University of Leeds (2014–2015) BSc in Electrical Engineering, University of Malawi - Polytechnic (1998–2003) His research focuses on efficient radio spectrum management , dynamic spectrum access , spectrum sharing , and heterogeneous wireless coexistence , with an emphasis on modeling techniques such as hypergraph theory. His work aims to improve wireless connectivity, especially in rural and developing regions, leveraging technologies like TV white space. The most recent publications reflect a consistent trend in applying advanced mathematical models to real-world spectrum challenges. His work bridges theoretical modeling with practical implementation in software-defined radio and policy analysis, particularly in the context of global connectivity and sustainable development. His scientific recognitions include: Fellowship of the Higher Education Academy (FHEA) Postgraduate Certificate in Learning and Teaching in Higher Education Nyasulu has served as a Teaching Assistant since 2016 on the Digital Electronics course at Strathclyde and has delivered international teaching at Shanghai University of Electric Power on Computer Architecture. He is completing the Strathclyde Supervisor Development Programme, which will allow him to co-supervise PhD students. He has contributed to major research projects such as the £12 million TUDOR project, where he led the spectrum sensing workstream, and has been involved in initiatives focused on rural connectivity and 5G innovation. His research aligns with UN Sustainable Development Goals, particularly through technology for development and education. He is an active member of the StrathSDR laboratory, a leading research group in software-defined radio and future wireless networks, contributing to both national and international collaborative efforts in telecommunications research.
Durai Arun Pannir Selvam is a Research Associate in the Department of Electronic & Electrical Engineering at the University of Strathclyde, Faculty of Engineering. He is based in the Neuromorphic Sensor Signal Processing Lab within the Centre for Signal and Image Processing, under the supervision of Dr. Gaetano. He also volunteers as a Student Mental Health First Aider (SMHFA) in the department. Education: Doctor of Engineering (PhD), The University of Edinburgh (Nov 2018 – Feb 2023). Research focused on enhancing image processing techniques for organ contour delineation in adaptive radiotherapy. Masters in Medical Electronics, with specialization in signal processing, image processing, and biomedical instrumentation. His research interests lie at the intersection of biomedical engineering and artificial intelligence, particularly in medical image processing , neuromorphic computing , and signal processing . He applies advanced computational methods to improve medical diagnostics and treatment planning, especially in radiotherapy. His work integrates AI models with hardware-efficient implementations using FPGAs and embedded systems. His recent publication explores quantisation-aware training in spiking neural networks, highlighting his focus on efficient, low-power AI models for real-world applications. The work investigates bi-modal accuracy distributions, contributing to the robustness and reliability of neuromorphic systems. He is actively engaged in academic service, having organized the StrathWide 2024 Researcher Conference and delivered an invited talk at a PGR Social and Networking Session, demonstrating his commitment to research community building. Professional Activities: Organiser, StrathWide 2024: The University of Strathclyde Researcher Conference (April 2024) Speaker, PGR Social and Networking Session (September 2024) He has prior experience as a Project Engineer, Senior Research Engineer, and Junior Research Engineer at CSIR National Aerospace Laboratories and CPEES & NHHID, Anna University, contributing to various signal and image processing projects. His lab work centers on the development and application of neuromorphic sensors and their signal processing pipelines.
Heikki Kariniemi is a Doctoral Researcher at the Faculty of Engineering and Natural Sciences (Tampere University). His work focuses on advanced computer architecture and fault-tolerant systems. Tampere University Research Interests: Network-on-Chip (NoC) design Fault-tolerant communication protocols Multiprocessor Systems-on-Chip (MPSoC) Field-Programmable Gate Arrays (FPGA) Globally Asynchronous Locally Synchronous (GALS) architectures Publication Trends (2001-2010): 21 total outputs, including 18 conference papers and 1 dissertation. Key themes: reconfigurable multiprocessor systems, fat-tree network topologies, and FPGA-based fault-tolerant communication mechanisms. Academic Activities: Session chair at SoC 2009 and SoC 2010 symposia Peer reviewer for journals: IEEE Transactions on Circuits and Systems, IEEE Transactions on Electronic Publishing Referee for conference articles at NORCHIP 2009
Oriod Malo is a Lecturer at the Faculty of Computer Science and IT , University of Metropolitan Tirana, Albania. He is currently pursuing a PhD in Artificial Intelligence and contributes to technical development through open-source projects. Lecturer in Computer Architectures PhD Candidate in AI Education BSc in Electronic and Telecommunication Engineering, University of Bologna (2019) MSc in Electronic Engineering, University of Bologna (2022) Research Interests His research focuses on creating mathematical models using MATLAB and Python (including AI models) and designing electronic systems with Field-Programmable Gate Arrays (FPGA). His work bridges theoretical modeling with practical implementation in hardware-software co-design. Technical Contributions Through his GitHub repositories, Oriod has developed projects such as: DE10_Lite_SONAR : FPGA-based SONAR mini-project using HC-SR04 ultrasonic sensors and VGA display Spartan3ANDevelopmentBoard : Xilinx Spartan-3AN FPGA development tkinterCalculator : Python GUI calculator with symbolic math capabilities
Bill Ashmanskas is a Senior Lecturer in the Department of Physics and Astronomy at the University of Pennsylvania's School of Arts & Sciences. His roles include teaching undergraduate physics courses and developing curriculum, alongside research in instrumentation, FPGA-based electronics, and medical imaging technologies. He is affiliated with both the Physics department’s HEP Instrumentation Group and the Radiology department’s Physics and Instrumentation Group. Education includes a Ph.D. from UC Berkeley (1998) and an A.B. from Harvard University (1992). His research focuses on readout electronics for High Energy Physics and Positron Emission Tomography (PET), with notable contributions to waveform-sampling DAQ systems and ASIC testing for HL-LHC upgrades. Key research interests span instrumentation design, medical imaging innovations, and HEP detector technologies. Recent publications emphasize PET-DBT hybrid systems and radiation-tolerant electronics. Awards include the 2020 Provost’s Teaching Excellence Award and a 2005 PECASE for early-career scientific achievement. Grants/Advising: Active in CDF and ATLAS collaborations, with extensive contributions to Fermilab experiments and Penn’s HEP instrumentation projects. Labs/Teams: HEP Instrumentation Group (Physics), Radiology Physics Group (Penn Medicine).
Mohamed-Yahia Dabbagh is an Associate Professor, Teaching Stream in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds a PhD from North Carolina State University (1989), an MSE from the University of Michigan (1981), and a BSE from the University of Aleppo (1976). His research focuses on digital signal processing, image/video processing, and VLSI multiprocessor implementation of algorithms for communication systems and computer vision. He has developed architectures for digital filters and explored video motion estimation, segmentation, and pattern recognition. Dr. Dabbagh has authored numerous articles in IEEE and other venues, including work on wireless sensor anomaly detection, smartphone application monitoring, and FPGA-based digital filter implementations. His teaching includes courses such as Signals and Systems (ECE 207), Linear Circuits (ECE 140), and Probability and Random Processes (ECE 316). His research has spanned VLSI environments, Bayesian classification for video processing, and real-time signal processing techniques. Notable contributions include multiprocessor filter implementations and adaptive motion estimation algorithms. Dr. Dabbagh is a Professional Engineer (PEng) registered in Ontario and has contributed to technical reports on imaging systems and geographic information systems during his tenure at Kuwait-based institutions.
Dr. Alexander Wild is a former researcher at Ruhr-University Bochum's Faculty of Computer Science, affiliated with the Horst Görtz Institute for IT-Security. He completed his Diploma in 'Security in Information Technology' (2006–2012) and held a PhD position (2012–2016) at the Chair for Embedded Security. His research focuses on FPGA security, side-channel analysis, and physical unclonable functions (PUFs). Education: PhD in Embedded Security, Ruhr-University Bochum (2012–2016) Diploma in Security in Information Technology, Ruhr-University Bochum (2006–2012) Research interests include securing FPGAs against side-channel attacks, designing PUF-based authentication mechanisms, and optimizing hardware security countermeasures. His work bridges hardware design with cryptographic applications, emphasizing practical implementations on reconfigurable systems. His publications (e.g., automated EM probe repositioning, GliFreD duplication schemes) highlight contributions to FPGA-based security primitives and PUF reliability. He has collaborated with institutions like the Horst Görtz Institute and co-authored works presented at conferences such as HOST, CHES, and FPL. Affiliated with the Emmy Noether Group and involved in projects like the CAVE research initiative, he emphasizes interdisciplinary approaches to hardware security challenges.
Associate Professor Cesar Ortega-Sanchez is affiliated with Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), where he holds the role of Associate Professor since 2005. He has served in leadership roles including Academic Lead of the Engineering Foundation Year (2015-2022), Associate Dean of Teaching and Learning (2013-2014), and Chair of IEEE WA Section (2016-2017). His expertise spans bio-inspired systems, embedded systems, robotics, and engineering education. Education: BEng (Hons) from the Metropolitan Autonomous University (Mexico), MSc from Brunel University (UK), and PhD from the University of York (UK). Specializes in curriculum design, assessment methodologies, and promoting student employability. Mentor of Curtin Robotics Club and IEEE Curtin Student Branch since 2009. Research Interests: Embedded systems, bio-inspired architectures, educational pedagogy, and indigenous content integration in engineering curricula. Over 60 publications in areas like FPGA-based assistive technologies, smart home systems, and energy storage monitoring. Awards include the 2015 Australian Office of Learning and Teaching Citation and M.K. Stott Prize (2000). Teaching roles include leading ELEN1000 (Electrical Systems) and ENGR1000 (Engineering Connections), with a focus on innovative teaching methods like project-based learning (e.g., 'Crazy Machine' lab projects). Active in educational committees at university and national levels.
Adrian Virgil CRĂCIUN is an Associate Professor at the Department of Electronic and Computers, Faculty of Electrical Engineering and Computer Science, Technical University of Brașov. His research focuses on analog electronics, electronic circuit modeling, and data acquisition systems. He has published extensively on topics like VHDL-based filter design, low-power amplifiers, and FPGA security. His work includes both theoretical contributions and practical implementations in medical electronics and embedded security systems. Education Background: No explicit education details provided in text Research Interests: Dr. CRĂCIUN specializes in electronic circuit analysis and design, with emphasis on analog systems. His work integrates hardware security (PUF-based solutions), low-power medical electronics, and advanced simulation techniques using VHDL. Recent efforts focus on optimizing power consumption in complex integrated circuits and developing robust FPGA identification methods. Grants & Collaborations: No grant information explicitly stated Labs & Teams: Participates in university-based electronics research groups focused on analog circuit design and embedded systems security.
M. Usman Jamal is a researcher specializing in advanced computational techniques, particularly focusing on High-Level Synthesis (HLS) for Field-Programmable Gate Arrays (FPGAs). His work addresses critical challenges in memory management and design optimization for hardware acceleration. Research Interests High-Level Synthesis (HLS) for hardware acceleration Memory hierarchy optimization in FPGA-based systems Application of machine learning models (e.g., GNNs) for design optimization Software-hardware co-design methodologies Publications & Collaborations Prominent contributions in IEEE Access (2022, 2023) with co-authors like L. Lavagno, M. Lazarescu, and Zhuowei Li Focuses on improving design efficiency through automated caching mechanisms and predictive models
Dr. Zainab Aizaz is a Research Fellow at the School of Engineering and Informatics , University of Sussex, specializing in Artificial Intelligence hardware design and Field Programmable Gate Arrays (FPGA) . Her work focuses on creating energy-efficient and high-performance hardware solutions for AI applications, particularly through innovative pipelined vector processor architectures extending RISC-V open-source frameworks. Her research interests include: Approximate computing for neural networks Hardware acceleration of machine learning Computer architecture optimization Energy-efficient FPGA design Medical imaging applications using AI Signal integrity in nanoscale circuits Dr. Aizaz's recent publications highlight her work on: Spiking neural network acceleration (2025) Approximate multiplier architectures (2022-2024) Hardware-software co-design for edge computing (2023) Cognitive radio antenna optimization (2016) Contact: Z.Aizaz@sussex.ac.uk
Eduard Ayguade Parra is a Full Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Faculty of Computer Science of Barcelona (FIB). He is a leading researcher in the UPC PM - Programming Models group and maintains a significant affiliation with the Barcelona Supercomputing Center (BSC-CNS), a premier national supercomputing facility. His primary research interests lie in High-Performance Computing (HPC) , with a deep focus on parallel and distributed architectures , programming models (especially task-based models like OmpSs), multicore and multiprocessor systems , and compilers for high-performance architectures . His work bridges hardware and software to optimize performance for complex computational problems. The trends in his recent publications highlight a sustained and evolving research program. He is actively advancing task-based programming for distributed memory and hybrid systems, exploring FPGA acceleration for key HPC kernels like SpMV, and innovating in memory system design, including active compute memory and hybrid memory object placement. His research also extends into applying AI techniques to hardware reliability and creating high-quality datasets for computer vision evaluation. HiPEAC Paper Award 2024 Professor Ayguade has been instrumental in securing and leading numerous competitive and non-competitive R&D+i projects, often funded by national and European programs. He has advised a significant number of doctoral students, whose theses cover topics such as task-based programming, FPGA acceleration, HPC compilers, and machine learning for systems. His collaborations are extensive, with frequent co-authorship with prominent figures at UPC and BSC-CNS, such as Jesús Labarta and Mateo Valero. His research has led to advancements in runtime systems, compiler technology, and FPGA-based acceleration. He is a core member of the UPC PM - Programming Models research group and his work is deeply integrated with the resources and mission of the Barcelona Supercomputing Center (BSC-CNS), one of Europe's leading institutions in supercomputing.
Dr Mohammad Hosseinabady is a Lecturer in Computer Science at the School of Computing and Creative Technologies, University of the West of England (UWE Bristol). His research focuses on accelerating computational functions using low-power reconfigurable platforms, particularly embedded FPGAs (Zynq/ZynqMPSoC) and GPUs (NVIDIA Jetson), within the domains of IoT, edge computing, and smart devices. Expertise : FPGA High-Level Synthesis Embedded Systems Smart Devices He leads the BSc(Hons) Computer Science program, emphasizing practical skills in AI, software development, and hardware integration. His work bridges theoretical computer science with real-world applications in robotics and digital industries.
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