Richard Nock is a Senior Lecturer at Aston University specializing in precision timing instrumentation and quantum technologies. His research develops FPGA-based photon counting systems for quantum key distribution and LiDAR applications. Recent innovations include high-precision time-to-digital converters with reduced dead-time and real-time photon counting correlators. His work supports atmospheric CO2 monitoring through advanced LIDAR designs. Royal Academy of Engineering Enterprise Fellowship Staff-Student Partnership Academic Award As Final Year Tutor, he coordinates electronics programs and supervises projects in IoT and embedded systems.
William Marnane is a Professor of Electrical and Electronic Engineering at University College Cork (UCC). He holds a B.E. from UCC (1984) and a D.Phil. from the University of Oxford (1989). His career includes roles as Lecturer (1989), Senior Lecturer (1999), Dean of Graduate Studies (2013–2016), and Head of the School of Engineering (2016–2019). He has led major research initiatives, including the SFI-funded INFANT Centre and the Claude Shannon Institute. His research focuses on biomedical signal processing, machine learning, and neonatal EEG analysis, with notable contributions to neonatal seizure detection algorithms and fetal health monitoring. He has been awarded the Giner de Los Ríos Visiting Research Fellowship (2007, 2020) and leads projects funded by Wellcome Trust, Science Foundation Ireland, and EU grants. His educational background includes significant contributions to curriculum development and graduate training in engineering and biomedical sciences. He has supervised numerous research projects, including advancements in cryptographic hardware, embedded systems, and low-power signal processing architectures. Research highlights include the ANSeR neonatal seizure detection algorithm (Wellcome Trust-funded), clinical trials on EEG-based seizure recognition, and development of secure TLS coprocessors. He has published over 200 peer-reviewed articles, with key contributions in neonatal EEG analysis, machine learning applications in healthcare, and cryptographic processor design. Current research explores AI for fetal heart rate monitoring and wearable health technologies. Grants and partnerships include leadership roles in EU-funded projects, SFI Strategic Research Clusters, and industry collaborations. He co-directs the INFANT Centre, bridging engineering, medicine, and data science for translational research in neonatal care.
Dylan Rankin is an Assistant Professor in the Department of Physics and Astronomy at the University of Pennsylvania’s School of Arts & Sciences. His research focuses on particle physics experiments at the Large Hadron Collider (LHC), leveraging machine learning (ML) for data analysis and optimizing high-speed trigger systems. He is a key contributor to the FastML collaboration, advancing FPGA-based ML inference for low-latency applications in physics and astronomy. Education: Ph.D. in Physics from Boston University (2018), Sc.B. in Physics from MIT (2012). Research Interests: Probing the Standard Model through LHC proton-proton collision data Machine learning applications in jet classification, mass regression, and event reconstruction Optimizing trigger systems for real-time data selection at the LHC Hardware acceleration (FPGA/GPU) for scientific computing challenges Recent work emphasizes ML deployment in latency-constrained environments, including gravitational wave astronomy and FPGA-as-a-service frameworks. His collaborative projects include hls4ml for low-latency inference and AIgean for heterogeneous cluster ML workflows. Advising/Grants: Active in training next-generation researchers in ML-driven particle physics methodologies. Involved in multi-institutional initiatives for computational infrastructure development. Labs/Teams: Core member of the FastML collaboration, leading FPGA-based ML solutions for physics experiments. Associated with the Penn High Energy Physics group.
Georgia Karagiorgi is an Associate Professor of Physics at Columbia University , affiliated with the Faculty of Arts and Sciences. Her research focuses on experimental particle physics, particularly neutrino experiments and high data rate processing. Ph.D. in Experimental High Energy Physics from MIT (2010) Technical Lead for DUNE Experiment's data acquisition system Research Interests: Searches for new physics in the neutrino sector Design of data processing hardware for large-scale experiments Machine learning on field-programmable gate arrays (FPGAs) Scientific Recognition: NSF CAREER Award (2018) APS Mitsuyoshi Tanaka Thesis Prize (2012) MIT Martin Deutsch Award (2010)
Abdellah Touhafi is a Professor at the Faculty of Engineering Technology, Department of Electronics and Informatics at Vrije Universiteit Brussel (VUB) in Brussels, Belgium. With an extensive research portfolio spanning nearly three decades, his work focuses on embedded systems, sensor networks, and FPGA technologies with applications in environmental monitoring and smart cities. His current research activities include leading multiple projects related to low-carbon technologies, health technologies, and sustainable sensing systems. Dr. Touhafi's research interests center around Field Programmable Gate Arrays (FPGA), wireless sensor networks, acoustic sensing, and machine learning applications for environmental monitoring. His work bridges hardware engineering with practical applications in smart city infrastructure, water quality monitoring, and sustainable sensing technologies. He has developed innovative approaches for hardware-assisted security mechanisms in environmental monitoring systems and has explored the integration of triboelectric sensors for self-powered sensing applications. His publication record shows consistent output with 154 research outputs, including recent articles in Sensors journal and conference papers at IEEE events. His h-index of 19 (with 1,433 citations) reflects significant impact in his fields of expertise. Current projects include DESTINY (Low-carbon solutions), GEAR (future health technologies), and ILSF 2024 (acoustic mapping). NSIS3: DESTINY: Low-carbon solutions and technology for a new future (2024-2029) OZR4208: Bilateral cooperation for joint PhD VUB-USMBA (2023-2027) IOF3016: GEAR: Future health technologies (2021-2025) BRGEOZ445: ILSF 2024 - This is the Sound of "ME" (2024) IOFACC12: Tech4Health (2024-2025) Dr. Touhafi actively supervises students and has served on PhD committees, including for projects related to sustainable public lighting and environmental monitoring systems. His research group has produced datasets like the AMIVU Acoustic Map Imaging VUB-ULB Dataset, demonstrating practical applications of his theoretical work. He regularly participates in conferences including IEEE events and has organized workshops on industrial electronics.
Dr. István László Oniga is an Associate Professor at the University of Debrecen , affiliated with the Faculty of Informatics and the Department of Information Systems and Networks . His research focuses on intelligent embedded systems , neural network implementations , and eHealth/ambient assisted living technologies. Research Areas : Embedded systems design, FPGA-based neural networks, eHealth systems, ambient assisted living, assistive robotics. Students : Levente Philipp, Ferenc Héjja, Laura Juhasz, Peter Polgar, Korteby Mohamed Amine Talbi, Djamila Xie Yu. Email : oniga.istvan@inf.unideb.hu Recent publications highlight his work in AI-powered cyber-physical systems , EEG signal processing , and real-time activity recognition using wearable sensors. His research integrates deep learning , FPGA hardware acceleration , and machine learning frameworks to advance healthcare and robotics applications. University Infrastructure : The Faculty of Informatics at the University of Debrecen emphasizes research in real-time communication, sensor networks, and distributed systems, aligning with Dr. Oniga’s expertise in embedded systems and machine learning.
Dr. Arindam Mukherjee serves as an Associate Professor in the Department of Electrical and Computer Engineering at the University of North Carolina at Charlotte, College of Engineering. His office is located in EPIC 2336, and he can be reached at amukherj@charlotte.edu or by phone at 704-687-8417. His educational qualifications are as follows: Ph.D. from the University of California at Santa Barbara (2002) M.S. from the University of California at Santa Barbara (2000) B.Tech. from Jadavpur University, India (1996) Dr. Mukherjee's research spans Smart System Architectures, Internet of Things (IoT), and Fog Computing. He investigates cooperative and autonomous mobile systems, real-time system software, and database management for Big Data. His work includes scheduling algorithms for IoT systems and the integration of edge, fog, and cloud computing paradigms for real-time applications in the Big-Squared Data space. His publication record from 2003 to 2018 shows an evolution from VLSI design and bioinformatics to contemporary IoT and fog computing. Early work focused on logic synthesis, biochip testing, and FPGA-based bioinformatics implementations, while recent contributions address power management in heterogeneous processors, energy-efficient communications for smart buildings, and the synergistic integration of edge, fog, and cloud computing for real-time IoT data processing. No scientific awards are mentioned in the provided text. There is no information available regarding students advised or research grants. Similarly, no specific research labs or collaborative teams are described in the source material.
Prof. Dr. Sıddıka Berna Örs Yalçın is a Professor at Istanbul Technical University , Faculty of Electrical and Electronics Engineering, Department of Electronics and Communication Engineering since 2020. She holds a PhD from Katholieke Universiteit Leuven (1999) and has maintained continuous academic engagement in cryptology, embedded systems, and hardware security. Education: PhD in Electrical Engineering, Katholieke Universiteit Leuven (1999) Master's in Electronics and Communications Engineering, Istanbul Technical University (1995) Licence in Electronics and Communication Engineering, Istanbul Technical University (1995) Her research focuses on post-quantum cryptography , side-channel attack mitigation , and hardware implementation for security systems. Recent work includes FPGA-based AI accelerators, IoT power consumption modeling, and quantum-resistant cryptographic algorithms. Current projects center on RISC-V processor optimization, embedded security, and hardware implementations for 5G communications. Her Scopus h-index is 17 with over 1353 citations, reflecting her impact in computer hardware and cryptographic engineering domains. Notable Research Outputs: Quantum tent map-based S-box designs for image encryption (2024) Post-quantum modular multiplication algorithms (2024) RISC-V implementation of CRYSTALS-Kyber (2023) IOT power consumption estimation frameworks (2024)
Julian Hoever, M.Sc., is a research associate in the Department of Intelligent Embedded Systems at the University of Duisburg-Essen's Faculty of Computer Science since June 2024. His work bridges academic research and industrial application through projects like ZaKI.D, focusing on AI accessibility for regional companies. Bachelor's in Applied Computer Science, University of Duisburg-Essen Master's in Cyber-Physical Systems, University of Duisburg-Essen (2024) His research centers on knowledge distillation and precomputable neural networks for FPGAs , enabling efficient AI models. This aligns with the ZaKI.D project 's goal of transferring AI expertise to industry via small-scale initiatives and training. Julian's recent publication in Sensors explores configurable soft sensors for fluid flow estimation, highlighting his expertise in adaptive embedded systems. Projects like IoT Garage and Elastic AI further demonstrate his focus on practical AI implementation in resource-constrained environments.
Alex Hanneman is a Doctoral Researcher at Loughborough University. He completed his MEng in Electronic and Computer Systems Engineering at the same institution from 2015 to 2020, followed by a placement year (2017-2018) at Leonardo's Airborne and Space Systems division as a Firmware Engineering intern, where he developed FPGAs for mission-critical applications. He later returned for a summer placement. Academic Rank: Researcher University: Loughborough University His research interests include Firmware Engineering, Field-Programmable Gate Arrays (FPGA), Electronic Engineering, and Computer Systems Engineering, reflecting his technical background and professional experience.
Dr. Chang Liu is a Senior Lecturer in Electronic Engineering at the University of Edinburgh's School of Engineering. He received his B.Sc. in Automation from Tianjin University (2010) and Ph.D. in Testing, Measurement Technology and Instrument from Beihang University (2016). Following postdoctoral research at Empa-Swiss Federal Laboratories, he joined the Agile Tomography Group at Edinburgh. His research focuses on laser spectroscopy, laser imaging, and data-driven imaging techniques for applications in reacting flow-field diagnostics and environmental monitoring. Key specialties include design of near/mid-infrared LAS sensing systems, development of high-sensitivity imaging methodologies, spectroscopic modeling, inverse problem solving, and embedded system design. His publications demonstrate a consistent focus on advancing tomographic imaging techniques, with recent work emphasizing machine learning integration, hardware acceleration, and industrial applications in aero-engine monitoring. Research consistently addresses challenges in spatial/temporal resolution enhancement and real-time system implementation. Dr. Liu teaches courses in Digital System Design, Analogue Circuits, and Embedded Systems. He leads multiple research projects including EPSRC-funded initiatives on laser imaging of turbine engine combustion species. His team collaborates with industrial partners to develop cutting-edge laser-based sensing solutions.
Christos Kozyrakis is a Professor in the Departments of Electrical Engineering and Computer Science at Stanford University. His research focuses on computer architecture, computer systems, and cloud computing, with contributions to energy-efficient systems, machine learning infrastructure, and database-oriented operating systems. He leads the MAST research group and directs the Stanford Platform Lab. Education: Bachelor's Degree in Computer Science, University of Crete PhD in Electrical Engineering and Computer Sciences, University of California, Berkeley Research Interests: Kozyrakis explores cloud management, machine learning systems, energy-efficient architectures, and hardware-software co-design. Notable projects include DBOS (a database-oriented OS), the MAST group's work on serverless computing and storage systems, and energy-proportional data center design. Awards: ACM SIGARCH Maurice Wilkes Award ISCA Influential Paper Award NSF Career Award Okawa Foundation Research Grant ACM and IEEE Fellowships Advising & Grants: Supervised over 20 PhD and master's students, with notable alumni contributing to academia and industry. Supported by NSF, DARPA, SRC, Google, Microsoft, and other industry collaborations. Labs & Teams: Leads the MAST research group and directs the Stanford Platform Lab, focusing on cutting-edge systems research and prototyping.
Vijay Krishna is a Distinguished Professor of Economics at Penn State University and serves as the Job Market Placement Director in the Department of Economics. His academic career includes a Ph.D. in Economics from Princeton University (1983), an M.A. in Economics from Delhi University (1978), and a B.A. in Mathematics from Delhi University (1976). His research focuses on Economic Theory and Industrial Organization , with recent interdisciplinary contributions in electronics and computer engineering. Notable areas include ferroelectric devices, memory storage technologies, and hardware security. He has also explored neural network architectures and energy-efficient computing systems. Recent publications (2023–2025) highlight innovations in cryogenic logic gates, nonvolatile memory systems, and hybrid neural networks. His work often bridges theoretical economics with applied engineering, reflecting cross-disciplinary collaboration. Awards and grants are not explicitly listed in the provided materials, but his extensive publication record underscores sustained academic engagement. Professor Krishna advises on job market placements for economics PhD students and maintains an active role in academic administration. His research group explores cutting-edge technologies in both traditional economic theory and emerging computational fields.
Jakob Lechner is an External Lecturer in the Computer Engineering department at Vienna University of Technology. His work focuses on fault-tolerant digital circuit design, asynchronous systems, and robust hardware architectures. Education : PhD in Computer Engineering (2014), Diploma in Computer Engineering (2008) Research interests include: Fault-tolerant computing Asynchronous circuit design Transient error mitigation in FPGAs Triple Modular Redundancy (TMR) Metastability containment Single Event Transients (SET) resilience His publications (2006–2018) emphasize fault injection techniques, robust GALS circuits, and asynchronous communication protocols. Key projects include Intel CARS (2017–2019) on delay-insensitive codes. Supervision : Guided K. D. Pados' thesis on AXI4 bus systems (2013).
Dr. Antisthenis Tsompanas is a Senior Lecturer in Computer Science at the University of the West of England (UWE), part of the Faculty of Engineering and the Environment (FET). His research focuses on unconventional and bio-inspired computing, with expertise in modeling biological processes, cellular automata, and memristive systems. He holds degrees in Electrical and Computer Engineering from Democritus University of Thrace, Greece. Research interests span unconventional computing, bio-inspired algorithms, electronic systems design, and applications of cellular automata in computing and engineering. His work includes projects on fungal-inspired computing, neuroevolution-based medical device design, and FPGA implementations of biological models. Publications highlight trends in mycelium-based systems, memristive oscillators, and neuroevolution techniques applied to soft robotics and medical devices. Collaborative projects include EU-funded initiatives and interdisciplinary research in biomimetic engineering. He is a member of the Technical Chamber of Greece since 2009. Current contributions include advancing biohybrid actuators, developing models for engineered living materials, and exploring computational universality in biological systems.
