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.
Patrick Irvin is a Research Professor in the Department of Physics & Astronomy at the University of Pittsburgh. He specializes in low-temperature transport phenomena, photonic measurements in semiconductor and oxide nanostructures, and microwave spectroscopy of ferroelectric thin films. His work focuses on nanoscale phenomena in oxide heterostructures, particularly at interfaces like LaAlO3/SrTiO3, exploring emergent electronic phases such as ferromagnetism and superconductivity. His research integrates advanced fabrication techniques with high-resolution spectroscopic and imaging methods, including cryogenic piezoresponse force microscopy. Key research interests include reconfigurable nanoelectronic devices, strain effects on superconductivity, and quantum transport in one-dimensional systems. He has contributed to seminal studies on ferroelectricity in strained SrTiO3 and nanoscale oxide photodetectors, published in top journals like Nature and Nature Photonics . His recent work explores long-range electron interactions, tunable magnetism in graphene-based systems, and integration of oxide heterostructures with silicon platforms. He collaborates extensively on projects involving van der Waals heterostructures, THz spectroscopy, and nanoscale control of electronic phases. Publications highlight advancements in understanding correlated electron systems, including electron pairing without superconductivity and non-Coulombic coupling phenomena. His lab develops novel devices such as superconducting quantum interference devices and programmable ferroelectric nanostructures, emphasizing practical applications in quantum technologies and optoelectronics.
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.
Milan Stojanovic serves as Associate Professor in Columbia University's Department of Biomedical Engineering within the Fu Foundation School of Engineering and Applied Science, with dual affiliation in the Department of Systems Biology. He concurrently holds the position of Associate Director in the Division of Clinical Pharmacology & Experimental Therapeutics. His pioneering research focuses on engineering self-operating molecular automata capable of information processing and therapeutic response, high-resolution sensor arrays for bodily fluid analysis, and molecular systems exhibiting programmed walking and self-organization through local interactions. This work bridges nanotechnology, synthetic biology, and clinical therapeutics. His publication record demonstrates consistent innovation in DNA-based molecular computation and biosensing, with key contributions spanning molecular robotics (2010), evolutionary sensor optimization (2012), and breakthrough Debye-length overcoming detection methods (2018). Recent work advances intradermal health monitoring through hydrogel microfilaments (2019). National Institute of Diabetes and Digestive and Kidney Diseases grant recipient His research program integrates biochemical engineering with computational principles to develop autonomous molecular systems for diagnostic and therapeutic applications, maintaining strong connections between fundamental nanotechnology and clinical translation through Columbia's medical and engineering infrastructure.
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)
Liam Mc Daid is a Professor of Computational Neuroscience at the School of Computing, Engineering and Intelligent Systems, Ulster University. He serves as Research Director for Computing, Engineering and Intelligent Systems within the Faculty of Computing, Engineering and Built Environment. His research spans computational neuroscience, neural networks, and biomedical engineering, with a focus on hardware implementations and fault detection in systems like RISC-V. He contributes to UN Sustainable Development Goals related to health and technology. Recent publications highlight his work on spiking neural networks for fault detection, deep learning in medical imaging, and biotech assays for cardiac diagnostics. His research has been cited over 1,600 times, with an h-index of 23. Scientific awards include the 2011 Adaptive Routing Strategies Prize, the 2010 Northern Ireland Science Park 25K Biotechnology Award, and InventNI’s 2019 Life and Health Startup of the Year. His work has been featured in media outlets addressing asthma management and health tech innovation.
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.
Bogdan Burlacu serves as R&D-Headquarters at the Center of Excellence for Smart Production HEAL at University of Applied Sciences Hagenberg. With an ORCID identifier 0000-0001-8785-2959 and h-index of 10 (619 citations), he maintains active research leadership through 2025. His research focuses on Symbolic Regression and Genetic Programming, with significant contributions to Multiobjective Optimization and Benchmark Problems. Key application areas include Explainable AI systems, hardware acceleration for evolutionary algorithms, and astrophysical modeling. His work demonstrates strong interdisciplinary connections between computer science and physical sciences. Recent publication trends show increasing focus on interpretability frameworks and domain-expert validation in symbolic regression, with notable applications in cosmology and engineering systems. His 2025 publications emphasize practical benchmarking methodologies and hardware acceleration techniques. Burlacu actively supervises research through two documented supervised works and contributes to major collaborative projects. He leads research activities within the Center of Excellence for Smart Production HEAL and participates in the Josef Ressel Center for Symbolic Regression. His work integrates distributed intelligence systems with rapid prototyping methodologies for industrial applications.
Aydın Tarık Zengin is an Assistant Professor at Istanbul Technical University , Department of Electrical Engineering , since 2022. He previously held administrative and academic roles (Head of Department, Deputy Director) at Istanbul Sabahattin Zaim University from 2015 to 2022. He earned his PhD from Kumamoto University (2010-2013) and has an educational background in Electrical Engineering. His research focuses on Robotics , Autonomous Systems , and Embedded Systems , with notable work in: Power Line Inspection Robots (ROSETLineBot, temperature mapping, sagging measurement) Autonomous Vehicle Platoons (control algorithms, stability under disturbances) AI/ML Applications (error correction codes, lane detection, wind forecasting) Human Skin Pain Modeling and Vibrotactile Feedback Systems He has received awards including the Young Author Award (2013) and Best Student Paper Award (2012) . His 15 most recent articles emphasize AI integration in power systems, autonomous vehicle control, and environmental monitoring. Current projects include the BAP-funded Multi-Channel Power Supply for Nuclear Electronics (2023-2025). He is an IEEE Member (2019-2020) and participates in academic governance as a board member.
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.
