Abdullatif Baba is an Assistant Professor in the Department of Computer Engineering at Turkish Aeronautical Association University. His research focuses on interdisciplinary applications of artificial intelligence, robotics, and sustainable energy systems. He develops solutions for smart infrastructure, including AI-driven energy forecasting, autonomous drones for grid maintenance, and hyperspectral imaging techniques. His research interests span: AI/ML Systems : Neural networks, probabilistic forecasting, and FPGA-accelerated deep learning Robotics : Autonomous drones with computer vision for industrial and energy applications Sustainable Energy : Solar-based systems, smart grid optimization, and consumption prediction Security : Chaos-based cryptographic systems for secure communications Recent publications (2020-2024) demonstrate strong trends in practical AI implementations. Key themes include energy forecasting using adaptive neural networks (11 papers), robotics for infrastructure maintenance (4 papers), computer vision for biometrics and imaging (3 papers), and cryptographic systems (2 papers). Methodologies frequently involve hybrid approaches combining ANN, fuzzy logic, and hardware acceleration. No awards, students, or grant details were mentioned in the source material. Research collaborations include institutions in Turkey and the Middle East.
Hugh Greatorex serves as a Researcher at the University of Groningen within the Faculty of Science and Engineering, affiliated with the Bio-inspired Circuits & Systems research group. His work centers on neuromorphic hardware design, specializing in spiking neural networks and event-based processing systems that prioritize energy efficiency and real-world applicability in robotics and embedded AI. His research spans critical areas in neuromorphic engineering: Neuromorphic Engineering Spiking Neural Networks CMOS Circuit Design Memristive Computing Event-based Vision and Audio Processing Symbolic Computation in Neural Hardware Greatorex integrates circuit design with neural algorithms to solve power efficiency challenges, frequently exploring beyond-CMOS technologies like memristors and multi-timescale neural dynamics for practical implementations. Analysis of his 14 recent publications (2022-2025) reveals a cohesive trajectory toward hybrid neuromorphic processors combining CMOS with emerging devices. Key contributions include the TEXEL and Fused-MemBrain architectures for beyond-CMOS integration, alongside advancements in event-based sensory processing and symbolic operations within spiking hardware. This work consistently bridges neural computation with symbolic AI paradigms while emphasizing scalability and robustness. No scientific awards were documented in available sources. Information regarding student advisement and research grants was not provided in the source materials. As a core member of Groningen's Bio-inspired Circuits & Systems group, Greatorex contributes to internationally collaborative projects focused on biologically inspired electronic systems for intelligent processing, with applications spanning edge AI and neural computation modeling.
Phuong Le Yen is a Research Fellow in the School of Engineering at RMIT University, affiliated with the ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS) in the Materials and Fabrication division. Her work focuses on nanotechnology, condensed matter physics, and advanced materials engineering. She holds an ORCID identifier: 0000-0001-8326-6725. Research Interests: Nanomaterials synthesis and characterization Electronic properties of materials at interfaces Development of neuromorphic and sensor technologies X-ray spectroscopic analysis techniques Device physics in semiconductors and amorphous systems Publications Trends: Recent work emphasizes nanostructured materials (e.g., SnO₂, amorphous carbon) applied to neuromorphic computing, sensor systems, and electronic device interfaces. Key themes include material fabrication methods, surface chemistry effects, and energy-efficient electronics. Labs/Teams: Active contributor to the ARC CoE TMOS, focusing on materials innovation for meta-optical systems. Collaborates with interdisciplinary teams across RMIT and international partners.
Baoze Wei is an Associate Professor at Aalborg University's Department of Electric Power Systems and Microgrids, part of the Faculty of Engineering and Science. His research focuses on advanced control strategies for power electronics, microgrid stability, and energy management systems. He leads and collaborates on projects such as the Digital Twin-based Reliability Framework for Aviation Systems and Holistic Optimization of Green Fuel-Powered Microgrids. Key contributions include work on distributed energy systems, fault-tolerant architectures, and predictive maintenance for power electronics. His research emphasizes practical applications in renewable integration, smart grids, and industrial electrification. Wei has supervised one PhD student, Q. He, and contributed to projects funded by entities like Horizon JU and Huawei. Notable collaborations include work on hybrid-electric aircraft systems (HECATE) and advanced control algorithms for distributed converters. His research spans technical areas such as voltage source inverters, uninterruptible power systems (UPS), and DC shipboard microgrids. His publications reflect expertise in model predictive control, energy trading strategies, and condition monitoring. Current research trends include data-driven lifetime prediction for power electronics components and optimization of multi-energy systems. He actively participates in international conferences, contributing to both theoretical advancements and real-world system implementations.
Dr. Thomas Reitmaier is a Lecturer in Computer Science at Swansea University's Faculty of Science and Engineering. He is part of the Department of Computer Science and holds an ORCID identifier: https://orcid.org/0000-0003-2078-6699 . His research focuses on Human-Computer Interaction (HCI), particularly in under-served communities. Key areas include speech technologies for low-resource languages, IoT applications for aging populations, and co-design methodologies with global south communities. He has supervised a current PhD student on water systems reinforcement learning at Dwr Cymru (Welsh Water), collaborating with Dr. Sara Sharifzadeh. Teaching includes advanced object-oriented programming (CSC371), covering C/C++ and foundational programming concepts. His work bridges technical innovation with ethical considerations, emphasizing community engagement and equitable technology access. Recent research highlights include developing self-powered smart materials for slum communities, rethinking public touch interfaces during pandemics, and advancing speech recognition for under-heard languages. His interdisciplinary approach integrates computer science with sociological and anthropological insights.
Longzhi Yang is a Professor at Northumbria University’s Department of Computer and Information Sciences, currently serving as Director of Education. He holds a PhD in Computing Science from Aberystwyth University (2011) and has extensive research experience in Artificial Intelligence, Robotics, Cybersecurity, and Manufacturing. His work emphasizes real-world applications under uncertain environments, supported by grants from EPSRC, Innovate UK, and industry partners. Research interests include Computational Intelligence, Machine Learning, Cybersecurity, Robotics, Planning/Scheduling, and Additive Manufacturing. Recent projects focus on energy storage systems, fuzzy logic control, and data-driven robotic systems. He has organized major conferences like the UK Workshop on Computational Intelligence and the IEEE ICMLA. Publications span topics such as reinforcement learning for EVs, fuzzy neural networks, and cybersecurity frameworks. Awards include the Best Paper Award at UKCI 2018 and 2016. He actively supervises PhD students and leads interdisciplinary initiatives like the COVER project for emergency response drones and 3D-printed furniture manufacturing. Education: PhD in Computing Science (Aberystwyth University, 2011) Funding: EPSRC, ESRC, Innovate UK, RAEng Grants: 4 active/funded projects including smart manufacturing and emergency response systems Labs/Teams: Involved in robotics, cybersecurity, and intelligent manufacturing research groups
Quinton Deeley is a Senior Lecturer in Social Behaviour and Neurodevelopment at King’s College London’s Institute of Psychiatry, Psychology, and Neuroscience (IOPPN). He is also a Consultant Neuropsychiatrist at the National Autism Unit and Neuropsychiatry Brain Injury Clinic at Maudsley and Bethlem Hospitals. His work bridges cognitive neuroscience with humanities scholarship, focusing on religious cognition, belief formation, and neurodevelopmental disorders like autism and ADHD. Deeley chairs the Cultural and Social Neuroscience Research Group and Maudsley Philosophy Group. He teaches undergraduate and postgraduate courses in clinical neurodevelopmental sciences, forensic mental health, and neuroimaging. Education: Theology and Religious Studies at University of Cambridge Medicine at Guys and St Thomas’ Medical School Psychiatry training at Maudsley and Bethlem Hospitals Research Interests: His work explores the intersection of culture, cognition, and brain function. Key topics include: Altered self-experience in religious vs. psychopathological contexts Neurochemical bases of suggestion and hypnosis Social cognition in neurodevelopmental disorders Functional neurological symptoms and dissociation Publications & Grants: Over 60 peer-reviewed articles (e.g., on dopamine’s role in social inferences, fMRI modeling of cultural phenomena). Active in projects like “The Power of Belief” and “Virtual Reality Oracle” exploring suggestibility and cultural cognition. Labs/Teams: Leads the UNIQUE Research Group studying persistent psychotic experiences, and collaborates with interdisciplinary teams across anthropology, religious studies, and neuroscience.
Ko-Le Chen is a Researcher at Northumbria University , with a focus on interdisciplinary work at the intersection of Human-Computer Interaction , Participatory Design , and Emotional Labor . Their academic contributions span transcultural support systems, creative economy initiatives, and critical frameworks for inclusive design practices. Research Interests : Chen's work examines the emotional and affective dimensions of participatory design, with projects addressing gender support systems ( Kharm Unn Jai ), place-based creative economies, and ethical challenges in field research. They investigate how design can mediate interpersonal relationships, as seen in the Lovers' Box project (2011), and advocate for disability rights through policy analysis (2016). Key themes include transcultural collaboration, respectful hierarchies in design, and the materiality of knowledge dissemination practices. Scientific Trends : Chen's publications from 2011-2025 reveal a trajectory from intimate relationship technologies to broader societal impacts. Their work bridges technical innovation (e.g., affective labor in PDC conferences) with critical social theory (disability law, feminist biology critiques) and practical applications (creative economy insights). Notable Collaborations : Co-authorships with J. Yee, Y. Akama, and others across international institutions in the UK, Thailand, and Australia highlight their global interdisciplinary network.
Sophie Fosson is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, Italy. She is a member of the System Identification & Control (SIC) research group and teaches courses such as Automatic Control and Modeling and Control of Cyberphysical Systems at both the master's and doctoral levels. Education: PhD in Mathematics for Industrial Technologies, Scuola Normale Superiore di Pisa Master’s in Mathematical Engineering, Politecnico di Torino Her research focuses on sparse optimization, machine learning, system identification, and control theory . She integrates mathematical modeling with practical applications in cyber-physical systems and neural networks. Her work emphasizes efficient and robust optimization techniques for deep learning and control systems. The recent publications highlight a strong trend in optimization of neural networks , particularly through sparse training, binary and ternary quantization, self-supervised learning, and fault tolerance . Her research bridges control theory with machine learning, applying mathematical rigor to modern AI challenges in safety-critical and embedded environments. Scientific Awards: No awards explicitly mentioned. She supervises several PhD students including Alice Re, Rosario Milazzo, and Simone Pirrera , and has been involved in research projects such as CRISP and collaborations with Centre Tecnològic de Telecomunicacions de Catalunya . She has held postdoctoral positions at DISMA and DET (Politecnico di Torino) and worked as a researcher at Istituto Superiore Mario Boella. Laboratories and Research Groups: System Identification & Control (SIC), DAUIN, Politecnico di Torin
Professor Patrick Langdon serves as Director of the Transport Research Institute at Edinburgh Napier University's School of Computing Engineering and the Built Environment (SEBE). He holds the position of Professor of Engineering Design, Transportation, and Inclusion and leads Ethics and Integrity initiatives for both SEBE and the School of Computing. With over 20 years of experience as an Experimental Psychologist, Langdon has established himself as a leading researcher at the intersection of cognitive science, artificial intelligence, and transportation engineering. Langdon's research spans multiple interconnected domains focused on human-centered design in transportation systems. His work centers on inclusive design principles for autonomous vehicle interfaces, human-machine interaction in complex environments, and the application of cognitive science to transportation challenges. Key research themes include designing for diverse user populations, predicting driver intent using Bayesian methods, enhancing situation awareness in multitasking scenarios, and developing ethical frameworks for human participation in technology research. His multidisciplinary approach bridges engineering, psychology, and computer science to address real-world transportation challenges. Analysis of Langdon's publication record reveals a consistent focus on human factors in automated transportation systems, with increasing emphasis on sustainable and inclusive mobility solutions. His work shows progression from foundational research in inclusive design to cutting-edge applications in autonomous vehicles and hydrogen-electric aviation. The publications demonstrate strong methodological diversity, incorporating experimental studies, theoretical frameworks, and real-world implementation projects that address both technical and social dimensions of transportation technology adoption. Langdon's research activities are supported by significant funding from diverse sources including Innovate UK, Engineering and Physical Sciences Research Council, European Commission, and industry partners. His leadership extends to major collaborative projects such as the HEART (Hydrogen Electric and Automated Regional Transportation) initiative, CAVForth automated bus service expansion, and research on inclusive kerb design for urban environments. These projects demonstrate his ability to translate theoretical research into practical transportation solutions with real-world impact. As Director of the Transport Research Institute, Langdon oversees a multidisciplinary team working at the forefront of transportation innovation. His research group focuses on the integration of cognitive science, artificial intelligence, and engineering design to create transportation systems that accommodate diverse user needs while advancing sustainable mobility solutions. The institute serves as a hub for collaboration between academia, industry, and government agencies working to shape the future of transportation.
Tudor Cioara is a Professor at the Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University in Romania. With over 100 publications spanning from 2008 to 2025, his research establishes him as a leading academic in the intersection of computer science and energy systems. His research interests focus on blockchain technology applications in energy systems, smart grid optimization, distributed computing, and AI-driven energy management. Cioara's work bridges theoretical computer science with practical energy applications, developing novel approaches for peer-to-peer energy trading, virtual power plants, and data center energy optimization. His recent work has expanded into federated learning, edge computing, and large language models applied to energy systems. Cioara's publication portfolio shows a consistent trajectory of increasing impact, with significant contributions in 2023-2025 focusing on AI-driven energy solutions, blockchain applications, and healthcare technology integration. His work demonstrates strong interdisciplinary connections between computer science, electrical engineering, and healthcare domains. He has received recognition through numerous collaborations with international researchers and institutions, particularly in European energy and computing projects. His work on blockchain-based energy trading systems and AI optimization for smart grids represents some of the most influential contributions in his field. Cioara leads a research group that has produced significant work on data center optimization, energy flexibility management, and digital twin applications for energy systems. His team has developed innovative approaches for integrating renewable energy sources, managing electric vehicle charging, and creating secure energy trading platforms using blockchain technology.
Xinyu Qin is a Professor at the Department of Electrical and Computer Engineering within the School of Information Engineering at Guangdong University of Technology. His research focuses on advanced robotics, signal processing, and integrated circuit design, contributing to fields like multi-manipulator systems and Delta-Sigma modulators. Education: Affiliated with prestigious institutions through collaborative research Research Interests: Robotics, Machine Learning, Electrical Engineering His recent publications (2023-2025) demonstrate expertise in robotic task allocation, high-speed circuit design, and explainable AI for healthcare. Award-winning work includes Interactive Explainable Deep Survival Analysis (2024) and SVP: Safe and Efficient Speculative Execution Mechanism through Value Prediction (2023). Key collaborations involve Guoxing Wang and Liang Qi across 16 records. Current projects involve optimizing convolutional neural network accelerators, analyzing atmospheric river impacts on Greenland's crustal deformation, and advancing MASH Delta-Sigma modulator architectures. His work bridges theoretical innovation with practical applications in smart energy systems and autonomous robotics.
Damir Malnar is a Senior Lecturer at Veleri College (Veleri-OI), specializing in Telecommunications and Electronics. He teaches courses such as Automation in Building Construction, Mobile Communication, Embedded Computer Systems, and IoT-related disciplines. His research focuses on advanced signal processing techniques, hydroacoustic signal analysis, FPGA-embedded systems, and real-time algorithm implementation. He has contributed to the development of the Veleri-OI IoT School's educational platforms and pioneered methods for optimizing time-frequency distributions in noisy environments. Malnar's academic work emphasizes practical applications of theoretical concepts, including FPGA-based genetic algorithms for antenna array recovery and real-time embedded systems. His recent efforts integrate IoT technologies into educational tools, exemplified by the Veleri-OI Meteo System. He maintains active involvement in both theoretical research and hands-on engineering projects, bridging the gap between academic instruction and industry-relevant solutions. Key technical areas include signal decomposition, noise analysis, and adaptive systems. His work often intersects with marine acoustics and environmental monitoring, reflecting a multidisciplinary approach to telecommunications engineering.
Carlos Andrés Pena is a Professor and Director of the Institute of Information and Communication Technologies (IICT) at HEIG-VD. His research focuses on Machine Learning, Artificial Intelligence, and Bioinformatics, with applications in healthcare, environmental science, and computational biology. He leads projects like ImpTox and EXPLaiN, addressing challenges in nanotoxicology, phage therapy, and ethical AI. His work bridges AI with real-world problems, including antimicrobial resistance and soil health monitoring. Education: Not explicitly listed in the provided text. Research Interests: Pena’s research integrates AI with interdisciplinary domains. Key areas include: Machine learning for healthcare diagnostics (e.g., diabetic retinopathy, perinatal health) Phage genome engineering using deep learning to combat antibiotic resistance Protist bioindicators for soil quality assessment using metabarcoding Fuzzy logic systems for biomarker discovery and disease classification Grants & Projects: He has secured over CHF 1M in funding, including EU Horizon 2020 projects and Swiss National Science Foundation grants. Notable projects include: ImpTox (2021–2025): Investigating nanoplastics’ toxicity on allergies PERPHECT (2019–2021): Engineering phages via LSTM networks HES-XPLAIN (2023–2024): Open platform for explainable AI Labs/Teams: Directs the IICT lab, collaborating with institutions like CNRS and University of Fribourg on bioinformatics and environmental studies.
Prof. Levent EREN is a Professor and Vice Rector at İzmir University of Economics, leading the Faculty of Engineering's Electrical and Electronics Engineering department. He holds a Ph.D. from the University of Missouri (2002) and has extensive experience in academia and industry, including roles as Vice Dean (2014–2019) and faculty member at Bahçeşehir University (2003–2012). His research focuses on motor fault diagnosis, power quality, and digital signal processing, with over 30 journal and conference publications. Prof. EREN’s work emphasizes innovative applications of neural networks and wavelet transforms in machinery condition monitoring. He has contributed to EU-funded projects like Future Education and Training in Computing (2013–2014). His administrative roles include managing institutional research and academic policies as Vice Rector since 2019. His teaching spans courses on electrical energy conversion, digital signal processing, and senior engineering projects. He has no explicitly listed awards but is recognized for his technical contributions in motor current signature analysis and bearing fault detection.
