Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Brain-Computer Interfaces
• +5 more

Naomi Du Bois is a Research Associate in Artificial Intelligence and Machine Learning at the Bath Institute for the Augmented Human, University of Bath, affiliated with the Department of Computer Science under the Faculty of Science. Her work bridges AI, neuroscience, and clinical applications to support human augmentation and neurorehabilitation. Research Interests: Her primary research focuses on developing objective neurophysiological markers using AI and machine learning to assess prolonged disorders of consciousness (PDoC). This includes analyzing brain signals such as auditory evoked potentials to differentiate wakefulness and awareness in patients with locked-in syndrome and related conditions. Her work lies at the intersection of biomedical signal processing, cognitive neuroscience, and intelligent systems. The available publication highlights a trend toward applying machine learning techniques to clinical neurophysiology, particularly for improving diagnostic accuracy in non-communicative patients. Her research contributes to the growing field of assistive and augmentative technologies for individuals with severe neurological impairments. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no information available regarding student supervision, grants, or funding roles in the provided content. Labs and Teams: Naomi Du Bois is a member of the Bath Institute for the Augmented Human, a multidisciplinary research center focused on enhancing human capabilities through technology, particularly in health and well-being contexts. The institute emphasizes wearable systems, sensory augmentation, and brain-computer interfaces.

Szegletes LucaView profile

Senior Lecturer

• Machine Learning
• Neuroscience
• Biofeedback Systems
• +4 more

Szegletes Luca is a Senior Lecturer at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics as part of the Applied Mobile Research Group (AMORG). Her research spans multiple interdisciplinary domains, focusing primarily on integrating machine learning techniques with biofeedback systems. Her work demonstrates expertise in diffusion probabilistic models for EEG signal processing, deep learning applications in embedded systems, and reinforcement learning frameworks. She has pioneered methods for physiological data analysis, including blood pressure measurement and face anonymization in low-power environments. Notable research contributions include adaptive educational game frameworks, cognitive profiling systems, and biofeedback-driven difficulty control mechanisms. Her publications reflect a strong focus on bridging computational methods with cognitive science and biomedical applications.

József SÜTŐView profile

Associate Professor

• Machine Learning
• Digital Signal Processing
• Microcontroller Applications
• +3 more

Dr. József SÜTŐ is an Associate Professor at the University of Debrecen's Faculty of Informatics, Department of IT Systems and Networks. His research focuses on machine learning, digital signal processing, and microcontroller applications for agricultural and biomedical systems. University of Debrecen, Faculty of Informatics Department of IT Systems and Networks Research areas combine computer science with environmental and health monitoring, including: Deep learning for insect pest management Edge computing in embedded systems Human activity recognition algorithms Computer vision for traffic and plant analysis Recent publications emphasize real-time AI deployment on low-power hardware, such as Raspberry Pi and microcontrollers, with applications in agricultural monitoring and wearable devices. He also investigates comparative model efficiency between shallow and deep learning architectures.

• Embedded Systems
• Neural Networks
• Machine Learning
• +3 more

Tamás Majoros is an Assistant Professor at the Faculty of Informatics, University of Debrecen , specializing in Embedded Systems and Neural Networks . His work bridges hardware design and biomedical applications. Primary Affiliation: Faculty of Informatics, University of Debrecen Academic Rank: Assistant Professor Major research areas include: Embedded systems for real-time data processing Neural networks in EEG signal analysis FPGA-based hardware acceleration for medical devices Particle physics collaborations (e.g., sPHENIX) His publications reveal a focus on EEG-based activity recognition , motor imagery classification , and radiation detector stability . Recent work emphasizes edge AI and low-cost neural interfaces . Scientific Awards: None explicitly mentioned. No details provided about advising or grants.

István László OnigaView profile

Associate Professor

• Embedded Systems
• Artificial Neural Networks
• Field-Programmable Gate Arrays (FPGA)
• +6 more

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.

Dávid SzabóView profile

Associate Professor

• Sociolinguistics
• French Language and Literature
• Lexicography
• +5 more

Dávid Szabó is a habilitated Associate Professor at the Institute of Romance Studies , Department of French Language and Literature , Eötvös Loránd University. His academic profile bridges sociolinguistics , focusing on argot and slang studies in French-Hungarian contexts, with computer graphics and C# software development . He has contributed to diverse fields, including AI in education , sustainable finance , and real-time graphics APIs . Research Highlights: His work explores the intersection of language evolution and technology, with recent publications on Green Finance , AI-driven educational tools , and parallel processing in graphics programming . He investigates linguistic taboos in Hungarian politics and translation challenges of urban French slang into Hungarian, while developing educational software like StudyHelper. Technical Contributions: Szabó has pioneered the integration of modern C# with graphics APIs (Vulkan, OpenGL) for real-time rendering , creating frameworks for multi-platform applications and shader program development . His technical papers emphasize code efficiency, API abstraction, and GPU optimization.