جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Human-Computer Interaction
• Virtual Reality
• +۶ مورد دیگر

Jan-Willem van 't Klooster is an Associate Professor affiliated with the Digital Society Institute, TechMed Centre, and BMS Lab at the University of Twente. His research bridges Artificial Intelligence, Human-Computer Interaction, and Health Informatics, focusing on social robotics, virtual reality, and e-health technologies. Education: PhD in a field related to Artificial Intelligence or Human-Computer Interaction. His work explores the societal context of AI, with a focus on physiological computing, mental workload detection, and user experience in digital systems. Recent projects include AI-reinforced social robotics for rheumatic disease patients and VR-based emotion assessment using multimodal data. Scientific awards include the Best Conference Paper Award (2019). He has contributed to datasets like Perceived Mental Workload Detection using Multimodal Physiological Data and served as an editor for conference proceedings such as IHCI 2024. His research trends emphasize neurostimulation, wearable device validation, and mobile e-coaching applications. Scientific Awards Best Conference Paper Award (2019) Van 't Klooster engages in public outreach, including media appearances on the Dutch CoronaMelder app and its societal impact. He actively participates in academic activities like invited talks on social robotics and oral presentations at international conferences.

• Neurophysiology
• Electroencephalography (EEG)
• Epilepsy
• +۳ مورد دیگر

Michel J.A.M. van Putten is a Full Professor of Clinical Neurophysiology at the TechMed Centre, University of Twente , with an h-index of 52 and 9261 Scopus citations. His research focuses on: EEG-based outcome prediction in post-cardiac arrest coma Deep learning applications for seizure and interictal discharge detection Neurophysiological modeling of epilepsy and traumatic brain injury Cultured cortical neural networks for disease mechanism inference AI-driven analysis of brain connectivity and excitation-inhibition balance His work contributes to UN Sustainable Development Goals for Health and Well-being , Human-AI Interaction , and Economic Impact of AI . Recent research includes: 2025: Automated inference of disease mechanisms in patient-derived neuronal networks 2025: Expert-level deep neural network for interictal discharge detection 2024: Grassmann manifold methods for invariant EEG/MEG feature extraction Scientific recognition includes: 2011: NVvTG Congres Prize 2012: Tripartite prize for Near Infrared Spectroscopy and EEG research His datasets on EEG analysis and cortical network modeling are publicly available through Zenodo, and he has presented 17 oral presentations on topics including: Post-ictal brain recovery mechanisms Mathematical models of peripheral axons Ischemic cerebral damage pathways

Pengcheng Liuمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Automatic Control
• +۷ مورد دیگر

Pengcheng Liu is an Associate Professor in the Department of Computer Science at the University of York, holding this position since January 2020. He maintains active memberships in IEEE, IEEE Robotics and Automation Society (RAS), IEEE Control Systems Society (CSS), and the International Federation of Automatic Control (IFAC), while serving on the IEEE Technical Committee for Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. His research spans robotics, machine learning, automatic control, and optimization, with specialization in humanoid robotics, rehabilitation systems, agricultural applications, and human-computer interaction. Key focus areas include developing lightweight neural networks for embedded agricultural systems, bionic-companionship frameworks for service robots, EMG-controlled rehabilitation devices, and precise control of robotic manipulators using ROS/Gazebo. His work consistently bridges theoretical control systems with practical implementations in healthcare and precision agriculture. Analysis of his publication trends reveals strong emphasis on applying machine learning to real-world robotics challenges, particularly in resource-constrained environments (e.g., agricultural robotics with efficient neural networks) and human-centered applications (e.g., rehabilitation gloves and brain-computer interfaces). Recent work demonstrates increasing integration of computer vision with control systems for autonomous operation. His notable scientific awards include: Global Peer Review Awards from Web of Science (2019) Outstanding Contribution Awards from Elsevier (2017) Dr. Liu has secured and managed research funding through major programs including EPSRC, Newton Fund, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, and NSFC. He serves as a regular reviewer for EPSRC, NIHR, and NSFC grant panels while reviewing for over 30 flagship journals and conferences in robotics, AI, and control systems. His editorial roles include Associate Editor for IEEE Access and PeerJ Computer Science, where he has edited 17 publications. Though specific lab affiliations aren't detailed, his research in agricultural robotics, rehabilitation systems, and humanoid platforms suggests active collaboration with York's robotics and AI research groups, particularly in developing practical implementations of control algorithms and machine learning models for real-world deployment.

Noura Al Moubayedمشاهده پروفایل

دانشیار

• Explainable Machine Learning
• Natural Language Processing
• Optimisation
• +۶ مورد دیگر

Dr. Noura Al Moubayed serves as an Associate Professor in the Department of Computer Science at Durham University, specializing in Explainable Machine Learning, Natural Language Processing, and Optimisation for high-dimensional data challenges. Education PhD from Robert Gordon University Post-doctoral positions at University of Glasgow and Durham University Research Focus Her work develops machine learning solutions for healthcare, social signal processing, cyber-security, and Brain-Computer Interfaces, specifically targeting noisy and imbalanced datasets through advanced NLP and optimization techniques. Current projects integrate deep learning with explainability frameworks to enhance real-world applicability. Publication Trends Recent publications (2020-2022) demonstrate consistent focus on improving question answering systems via bilinear pooling and paraphrasing techniques, alongside novel deep learning approaches for probabilistic topic modeling. These works bridge computer vision, NLP, and multimodal learning to address data imbalance challenges across healthcare and social computing domains. Academic Service PeerJ Computer Science Editorial Board Member 335 authorship points and 30 reviewer points on PeerJ platform