جست‌وجوی استاد و پژوهشگر

Mohsen Kaboliمشاهده پروفایل

استادیار

• Embodied Robotics
• Tactile Intelligence
• Machine Learning
• +۳ مورد دیگر

Mohsen Kaboli is an Assistant Professor (Part-time) at Eindhoven University of Technology (TU/e) in the Netherlands, affiliated with the Department of Electrical Engineering and the Electronic Systems group. Since 2018, he has served as head of the AI, Robotics & Cognitive Vehicle research lab at BMW Group’s Center of Invention in Munich, Germany. Previously, he held positions at Radboud University’s Institute for Brain and Cognition (2019-2022) and Technical University of Munich (TUM) as a postdoctoral research fellow. His research focuses on Embodied Robotics, Visuo-Tactile Interactive Perception, and Machine Learning, with applications in Robotic Grasping, Human-Robot Collaboration, and Neuromorphic Computing. He leads European-funded projects like PHASTRAC (Oscillatory Neural Networks for AI Edge Computing) and INTUITIVE (Tactile User Interfaces). Keywords: Tactile Intelligence, AI, Robotics Application Domains: Mobile Robotics, Medical Instrumentation, Neuroprosthetics His recent publications (e.g., ViTract, Shared Visuo-Tactile Perception) emphasize robust object pose estimation and shape reconstruction using Bayesian filtering and Graph Neural Networks. He has authored ~40 academic works and contributed to 20 patents. Scientific Awards : IEEE Senior Member (2018) Georges Giralt Ph.D. Award (Finalist) IEEE ICRA Outstanding Paper Award (Finalist, 2023) IEEE FLEPS Outstanding Student Award (Winner, 2022) IEEE ROSE Outstanding Paper Award (Winner, 2024) Mohsen Kaboli serves as Editor/Associate Editor for IEEE ICRA, IROS, R-AL, T-RO, and IJRR. His work bridges tactile perception, AI, and robotics, with impacts in wearables, medical devices, and autonomous systems.

• Embedded Systems
• Cyber-Physical Systems
• Performance Engineering
• +۳ مورد دیگر

Jeroen Voeten is a Full Professor in the Electronic Systems group of the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He also holds a position as a Research Fellow at the Embedded Systems Institute in Eindhoven and is a Senior Scientist and Scientific Advisor to TNO-ESI since 2017. Academic Background: MSc in Mathematics and Computing Science (1991, TU/e) PhD in Electrical Engineering (1997, TU/e) Voeten's research focuses on formal methodologies for hardware/software system specification, design, and implementation. His work spans computer architectures, embedded systems, performance modeling, and cyber-physical systems. He is currently leading the Carm 2G project with ASML to enhance model-based engineering environments for wafer scanner control systems. His recent publications emphasize advancements in global scheduling, fault-tolerant real-time systems, and hybrid performance modeling. These studies address critical areas like latency reduction, schedulability improvements, and data age analysis in multi-rate task chains. Scientific Awards: Best Paper Award, Forum on Specification and Design Languages (FDL 2005) Best Paper Award, Property-Preserving Synthesis for Unified Control and Data-Oriented Models (2005) Voeten has contributed to 87 conference reports, 13 academic reports, 11 book chapters, and 11 journal articles, reflecting his extensive involvement in both academic and industrial research. Labs and Collaborations: He is affiliated with the Model-Based Design Lab and the High Tech Systems Center at TU/e, collaborating with institutions like TNO-ESI and industry leaders such ASML. His work aligns with the UN Sustainable Development Goals (SDGs) through applications in embedded systems and high-tech manufacturing.

Kees Goossensمشاهده پروفایل

استاد

• Embedded Systems
• Real-Time Systems
• Networks-on-Chip
• +۳ مورد دیگر

Kees Goossens is Full Professor of Real-time Embedded Systems in the Electronic Systems group at Eindhoven University of Technology (TU/e). He leads the CompSOC Lab focused on predictable and composable embedded systems. His research spans composable virtualization, real-time systems, low-power design, and FPGA-based dynamic partial reconfiguration. Previously at NXP Semiconductors, he pioneered networks-on-chip research including the Aethereal NoC architecture. His research interests include: Composable and predictable embedded systems Real-time networks-on-chip (NoC) Memory management and controllers FPGA reconfiguration Hardware verification Low-power electronic design Publication analysis shows consistent focus on real-time systems, networks-on-chip, memory controllers, and embedded systems design, with recent expansion into machine learning applications for networking and error-correction coding. His work frequently addresses automotive and industrial control applications. Editorial contributions include: ACM TODAES editorial board (since 2009) Associate editor for Springer DAEM journal (since 2006) Guest editor for multiple NoC special issues He leads the CompSOC laboratory developing virtualized execution platforms for mixed-criticality systems. Current educational activities include courses on Systems-on-Chip, Embedded Systems, and Computer Architecture.

Yanqiu Huangمشاهده پروفایل

استادیار

• Internet of Things (IoT)
• Cyber-Physical Systems
• Embedded Systems
• +۶ مورد دیگر

Yanqiu Huang is an Assistant Professor in Pervasive Systems, focusing on interdisciplinary research in Internet of Things (IoT), cyber-physical systems, embedded systems, wireless communication, and optimization. Their work addresses resource-constrained networks, emphasizing energy efficiency and collaborative algorithms for IoT devices. Research Interests Design of energy-efficient algorithms for IoT networks Integration of wireless communication and control systems Edge AI and sensor technologies for intelligent transport Recent Publications Key contributions include advancements in Bluetooth Low Energy (BLE) discovery, DNN task allocation for edge computing, and cyclist behavior prediction via IMU data. Research spans environmental monitoring, industrial ventilation, and smart mobility solutions like connected bicycles. Scientific Awards Best Poster Award: DNN Task Allocation for Edge-Aided IoT (2023) Best Poster Award: Smart Connected Bikes (2023) Best Presentation Award (2025) Activities Active in organizing and participating in international conferences (PerVehicle 2024/2025), hosting academic visitors (Koojana Kuladinithi, Konrad Fuger), and delivering invited talks on privacy-preserving crowdsensing.

Gerwin Hoogsteenمشاهده پروفایل

استادیار

• Smart Grids
• Energy Management
• Cyber-Physical Systems
• +۷ مورد دیگر

Gerwin Hoogsteen is an Assistant Professor at the University of Twente, affiliated with the Computer Architecture for Embedded Systems chair. He focuses on smart grids, cyber-physical systems, and applying theoretical research in field-tests. PhD in Decentralized Energy Management (2017, University of Twente) His research integrates machine learning , distributed coordination , and cybersecurity into smart grid optimization. Recent work emphasizes multi-objective optimization for EV charging hubs, energy community resilience, and congestion management. Key article trends include EV charging algorithms , decentralized control , and hybrid storage systems . He contributes to UN SDGs like Climate Action and Affordable Energy . Founder of DEMKit and ALPG open-source software Collaborator in EU projects (SUSTENANCE, SERENE, LocalRES)

Herman van der Kooijمشاهده پروفایل

استاد

• Human balance
• Human motor control
• Adaptation and learning
• +۸ مورد دیگر

Prof. Dr. Ir. Herman van der Kooij is a leading academic in Biomechatronics and Rehabilitation Technology , affiliated with the University of Twente (0.8 FTE) and Delft University of Technology (0.2 FTE). He chairs the Biomechatronics group at UT and has made groundbreaking contributions to wearable robotics for medical and industrial applications. His research focuses on human balance control , neuromechanical modeling , and exoskeleton-assisted mobility . He develops technologies like the LOPES gait rehabilitation robot and the Mindwalker exoskeleton , combining experimental and computational approaches to advance wearable robotics. His work spans soft robotics , real-time EMG-driven control , and low-cost sensor integration . Van der Kooij has published over 170 peer-reviewed works and received prestigious Dutch VIDI and VICI grants . He leads national programs in Wearable Robotics and 4TU Soft Robotics , and serves as associate editor for IEEE journals. He founded two specialized labs: the Rehabilitation Robotics Laboratory (with Roessingh Research and Development) and the Virtual Reality Human Performance Lab , which integrates robotics, motion capture, and VR for testing. He emphasizes active learning in courses like Biorobotics and Biomechatronics , encouraging students to learn through hands-on projects and mistakes. His work also explores non-medical applications of exoskeletons, including industrial ergonomics and entertainment technology .

• Brain Connectivity
• Cognitive Neuroscience
• Language Processing
• +۵ مورد دیگر

Dr. Jan Mathijs Schoffelen serves as Associate Principal Investigator at Radboud University's Donders Institute for Brain, Cognition and Behaviour, where he leads research on brain connectivity using MEG and EEG methodologies. He represents the Norris PI group in the DCCN Representative Council and is a core developer of the globally adopted FieldTrip open-source analysis toolbox, contributing to major initiatives including the Human Connectome Project and MOUS. His research centers on neural synchronization mechanisms during cognitive processes, particularly investigating how brain rhythms facilitate inter-regional communication in language tasks. He addresses critical challenges in non-invasive electrophysiology including signal-to-noise optimization and source reconstruction validity, advancing methods to interpret neural interactions in healthy human cognition where invasive techniques are impossible. Publications reveal consistent focus on gamma-band coherence in sensorimotor systems, attentional modulation through synchronization, and computational frameworks for connectivity analysis. His work bridges theoretical neuroscience with practical tool development, demonstrating growing emphasis on language processing applications alongside foundational motor and visual system investigations. Scientific recognition includes: Young investigator award at Biomag conference, Sapporo (2008) NWO VIDI-grant for independent research leadership (2015) As Principal Investigator, he directs the FieldTrip project while contributing to large-scale collaborations like the Human Connectome Project. His NWO VIDI grant supports methodological innovations for non-invasive connectivity analysis, enabling new investigations into cognitive neuroscience. Embedded within the Donders Institute's Norris research group, he collaborates through the international FieldTrip community to advance electrophysiological analysis standards, maintaining active development of open-source tools that serve thousands of neuroscientists worldwide.

Sandjai Bhulaiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Optimization
• +۴ مورد دیگر

Dr. Sandjai Bhulai is a Researcher at Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands, and affiliated with Vrije Universiteit Amsterdam. He works in the Stochastics department within the Faculty of Science, focusing on applying machine learning and optimization techniques to solve complex real-world problems across diverse domains including telecommunications, mental health crisis intervention, and operations research. His research spans multiple critical areas of applied mathematics and computer science: Development of novel machine learning evaluation frameworks (Dutch Draw, Dutch Scaler) Optimization algorithms for facility location and supply chain problems Telecommunications signal processing using deep learning Mental health applications, particularly suicide prevention helpline analysis Natural language processing for conversation analysis in crisis intervention Dr. Bhulai's recent work demonstrates exceptional interdisciplinary impact. His Dutch Draw framework provides a universal baseline for binary classification problems that has been adopted across multiple research communities. His mental health research has directly influenced suicide prevention practices through machine learning analysis of helpline conversations, identifying specific counselor interventions that improve outcomes. In telecommunications, he's developed neural network approaches that simultaneously handle multiple modulation schemes, improving efficiency in wireless communications. His scientific contributions appear in leading venues including: Computers and Operations Research Journal of Classification IEEE Open Journal of the Communications Society JMIR Mental Health Journal of Applied Probability Dr. Bhulai maintains strong collaborative relationships with researchers across institutions, particularly with Rob van der Mei at CWI. He has secured funding for impactful projects including the collaboration between Stichting 113 Suicide Prevention and CWI, which applies machine learning to improve crisis intervention services. His work exemplifies how theoretical advances in machine learning can address critical societal challenges.

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.