جستجوی استاد راهنمای دانشگاهی

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.

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.

• Artificial Intelligence
• Robotics
• Materials Science
• +۳ مورد دیگر

Dengpeng Huang is an Assistant Professor specializing in Artificial Intelligence and Robotics within the field of Elastomer Technology and Engineering . His work bridges computational modeling with advanced materials, focusing on applications in smart materials and mechanical systems. Research Interests: Development of AI-driven models for predicting elastomer properties Multiscale analysis of rubber composites Electromechanical coupling in dielectric elastomer actuators Meshfree methods for metal cutting and chip formation Ultra-precision polishing of optical surfaces Recent Trends: His 2024–2025 publications emphasize data-driven modeling of rubber's viscoelastic behavior, multiscale analysis of composites, and CNN-based approaches for material characterization. Earlier work (2014–2022) explores tool path optimization, beam modeling, and computational machining. Scientific Recognition: Holds an h-index of 5 according to Scopus citations, with recognition as an AI and robotics expert in the service industry. Advising & Collaboration: Collaborates with researchers like Anna Blume, Evgeny Karaseva, and Tim Bor on elastomer composites. Supervised at least one academic work, though specific students are not named in the provided data. Labs & Teams: Affiliated with simulation and robotics teams in the smart materials sector, likely within an advanced materials or mechanical engineering research group.

Christoph Bruneمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Christoph Brune is a Full Professor at the University of Twente , affiliated with the Digital Society Institute and TechMed Centre . His research bridges Artificial Intelligence , Deep Learning , and Medical Imaging with applications in Robotics and Computational Fluid Dynamics . Keywords: Machine Learning , Network Analysis , Optimal Transport , Medical Image Segmentation Research Trends : Recent publications focus on physics-informed neural networks , equivariant learning for vascular modeling, and operator learning for PDE-constrained problems. His work emphasizes geometric deep learning , implicit neural representations , and robust AI for clinical settings. Scientific Awards : CMI-NEN seed-money award (2022) for medical imaging innovation Collaborative Networks : Active in graph neural networks , optimal transport , and Barron space theory . Key collaborations include Zenodo dataset contributions and ArXiv preprints with multidisciplinary teams in AI, medicine, and engineering.

Bojana Rosicمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Robotics
• +۱۲ مورد دیگر

Bojana Rosic is a Full Professor specializing in Applied Mechanics & Data Analysis. Her research spans Artificial Intelligence, Machine Learning, Robotics, and Uncertainty Quantification, with a focus on integrating computational methods into mechanical systems and materials science. Key Research Areas: Machine Learning, Uncertainty Quantification, Robotics, Soft and Compliant Mechanisms, Materials Simulation. Recent Work: Contributions to neural network-based constitutive modeling for anisotropic materials, real-time control systems for robotic manipulators, and uncertainty quantification techniques using Polynomial Chaos Expansion. Collaborations: Active in interdisciplinary research with applications in energy, sustainability, and biomedical engineering. Her work emphasizes practical implementations of AI in mechanical engineering, including autonomous systems and collaborative robots (cobots). While no specific awards or educational background are detailed here, her extensive research output (68 publications) highlights her leadership in computational methods and machine learning integration.

• Biomechatronics
• Biorobotics
• Exoskeletons
• +۵ مورد دیگر

A.Q.L. Keemink serves as an Assistant Professor in Biomechatronics and Biorobotics at the University of Twente, within the Faculty of Engineering Technology's Department of Biomechanical Engineering. He is a member of the Biomechatronics and Rehabilitation Technology (ET-BE-BRT) research group and the TechMed Centre. His academic credentials include an MSc (cum laude) in Mechatronics and a PhD in human power augmentation systems and interaction control. Research interests encompass optimization-based control for human-robot interaction, specifically exoskeletons for upper and lower limb support. His work integrates machine learning, optimal motion planning, model-predictive control, and neuromechanics imitation to address rehabilitation challenges for individuals with movement deficits. Key domains include biomechatronics, biorobotics, and rehabilitation engineering. Keemink collaborates within the Biomechatronics and Rehabilitation Technology group, contributing to the University of Twente's TechMed Centre for medical technology innovation.

Mahdi Nabipourمشاهده پروفایل

پژوهشگر

• Human locomotion
• Wearable robotics
• Exoskeleton design
• +۴ مورد دیگر

Mahdi Nabipour is a postdoctoral researcher at the Department of Biomechanical Engineering, University of Twente, Netherlands. He is affiliated with the Neuromuscular Robotics group (ET-BE-NR) under the Faculty of Engineering Technology and the TechMed Centre. B.S. and M.S. in Mechanical Engineering (2011 & 2014) from Shahid Chamran University of Ahvaz and Amirkabir University of Technology, Iran His research focuses on: Design of wearable robotic devices Predictive control strategies using real-time neuromuscular feedback and biomechanical models Development of human-in-the-loop control systems for lower-limb ankle exoskeletons

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 .

Dannis Brouwerمشاهده پروفایل

استاد

• Precision Engineering
• Flexure Mechanisms
• Mechatronic Systems
• +۶ مورد دیگر

Prof. Dr. Ir. Dannis Brouwer is a professor at the Faculty of Engineering Technology, University of Twente, leading the Precision Engineering group. His work focuses on flexure mechanisms with applications in ultra-precision machinery, robotics, orthoses, and flexible implants. He lectures Design Principles for Precision Mechanisms in Mechanical Engineering programs and has pioneered advancements in large-motion flexure joints. Education: MSc in Mechanical Engineering and Mechatronic Design (Eindhoven University of Technology, 1998-2001); PhD (University of Twente, 2007) Past Roles: Mechatronics System Designer at Philips (2001-2004); Senior Applied Research Engineer at Demcon (2007-2009) Brouwer’s research addresses the limitations of traditional bearings by optimizing flexure joints for high load capacity, large motion, and stiffness. His group developed topology synthesis methods and leverages additive manufacturing to enable geometric complexity at low cost. Applications span space mechanisms, cryogenic systems, and medical devices. His 15 most recent publications focus on flexure modeling, optimization, and applications in robotics and precision engineering. Key subfields include torsion reinforcement, underactuated grippers, and superelement formulations. Scientific Leadership: Associate Editor of Precision Engineering; Director-at-Large, American Society for Precision Engineering (2015-2017) Grants: 14 projects (total 5.5M€), supervising 11 PhD students, 7 PostDocs, and 2 EngD candidates Brouwer integrates education with industry through intensive Master’s courses and lectures at industrial academies. His work bridges theoretical advancements with practical implementations in mechatronic systems.

Antonios Liapisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Procedural Content Generation
• Computational Creativity
• +۵ مورد دیگر

Antonios Liapis is an Associate Professor at the Institute of Digital Games, University of Malta. He completed his PhD in September 2014 under the supervision of Georgios N. Yannakakis at the IT University of Copenhagen. His academic journey includes an M.Sc. in Information Technology from the same institution and a 5-year Diploma in Electrical and Computer Engineering from the National Technical University of Athens. Dr. Liapis has held various academic positions at the University of Malta: Post-doctoral Researcher (2014-2015), Lecturer (2015-2020), Senior Lecturer (2020-2022), and currently Associate Professor (2022-present). He has served as General Chair for multiple international conferences including FDG (2020), GALA (2019), and EvoMusArt (2018-2019). He is an Associate Editor of the IEEE Transactions on Games and a member of the Games Technical Committee of the IEEE Computational Intelligence Society. His research focuses on Artificial Intelligence as an autonomous creator and as a facilitator of human creativity. Key areas include computationally intelligent tools for game design and computational creators that blend semantics, visuals, sound, plot, and level structure to create various game genres including horror, adventure, shooter, and dungeon crawler games. His work has resulted in over 150 peer-reviewed publications and several research awards. Dr. Liapis has secured multiple research grants from the European Commission, including projects on AI-powered robotic material recovery, virtual reality aided design, and learning science through coding and play. His notable project series "Data Adventures" demonstrates the use of open data from Wikipedia, DBpedia, Wikimedia Commons, and OpenStreetMap to automatically generate adventure games with complete plots, characters, items, and locations. His scientific contributions have been recognized with several awards including Best Paper Awards at major conferences, Best Reviewer Award, and Runner-Up Best Student Paper Award. Dr. Liapis has also co-organized 16 workshops in diverse conferences throughout his career. Research interests include: Artificial Intelligence for creative applications Procedural Content Generation in games Computational Creativity systems Machine Learning for game design Affective Computing in virtual environments Human-AI collaboration in creative processes His recent work shows a strong trend toward integrating Large Language Models with game design, exploring quality diversity algorithms for creative applications, and advancing affect modeling for improved player experience. The research spans computer science, artificial intelligence, game studies, and human-computer interaction, with practical applications in education, entertainment, and design.

Marco Dorigoمشاهده پروفایل

استاد پژوهش

• Swarm Intelligence
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Marco Dorigo serves as Research Director for the Belgian F.R.S.-FNRS and co-director of IRIDIA, the artificial intelligence laboratory at the Free University of Brussels. He holds the position of Editor-in-Chief for Springer's Swarm Intelligence journal, establishing him as a leading authority in bio-inspired computing and collective intelligence systems. His research fundamentally centers on swarm intelligence and its intersections with robotics, multi-agent systems, and adaptive computing. Dorigo pioneered ant colony optimization algorithms and continues to drive innovation in human-swarm interaction, reality gap mitigation, and self-organizing systems. His work bridges theoretical computer science with practical applications in robotics and complex system design, emphasizing emergent behaviors and decentralized control mechanisms. Dr. Dorigo's scientific accolades include the Italian Prize for Artificial Intelligence (1996), Marie Curie Excellence Award (2003), Dr. A. De Leeuw-Damry-Bourlart award (2005), Cajastur International Prize for Soft Computing (2007), ERC Advanced Grant (2010), and the IEEE Frank Rosenblatt Award (2015). He maintains elite recognition as a Fellow of IEEE, AAAI, and ECCAI. As co-director of IRIDIA, he leads a multidisciplinary research collective advancing swarm robotics and collective intelligence through experimental validation and theoretical frameworks. His leadership in the ERC Advanced Grant project demonstrates sustained capacity to secure major research funding while mentoring the next generation of researchers in computational intelligence.

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.

Paulo Jorge Coelhoمشاهده پروفایل

استاد مدعو

• Industrial Automation
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Paulo Jorge Coelho serves as an Adjunct Professor in the Electrical Engineering Department at the School of Technology and Management, Polytechnic University of Leiria, and as an integrated researcher with the ROBiTECH (Advanced Robotics and Smart Factories) group at INESC Coimbra's Leiria delegation. With over 20 years of academic experience since 2004, he specializes in Microprocessors, Industrial Automation, and Computer Vision instruction. Education: Ph.D. in Informatics (2019), Trás-Os-Montes and Alto Douro University Specialization in Automation and Control (2007), Coimbra University Bachelor of Electrical Engineering (2004), Coimbra University Research Focus: His work bridges industrial automation and computer vision with cutting-edge machine learning applications in biomedical imaging, ambient assisted living, and assistive technologies. Current projects emphasize practical implementations for reducing physical impairments and enhancing healthcare solutions through deep learning frameworks. Publication Trends: Recent work (2024-2025) reveals strong interdisciplinary convergence between healthcare diagnostics (schizophrenia/EEG analysis, perinatal depression prediction) and industrial/computer vision systems (sports analytics, activity recognition). His research consistently leverages sensor fusion and deep learning architectures to solve real-world problems across medical and engineering domains. Professional Engagement: Active member of the Portuguese Engineers Order and Portuguese Association for Pattern Recognition, with significant editorial contributions (89+ edited articles) across AI and computer vision domains. Previously served as course director and Scientific-Pedagogical Committee member for the Master's in Electrical and Electronic Engineering. Research Infrastructure: Operates within ROBiTECH's advanced robotics ecosystem at INESC Coimbra, focusing on smart factory solutions and human-robot interaction systems. His lab environment integrates industrial automation testbeds with biomedical sensor networks for cross-domain innovation.