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Jordy P.F. SendenView profile

Researcher

• Robotics
• Control Systems
• Artificial Intelligence
• +3 more

Jordy P.F. Senden is a Researcher at Eindhoven University of Technology's Department of Mechanical Engineering within the Control Systems Technology group. He holds a PhD in Robust Robotic Systems (2024) and a Master's in Mechatronics Engineering (2016). His primary role is Knowledge Valorisation Officer, focusing on bridging academic research with industrial applications. Research interests include advanced robotics, particularly world model architectures, autonomous systems, and sensor-based perception. Key projects include the EU-funded AI-matters initiative (2023–2027), which develops AI-driven testing frameworks for manufacturing industries. Publications emphasize robust robotic systems, multi-hypothesis tracking, and plant dynamics exploitation. His work combines control theory with AI, addressing challenges in navigation, perception, and system resilience. Active collaborations span European institutions, with a focus on agricultural and industrial robotics applications.

Wesley RoozingView profile

Assistant Professor

• Robotics
• Mechatronics
• Actuation Systems
• +5 more

Dr. Wesley Roozing is an Assistant Professor at the Robotics and Mechatronics (RaM) group and Robotics Centre of the University of Twente, Netherlands. He holds a PhD from the Italian Institute of Technology (2018) and was a visiting student at the Australian Centre for Field Robotics. His research focuses on compliant actuation, mechatronic co-design, energy-efficient systems, bio-inspired mechanisms, and control methodologies. He actively contributes to the robotics community through roles such as chair of the euRobotics Mechatronics Topic Group, associate editor for IROS and BioRob, and organizer of ICRA events. His work emphasizes high-performance robotics for applications like jumping, grasping, and athletic feats, with publications in top journals and conferences like IJRR, T-MECH, and ICRA. Education: PhD in Robotics, Italian Institute of Technology (2018) Visiting Student, Australian Centre for Field Robotics Research interests include advanced actuation systems, motor-gearing integration, and safety-critical control strategies. He leads national/international projects and teaches courses on energy-based modeling. His research bridges electric motor innovations with mechanical design to achieve dynamic robot capabilities. Notable awards include an honorable mention for the RA-L Best Paper Award (2021). His work contributes to UN Sustainable Development Goals related to Industry, Innovation, and Infrastructure (Goal 9) through advancements in energy-efficient robotics. Advising and Grants: Coordinates research projects, advises on robotics program development, and serves on examination boards. His grants fund collaborative efforts in mechatronics and robotics education. Labs/Teams: Part of the Robotics and Mechatronics group at University of Twente, contributing to the Robotics Centre’s interdisciplinary initiatives.

Nur HazlizaView profile

Lecturer

• MEMS
• IoT
• Embedded Systems
• +3 more

Nur Hazliza is a Lecturer in the Department of Common Engineering at the Malaysia School of Engineering, Monash University Malaysia. She holds a PhD in Electrical & Electronics Engineering from Universiti Kebangsaan Malaysia (UKM), completed in 2019, with a focus on MEMS-based true north determination. Her research has been recognized with awards, including the Islamic Innovation Challenge 2019 Gold Medal and a gold medal at the 2017 International Conference and Exposition on Inventions. She has over ten years of teaching experience, previously serving as Head of Tutor and Diploma Coordinator at UCSI University (2008–2015) and part-time lecturer at Segi University (2017–2018). Her expertise spans MEMS, IoT, embedded systems, control systems, robotics, and digital signal processing. Education: PhD (UKM, 2019), MSc Microelectronics (UKM, 2015), Bachelor of Electronics Engineering Technology (UniKL, 2008). Current Project: Leading as Chief Investigator in the 'Agriculture 4.0' project (2023–2025), focusing on digital twins and deep learning for agriculture. Her research trends emphasize sensor development, navigation systems (e.g., gyrocompassing), and applications in agriculture and food safety (e.g., honey adulteration detection). Recent articles highlight innovations in MEMS, optical fiber sensors, and machine learning. Awards: Islamic Innovation Challenge Gold Medal (2019), 2017 National Intellectual Property Awards finalist. Activities: Peer reviewer for journals like Spectrochimica Acta and Measurement , mentor for internships, and organizer of STEM programs like Long Pasia Solar STEM.

Xiaoxiao ChengView profile

Lecturer

• Adaptive Control Systems
• Human-Machine Interaction
• Robotics
• +5 more

Xiaoxiao Cheng is a Lecturer in Engineering Systems for Robotics at the University of Manchester's Department of Electrical and Electronic Engineering. Previously, he held roles as a Research Associate at Imperial College London (2020-2023) and a Research Fellow at Stanford University (2019-2020). He earned a Ph.D. in Electrical and Electronic Engineering from The University of Melbourne (2019), M.Phil. in Mechanical Engineering from Tsinghua University (2014), and B.Eng. in Mechanical Engineering from Beijing Institute of Technology (2011). His research focuses on intelligent autonomous systems and human-machine interfaces, integrating robotics, control theory, AI, and neuroscience. Key areas include adaptive control for human-robot interaction, sensory integration, intention detection, and multi-sensory feedback for applications in manufacturing and hazardous environments. He actively explores autonomous navigation and computational modeling to enhance human-machine collaboration. Recent publications emphasize haptic feedback systems, shared control in robotic surgery, and multimodal sensory datasets. His work contributes to SDGs related to industry innovation and infrastructure, and sustainable cities. Xiaoxiao Cheng welcomes prospective PhD students in robotics, control systems, and human-machine interaction. He is open to collaborations and can be contacted at Xiaoxiao.cheng@manchester.ac.uk .

• Neural Engineering
• Biomedical Engineering
• Human-Computer Interaction
• +3 more

Alexandre Armengol Urpi is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Mechanical Engineering within the School of Engineering. His research focuses on neural engineering, biomedical engineering, human-computer interaction, IoT systems, and signal processing. He explores applications such as brain-computer interfaces (BCI), EEG-based diagnostics, and augmented reality (AR) integration with IoT devices. His work emphasizes translating tacit expert knowledge into actionable data, improving medical diagnostics through machine learning, and enhancing cybersecurity in BCI systems. Projects include developing wireless health-monitoring systems and hands-free AR navigation tools using SSVEP signals. Recent studies address covert spatial attention decoding, industrial IoT-AR systems, and RFID innovations beyond traditional identification. While no formal student advisees are listed, his research collaborations involve interdisciplinary teams across MIT. His contributions span theoretical frameworks and practical implementations, with a focus on closing the gap between human expertise and technological systems. Key research trends include integrating biosignals with smart devices, enhancing neural interface accuracy, and applying deep learning to object pose estimation in industrial contexts. His publications highlight a balance between foundational neuroscience and applied engineering solutions. No scientific awards or grants are explicitly mentioned in the provided texts.

Alejandro SztrajmanView profile

Researcher

• Machine Learning
• Computer Graphics
• Computer Vision
• +3 more

Alejandro Sztrajman is a Researcher and Research Associate at the University of Cambridge's Department of Computer Science and Technology, affiliated with the Rainbow Group. He holds a PhD in Computer Science from University College London (UCL), supported by a Marie Curie Fellowship, and has conducted research internships at Microsoft and Adobe. His work bridges machine learning and visual computing, focusing on neural fields, generative AI, and physics-based rendering, with applications in material appearance modeling, scene illumination, and computational photography. His research spans topics like neural BRDF representations, point cloud generation via diffusion models, and interpretable time series analysis. Key contributions include LSCD (irregular time series imputation), NeuMaDiff (material synthesis via hyperdiffusion), and FrePolad (point cloud generation). He has also developed methods for HDR image deglaring and color calibration for OLED displays. Awards include the Wiley Top Cited Paper and Marie Curie Fellowship funding. His work emphasizes practical applications, with publications in top-tier venues like ECCV, CVPR, and ICML. Collaborations involve Professors Cengiz Öztireli and Rafał Mantiuk at Cambridge, and Tobias Ritschel at UCL. His research aims to advance generative AI, neural rendering, and physically grounded machine learning techniques.

• Computer Vision
• Robotics
• 3D Vision
• +4 more

Sangyun Shin is a Researcher in the Department of Computer Science at the University of Oxford, supervised by Professors Niki Trigoni and Andrew Markham. His work focuses on advancing object localization systems for robotics through 3D vision technologies. He specializes in integrating multimodal sensing (e.g., RGB-D cameras, acoustic arrays) to enhance robotic perception in challenging environments. His research interests include 3D motion capture for wildlife, domain-adaptive 3D detection, and self-supervised learning for nighttime vision. He has explored applications ranging from long-range wildlife tracking to autonomous drone navigation using reinforcement learning. Recent efforts emphasize sensor fusion and neural network architectures tailored for dynamic environments. Key technical contributions include the SoundLoc3D system for 3D sound localization and the WildPose framework for wildlife motion capture. His publications span topics like acoustic neural warping fields (SPEAR), spherical point cloud segmentation, and LiDAR-based object detection for urban driving. Shin's work bridges theoretical advancements in machine learning with practical robotics applications, addressing challenges such as cross-domain adaptation and low-resource sensor setups. His research has implications for autonomous systems, environmental monitoring, and human-drone interaction interfaces.

Liang ZhengView profile

Senior Lecturer

• Computer Vision
• Deep Learning
• Person Re-identification
• +3 more

Liang Zheng is a Senior Lecturer in the School of Computing at The Australian National University (ANU). He holds a Computer Science Futures Fellowship and an ARC DECRA Fellowship. His research focuses on computer vision applications such as person re-identification and medical image understanding, alongside fundamental challenges in dataset-level learning and synthetic data analysis. Zheng has contributed to influential datasets like Market-1501, PRW, and MARS, advancing person re-identification and multi-camera tracking. He has served as Area Chair for CVPR, ECCV, and ACM Multimedia, and is an Associate Editor for IEEE T-CSVT. Education: PhD (Electrical Engineering) from Tsinghua University (2015), Bachelor of Science (Life Science) from Tsinghua University (2010). His research integrates deep learning and generative models, addressing challenges in image generation, adversarial robustness, and multi-agent systems. Notable awards include the Computer Science Futures Fellowship (ANU) and ARC DECRA Fellowship (2020). Key contributions include pioneering work on re-identification datasets and methods, as well as advancements in cross-modal retrieval and domain adaptation. Ongoing research emphasizes scalable models, synthetic data generation, and ethical AI practices.

Benjamin BrackeView profile

Researcher

• Machine Learning
• Medical Imaging
• Computer Vision
• +3 more

Benjamin Bracke, M.Sc., is a researcher with a strong focus on interdisciplinary applications of machine learning and computer vision. His work spans medical imaging, biodiversity informatics, and educational technology. He has contributed to projects such as ImageCLEF challenges, advancing multimedia retrieval systems in healthcare and ecological contexts. Recent research includes diabetic foot ulcer diagnosis, snake species classification using multimodal data, and kidney tumor segmentation via U-Net architectures. Key research interests include machine learning pedagogy, integrating industrial projects into Master’s curricula, and developing automated solutions for medical diagnostics. His technical expertise lies in object detection, semantic segmentation, geospatial data fusion, and metadata-driven classification systems. Bracke’s publications demonstrate a decade-long commitment to medical engineering (e.g., femoral neck axis detection for hip surgeries) alongside modern AI applications. Despite no listed awards or grants, his work bridges academic research with practical clinical and ecological challenges. No lab affiliations or student advisements are explicitly mentioned in the provided texts.

Stephan PareigisView profile

Professor

• Autonomous Systems
• Robotics
• Reinforcement Learning
• +3 more

Prof. Dr. Stephan Pareigis is the Head of the Department of Computer Science at Hamburg University of Applied Sciences. He holds a professorship in Applied Mathematics and Technical Informatics. His career includes leadership roles such as Chair of the Examination Board for Computer Science (2005–2018) and founding director of the autosys research lab . His expertise spans autonomous systems, reinforcement learning, robotics, and embedded machine learning. He has extensive industry experience, including roles at Heidelberger Druckmaschinen AG and Siemens AG before joining academia in 2004. His research focuses on bridging simulation and real-world applications in autonomous systems, particularly in miniature platforms for reliable AI development. Education and Academic Journey: 1992: Diplom Mathematiker (LMU Munich, minor in Computer Science) 1998: Dr.rer.nat in Numerical Methods for Reinforcement Learning (CAU Kiel) Research Interests: Autonomous and Intelligent Systems Reinforcement Learning for robotics Embedded Machine Learning Miniature autonomy testing frameworks His work emphasizes real-world applicability through projects like Miniature Autonomy for scalable AI validation. Labs and Teams: Leads the autosys research lab , focusing on autonomous systems research and industry collaboration through the Forschungs- und Transferzentrum Smart Systems .

DANIEL CORES COSTAView profile

Assistant Professor

• Computer Vision
• Deep Learning
• Machine Learning
• +3 more

Daniel Cores Costa is an Assistant Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing under the Higher Technical School of Engineering. His research focuses on advanced computer vision techniques, including spatio-temporal convolutional neural networks, few-shot learning, and generative adversarial networks (GANs) for small object detection. He earned his Doctorate from the same university in 2022 with a thesis titled Spatio-temporal convolutional neural networks for video object detection , advised by Dr. Victor Manuel Brea Sánchez and Dr. Manuel Mucientes Molina. His work spans video object detection, medical imaging applications (e.g., COVID-19 diagnosis), and autonomous systems like UAV navigation. Key areas include improving data efficiency via pseudo-label mining, enhancing GAN-based data augmentation pipelines, and developing spatio-temporal feature aggregation techniques for dynamic scenes. He collaborates with the GSI Group (Intelligent Systems Group) , contributing to interdisciplinary projects at the intersection of AI, robotics, and healthcare. Publications highlight trends in few-shot learning for medical imaging, video-language evaluation benchmarks (TVBench), and real-time UAV-based object detection. Notable contributions address challenges in small object detection in videos and X-ray vision systems. His research emphasizes practical applications in healthcare diagnostics and autonomous systems. Despite no explicit mention of awards or grants, his prolific publication record (2015–2025) underscores sustained academic engagement. Advising details are unavailable, but his involvement in thesis supervision and collaborative groups like GSI indicates active mentorship.

Henny AdmoniView profile

Associate Professor

• Human-Robot Interaction
• Assistive Robotics
• Nonverbal Communication
• +5 more

Henny Admoni is an Associate Professor in the Robotics Institute at Carnegie Mellon University (CMU), with a courtesy appointment in the Human-Computer Interaction Institute. She leads the Human And Robot Partners (HARP) Lab, focusing on assistive and collaborative robotics, particularly how robots can interpret human nonverbal cues like eye gaze to improve interactions. Her research spans healthcare, human-robot teamwork, and socially assistive robotics, supported by NSF, ONR, and industry partners. Admoni holds a PhD in Computer Science from Yale University and a BA/MA from Wesleyan University. Education PhD in Computer Science, Yale University BA/MA in Computer Science, Wesleyan University Research Interests Admoni’s work emphasizes human-centered robotics, including assistive systems for mobility-impaired users, driver situational awareness modeling, and fostering social support networks through AI. She investigates how robots can proactively learn from humans, adapt to team dynamics, and communicate transparently to build trust. Key themes include nonverbal communication, shared autonomy, and ethical design. Grants & Awards NSF CAREER Grant (2020) Okawa Research Grant (2021) A. Nico Habermann Career Development Professorship (CMU) Advising & Teaching Admoni advises graduate students on topics like robot learning, assistive systems, and human-robot teaming. She teaches courses on Human-Robot Interaction at both undergraduate and graduate levels, emphasizing interdisciplinary approaches that blend robotics, AI, and cognitive science. Labs & Projects The HARP Lab develops robots for meal preparation assistance, driving support, and socially assistive interventions. Current projects include the HARMONIC dataset for collaborative tasks and COHUMAIN for socio-cognitive architectures in human-machine teams. Admoni is on sabbatical at KTH University through 2025.

• Agro Food Robotics
• Precision Farming
• Optimal Control
• +3 more

Eldert van Henten is a full professor of Biosystems Engineering at Wageningen University & Research. His research focuses on agro food robotics, precision farming, and optimal control systems in agriculture. He earned his MSc (1987) and PhD (1994) in Agricultural and Environmental Sciences from Wageningen University. He previously led the Farm Technology Group until 2021 and currently leads projects like FlexCRAFT (Cognitive Robots for Flexible Agro Food Technology) and Trimbot2020, developing robotics and automation for agricultural applications. His work emphasizes translating technology to practical environments (TRL 5-6), with contributions to robotic harvesting, precision weed control, and greenhouse climate modeling. Education: MSc (cum laude, 1987), PhD (1994) in Agricultural Sciences at Wageningen University. Key Projects: FlexCRAFT: Generic robotics principles for food processing, packing, and greenhouse operations. Trimbot2020: Outdoor garden trimming robot with 3D path planning. PoultryBot: Autonomous egg collection in poultry farming. Editorial Roles: Member of editorial boards for Biosystems Engineering, Computers and Electronics in Agriculture, and others. Research Themes: Agro food robotics (harvesting, vision systems, machine learning). Optimal control of agricultural processes (greenhouse climate, irrigation). Generic robotics frameworks for agri-food applications. Awards: Not explicitly listed, but recognized via research impact (h-index 31–47 across platforms). His teaching includes courses on greenhouse technology, field robotics, and thesis supervision. He collaborates with industry through user groups and consortia to ensure practical technology transfer.

Ganesh SankaranarayananView profile

Associate Clinical Professor

• Surgical Simulation
• Medical Robotics
• Artificial Intelligence in Surgery
• +3 more

Dr. Ganesh Sankaranarayanan is a Clinical Associate Professor and co-director of the Center for Assessment of Surgical Proficiency at the University of Texas Southwestern Medical Center. He holds adjunct roles at the University of Texas at Arlington and Texas A&M College of Medicine. His expertise spans surgical simulation, medical robotics, and AI-driven surgical guidance. He earned a Ph.D. in Electrical Engineering from the University of Washington and completed postdoctoral research at Rensselaer Polytechnic Institute. Education: Ph.D. in Electrical Engineering, University of Washington Postdoctoral Research, Rensselaer Polytechnic Institute Key Roles: Co-director, Center for Assessment of Surgical Proficiency Adjunct Professor (UT Arlington) Clinical Associate Professor (Texas A&M College of Medicine) Research Interests: Dr. Sankaranarayanan focuses on advancing surgical education through virtual reality (VR) simulators, haptic technology, and AI applications in surgery. His work includes developing simulators for NOTES, laparoscopic procedures, and emergency scenarios like OR fires. He emphasizes objective performance metrics and cognitive load management in training. Publications: Over 60 peer-reviewed articles in journals like Annals of Surgery and Surgical Endoscopy , with recent work on AI-guided anatomy identification and VR-based safety training. Awards: Immy Award finalist (Lap Band Simulator) Grants & Leadership: NIH-funded research, leadership in SAGES task forces, and development of collaborative surgical simulation frameworks. His work bridges engineering and medicine to enhance surgical training and patient safety.

Ya HuangView profile

Associate Professor

• Biomechanics
• Human Motion Dynamics
• Vibration Analysis
• +6 more

Ya Huang is an Associate Professor in Human Motion Dynamics at the University of Portsmouth, affiliated with the School of Electrical and Mechanical Engineering within the Faculty of Technology. He specializes in human responses to motion, signal processing, and vibration analysis. Huang joined Portsmouth in 2009 after postdoctoral research at the University of Sheffield on structural dynamics and earned his PhD in human responses to whole-body vibration from the Institute of Sound and Vibration Research (ISVR), University of Southampton (2008). Education: PhD in Human Responses to Whole-Body Vibration, University of Southampton (2008) Postdoctoral Researcher, Blast and Impact Dynamics Group, University of Sheffield Research Interests: Dr. Huang focuses on applying analytical and computational methods to understand human biomechanical responses to whole-body vibration and mechanical shocks across land, air, and sea transport. Key areas include: - Development of signal reconstruction methodologies for vibration analysis - Maritime safety through real-time wave imaging and vessel seakeeping optimization - Musculoskeletal modelling for crew bracing strategies on fast lifeboats Grants & Projects: EPSRC-funded project (EP/X035778/1) for real-time semantic wave imaging in maritime safety Royal National Lifeboat Institution (RNLI)-funded studies on crew dynamics on lifeboats Development of stereo vision systems for ocean wave analysis Awards: Best Conference Paper Award at IEEE Conference on Intelligent Systems (2020) Labs & Teams: Part of the Centre of Excellence in Defence, Risk & Resilience. Collaborates on projects involving hydrodynamics, autonomous vessels, and human-centred marine transport design.