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Abdeslam Boularias is an Associate Professor in the Department of Computer Science at Rutgers, The State University of New Jersey. His research focuses on robotics, artificial intelligence, and reinforcement learning, with a strong emphasis on robotic manipulation, model-based control, and perception in cluttered environments. He leads research groups in Artificial Intelligence, Intelligent Systems, and Robotics. Key contributions include advancing techniques for dynamic object tracking, sensorimotor learning in unstructured settings, and integrating physics-based reasoning with deep learning. His work has been recognized with prestigious grants such as the NSF CAREER Award and collaborative grants with institutions like Yale University. Grants: NSF NRI Grant (2022), NSF SA&S Grant (2021), NSF CAREER Award (2024) Research Themes: Robotics in clutter, reinforcement learning for manipulation, physics-aware perception, and end-to-end systems for autonomous robots His recent publications emphasize scalable manipulation learning, one-shot imitation, and diffusion models for affordance prediction. He actively contributes to advancing robotic systems that operate reliably in complex real-world scenarios.

• Robotics
• Artificial Intelligence
• Reinforcement Learning
• +5 more

Jie Luo is a researcher affiliated with Harvard Medical School's Radiology Department, Brigham and Women's Hospital, and collaborates with institutions like Colorado State University and Beihang University. Their work spans biomedical signal processing, machine learning, computer vision, and wireless communication. Key research interests include neuroscience applications such as epileptic zone localization, AI-driven medical imaging with transformers, and telecommunications advancements in hollow-core fiber transmission. Publications reflect interdisciplinary expertise in robot evolution , steganography , and 3D reconstruction . Recent articles focus on integrating high-frequency EEG analysis robust point cloud merging quantum-classical signal co-transmission lightweight AI for industrial quality control across 2025 journals like Biomedical Signal Processing and Control and IEEE Transactions . No awards or student advising data are currently available.

• Biomedical Signal Processing
• Machine Learning
• Image Processing
• +4 more

Sara Falcone is a Visiting Assistant Professor of Computer Science at Vassar College since 2023. She holds a BA from Università degli Studi di Napoli Federico II, an MA from Università degli Studi di Trento, and a PhD from the University of Twente. Her research focuses on teleoperation systems, embodiment in human-robot interaction, and multimodal interaction design. Education: PhD, University of Twente MA, Università degli Studi di Trento BA, Università degli Studi di Napoli Federico II Her work explores how perceptual cues and calibration techniques influence the sense of embodiment in teleoperation, with applications in robotics and human-centered design. Recent studies include developing pupil dilation metrics for embodiment assessment and investigating operator-centric teleoperation models. Her research outputs span robotics, cognitive science, and engineering, emphasizing interdisciplinary approaches to human-robot interaction challenges. Current projects aim to establish standardized guidelines for teleoperation embodiment design.

• Robotics
• Human-Computer Interaction
• Teleoperation Systems
• +3 more

Dr. Hamid Rezatofighi is a Senior Lecturer in the Department of Data Science & AI at Monash University's Faculty of Information Technology in Australia. He holds a PhD from the Australian National University (2015) and a Master's in Biomedical Engineering from the University of Tehran (2009). Previously, he was an Endeavour Research Fellow at Stanford University and a Senior Research Fellow at the University of Adelaide's Australian Institute for Machine Learning (AIML). His research focuses on computer vision, robotics, and deep learning, with emphasis on robot visual perception in dynamic environments. Key contributions include multi-object tracking, autonomous systems, and neuro-symbolic AI frameworks. He has led over $17M in research funding, including DARPA projects and an ARC Discovery Project. His work contributes to UN SDG goals related to innovation and infrastructure. Notable projects include the Neuro-Symbolic Systems (HARNESS) project ($5M+), real-time safety intervention systems, and semantic 3D reconstruction frameworks. He has published >80 peer-reviewed papers in top venues like CVPR/ICCV/ECCV and serves as an Area Chair for major AI conferences. His datasets (JRDB series) are widely used benchmarks for robotic perception. Current research spans autonomous navigation, multi-sensor fusion, and socially-aware human-robot interaction.

• Computer Vision
• Robotics
• Machine Learning
• +5 more

Jun Ueda is a Professor at the Georgia Institute of Technology's School of Mechanical Engineering, affiliated with the Biorobotics and Human Modeling Lab. He joined Georgia Tech in 2008, previously serving as a Visiting Scholar/Lecturer at MIT (2002-2008) and Assistant Professor at Japan's Nara Institute of Science and Technology (2002-2008). He holds a Ph.D. (2002), M.S. (1996), and B.S. (1994) from Kyoto University, Japan. His research focuses on robotics, mechatronics, and bioengineering, emphasizing actuation technology for human-assistive systems. Key projects include bio-inspired actuators, MRI-guided robotics, and secure teleoperation systems. Education: Ph.D., Kyoto University, Japan (2002) M.S., Kyoto University, Japan (1996) B.S., Kyoto University, Japan (1994) Research Interests: Automation & Mechatronics: Robust control, bio-inspired actuators, and sensor integration Bioengineering: Medical robotics, neuromodulation, and tissue modeling Cybersecurity: Secure teleoperation and encrypted control systems Awards: 7th Nagamori Award (2021) IEEE Robotics and Automation Society Early Career Award (2009) FANUC FA and Robot Foundation Award (2005) Advising & Grants: Directed the Interdisciplinary Robotics PhD Program at Georgia Tech (2015-2017) Recipient of Helmsley Distinguished Fellowship (2016) Active grants in MRI-guided robotics and actuator development Labs/Teams: Leads the Biorobotics and Human Modeling Lab, collaborating on medical robotics, tactile sensing, and human-robot interaction.

• Automation
• Mechatronics
• Robotics
• +5 more

Alexandra Tinnermann is a Postdoc researcher at the University Medical Center Hamburg-Eppendorf (UKE), affiliated with the Institute of Systems Neuroscience within the Faculty of Medicine. Her research focuses on neuroimaging techniques, brain-spinal cord interactions, pain mechanisms, and placebo effects. She holds academic rank as a Researcher. Her work spans studies on CNS structure, spinal cord activation during motor tasks, and the influence of opioid analgesia on neural pathways. She has contributed to open-access neuroimaging protocols and multi-center collaborations. Her studies often involve functional MRI, behavioral experiments, and translational pain research. Key areas include exploring how expectations affect vaccine side effects, neural correlates of pain perception, and the role of the ventral premotor cortex in force generation. Her research bridges clinical neuroscience with quantitative neuroimaging, emphasizing reproducibility and interdisciplinary approaches.

• Neuroscience
• Pain Management
• Neuroimaging
• +3 more

Robin Zbinden is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) . He is part of the Laboratoire de science computationnelle pour l'environnement et l'observation de la Terre (ECEO) , focusing on computational methods for environmental and Earth observation challenges. His work bridges machine learning, remote sensing, and ecological modeling to address climate-driven environmental questions. He is also a doctoral student in the Programme doctoral en informatique et communications at EPFL, specializing in data-driven approaches for species distribution and environmental systems. Research interests include species distribution modeling (SDM), multi-modal data fusion, and algorithmic solutions for imbalanced ecological datasets. His work emphasizes adaptive machine learning frameworks like MaskSDM , integrating climate, satellite, and field data to predict species responses to environmental changes. Applications span biodiversity conservation, climate impact assessments, and sustainable food systems. His lab’s computational methods prioritize explainability and scalability, addressing gaps in ecological modeling robustness and generalization. Notable contributions include foundational work on MaskSDM (2025), which enhances SDM flexibility through Shapley values, and studies on monarch butterfly migration under climate shifts. His 2024 research on pseudo-absence selection for deep learning models and 2023 exploration of neural networks in SDM highlight methodological innovations. Recent projects address food environment metrics via sales logs and carbon footprint perception studies (2019). Collaborations span environmental policy, geospatial analysis, and interdisciplinary sustainability challenges. Lab affiliations include the ECEO team at EPFL Valais Wallis, where he develops computational tools for environmental observation. His research narrative combines technical algorithm design with ecological applications, aiming to bridge data science and real-world conservation needs.

• Environmental Science
• Computational Science
• Earth Observation
• +3 more

Peter C.-H. Cheng is a Professor in the Department of Informatics at the University of Sussex, with a distinguished career spanning over three decades in the fields of diagrammatic reasoning, cognitive science, and visual representation systems. His research has significantly contributed to our understanding of how humans interpret and utilize visual representations for problem-solving and knowledge acquisition. Cheng's research interests focus on the cognitive aspects of visual representations, diagrammatic reasoning, representation systems theory, and mathematical knowledge representation. His work bridges cognitive science, computer science, and educational technology, exploring how different representational formats impact human cognition and problem-solving abilities. He has developed theoretical frameworks such as Representational Systems Theory (RST) and has investigated the cognitive properties of various diagrammatic notations including Feynman diagrams, truth diagrams, and algebra diagrams. His recent publications demonstrate a continued focus on the intersection of human cognition and machine intelligence, particularly in how humans and AI systems can collaboratively select and use appropriate representations for problem-solving. His work on Oruga, a system implementing Representational Systems Theory, shows his commitment to translating theoretical insights into practical applications. Expertise in diagrammatic reasoning and visual representation systems Development of theoretical frameworks like Representational Systems Theory Research on mathematical knowledge representation Investigations into cognitive aspects of visualization Applications in educational technology and AI systems Professor Cheng has maintained an active research program with consistent publications in top venues including CHI, CogSci, and Diagrams conferences. His work continues to influence both theoretical understanding of human cognition and practical applications in human-computer interaction and AI systems.

• Diagrammatic Reasoning
• Cognitive Science
• Visual Representations
• +4 more

Arsen Abdulali is a Research Fellow in the Department of Engineering at the University of Cambridge, specializing in soft robotics, haptic modeling, and data-driven control systems. He is a key member of the Bio-Inspired Robotics Laboratory led by Professor Fumiya Iida, where he has made significant contributions to robotic manipulation, tactile sensing, and culinary robotics applications. Dr. Abdulali's research focuses on advancing the capabilities of soft robotic systems through innovative approaches to haptic perception and control. His work bridges material science, machine learning, and practical robotic applications, with a strong emphasis on robotic cooking systems that can perceive and respond to food properties during preparation. His methodology combines experimental work with physical platforms, data-driven modeling, and machine learning techniques for real-world validation. His publication record demonstrates a consistent trajectory of impactful research, with 38 publications spanning from 2015 to 2025. A notable trend in his recent work (2022-2025) shows a strong focus on culinary robotics, where he has developed systems capable of tactile discrimination of food materials, closed-loop control for cooking processes, and multi-modal feedback for optimizing culinary outcomes. His research has practical implications for domestic service robotics, food industry automation, and assistive technologies. Dr. Abdulali maintains productive collaborations across multiple institutions including Fumiya Iida, Josie Hughes, and Grzegorz Sochacki at Cambridge, along with international collaborators from earlier phases of his career. His contributions to the robotics community include serving as co-editor of the proceedings for the 24th Annual Conference on Towards Autonomous Robotic Systems (TAROS 2023) held at the University of Cambridge, indicating recognition of his standing within the field.

• Soft Robotics
• Haptic Modeling
• Tactile Sensing
• +4 more

Yoshihide Sekimoto is a researcher with extensive contributions to computer science, geospatial engineering, and urban planning. His work focuses on leveraging deep learning, remote sensing, and GIS data for infrastructure analysis, human mobility modeling, and disaster response systems. Specializes in road damage detection using federated learning and computer vision Develops synthetic human mobility datasets for urban and disaster scenarios Integrates spatio-temporal modeling with graph networks for transportation systems Applies AI to environmental monitoring (e.g., satellite wind resolution) His recent publications highlight collaborations on 3D building reconstruction, public transit visualization, and land price estimation via street view images. Articles trends include multi-modal data fusion, cross-country infrastructure analysis, and agent-based simulations for urban dynamics.

• Computer Science
• Geospatial Engineering
• Urban Planning
• +3 more

Mingyong Li is a Professor in the Department of Agricultural Engineering at Huazhong Agricultural University's College of Engineering, specializing in agricultural robotics, computer vision, and cross-modal retrieval systems. His research bridges agricultural engineering with advanced machine learning techniques, focusing particularly on rice transplanting automation and precision agriculture systems. His primary research interests include Cross-Modal Retrieval, Image-Text Matching, Agricultural Robotics, Computer Vision, Machine Learning, Semantic Analysis, and Emotion Recognition. Li's work demonstrates strong integration between theoretical machine learning advancements and practical agricultural applications, with particular emphasis on developing vision-based systems for crop monitoring and robotic manipulation in farming environments. Analysis of his recent publications (2023-2025) reveals a strong trend toward multi-modal learning systems that integrate visual perception with textual understanding, particularly applied to agricultural robotics. His work shows increasing sophistication in handling ambiguity in image-text matching while maintaining practical applications in agricultural machinery automation. The research spans both theoretical machine learning advancements and their concrete implementations in agricultural equipment. Professor Li has mentored several researchers who have become frequent collaborators, including Junyu Chen, Yewen Li, and Mingyuan Ge. His work demonstrates consistent funding support through numerous collaborative projects focused on agricultural automation and intelligent systems. His laboratory appears to focus on agricultural robotics systems, with particular emphasis on seedling transplantation machinery, rice farming automation, and sensor-based monitoring systems for crop quality assessment. The research integrates mechanical engineering, computer vision, and machine learning to create practical agricultural solutions.

• Cross-Modal Retrieval
• Image-Text Matching
• Agricultural Robotics
• +4 more

Pratap Tokekar is an Associate Professor in the Department of Computer Science at the University of Maryland, with appointments in UMIACS and Electrical and Computer Engineering. He leads the RAAS Lab and focuses on robotics, particularly algorithms for mobile robots in active sensing and environmental monitoring. His research spans AI, machine learning, and multi-robot systems. Education: Ph.D., Computer Science, University of Minnesota (2014); B.Tech., Electronics and Telecommunication, College of Engineering Pune (2008). Awards include the NSF CAREER Award (2020) and CISE Research Initiation Initiative Award (2016). Teaching highlights include courses on Decision-Making for Robotics, Artificial Intelligence, and Robot Motion Planning at both UMD and Virginia Tech. He advises six Ph.D. students and collaborates with the Maryland Robotics Center and Center for Machine Learning. Awards: NSF CAREER (2020), NSF CISE CRII (2016) Labs/Teams: RAAS Lab, Maryland Robotics Center Key Focus: Multi-robot coordination, active perception, and decision-making under uncertainty.

• Robotics
• Artificial Intelligence
• Machine Learning
• +4 more

Mohamed Benmahdjoub is a Researcher at Erasmus MC's Department of Radiology & Nuclear Medicine, focusing on innovative applications of augmented reality (AR) in surgical procedures. His work bridges computer science and clinical medicine, with a strong emphasis on improving surgical navigation, visualization, and precision in complex interventions such as craniofacial synostosis repairs and neurosurgical catheter placements. Key research areas include AR-assisted surgical guidance, image-to-patient alignment using synthetic data, and multimodal tracking systems. His doctoral thesis (2024) explores AR surgical navigation systems, emphasizing alignment accuracy and 3D perception in operating rooms. Recent studies include feasibility trials for AR-guided rib fracture surgeries and catheter insertion techniques, demonstrating improved precision and reduced procedural risks. Benmahdjoub collaborates extensively with surgeons and engineers to develop practical AR solutions, such as fiducial marker detection algorithms and cross-platform navigation integration. His work has been published in journals like IEEE Transactions on Visualization and Computer Graphics and presented at international conferences on mixed reality and medical imaging.

• Augmented Reality Applications in Surgery
• Craniofacial Disorders
• Surgical Navigation Systems
• +3 more

Kate Larson is a Professor at the David R. Cheriton School of Computer Science, University of Waterloo. Her research explores artificial intelligence with emphasis on multiagent systems, reinforcement learning, and AI applications for sustainability and climate challenges. She employs game-theoretic approaches to address coordination and cooperation in complex systems. Education Ph.D. in Computer Science from Carnegie Mellon University (2004), M.Sc. from Washington University in St. Louis (1999), and B.Sc. from Memorial University of Newfoundland (1997). Research Focus Professor Larson's work bridges theoretical AI and practical societal impacts. Key areas include: Designing cooperative AI frameworks for multiagent environments Developing value-alignment mechanisms for ethical AI Applying reinforcement learning to climate change mitigation Creating robust AI systems through game-theoretic guarantees Publication Trends Her recent publications (2024-2025) demonstrate strong focus on AI safety, game-theoretic foundations, and multimodal learning systems. Dominant themes include: algorithmic alignment techniques, multi-agent risk management, and interpretable AI architectures, with increasing emphasis on real-world climate and sustainability applications. Awards & Recognition No major scientific awards mentioned in source materials. Research Infrastructure No specific labs or teams detailed in available information.

• Artificial Intelligence
• Multiagent Systems
• Reinforcement Learning
• +3 more

Oliver Schneider is an Associate Professor at the University of Waterloo's Department of Information Systems, specializing in haptic technology, human-computer interaction, and sustainable digital fabrication. His research focuses on advancing haptic experience design, accessibility in technology, and innovative approaches to 3D printing sustainability. Key contributions include the Haptic Experience Inventory (HXI), the Proteus Card Deck for remote play, and the Substiports system for sustainable 3D printing. His work bridges engineering and psychology, addressing challenges in user perception, inclusive design, and multimodal interaction. Notable projects include the Haptic Playground (empowering accessible haptic prototyping) and TactJam (collaborative vibrotactile design). He explores intersections between haptics and robotics, such as multi-robot supervision systems and blind-user virtual world interfaces. Research interests span Human perception in haptic systems Design methodologies for tangible interfaces Sustainable manufacturing techniques Accessibility in virtual and augmented reality Recent publications analyze intermittent interaction in fabrication workflows, unified haptic experience models, and user-defined gestures in virtual environments. Articles emphasize practical applications like assistive devices and eco-friendly fabrication.

• Haptic Technology
• User Experience (UX) Design
• Digital Fabrication
• +5 more