جست‌وجوی استاد و پژوهشگر

Mohammad Ehsan Rangihaمشاهده پروفایل

عضو هیئت علمی

• Business Process Management
• Social Computing
• Process Knowledge Discovery
• +۲ مورد دیگر

Dr. Mohammad Ehsan Rangiha is a Visiting Lecturer at City, University of London, where he teaches undergraduate and postgraduate modules including Business Engineering with ERP Solutions and Project Management . He concurrently serves as Assistant Vice President at Moody's Corporation, indicating a part-time academic engagement. His research focuses on Social Business Process Management (BPM) , particularly integrating social media with BPM to enhance process design and user collaboration. Education: PhD in Social BPM, City University London (2016) MA, Middlesex University (2012) MSc in Business Systems Analysis & Design, City University London (2011) BSc in Computer Science, City University London (2008) Research Interests: His work explores social tagging, process knowledge reuse, and goal-oriented frameworks in BPM. Key innovations include developing platforms for BPM-social media integration and user engagement optimization. His publications emphasize collaborative process design and real-time feedback mechanisms. Publication Trends: Articles (2013–2019) demonstrate a consistent focus on social BPM, with evolving themes: early work on goal-driven models progressed toward practical frameworks for user engagement and knowledge reuse. Recent publications address AI-enhanced task recommendations and process automation. Student Advising: Supervised 20+ MSc dissertations on topics like: Robotic Process Automation (RPA) Agile methodology implementation Social BPM in retail/hospitality sectors Professional Activities: Editorial board member for multiple journals and program committee roles at conferences including BPMS2 and BPM workshops.

Sehoon Haمشاهده پروفایل

استادیار

• robotics
• artificial intelligence
• computer graphics
• +۴ مورد دیگر

Sehoon Ha is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology's College of Computing, with affiliations to the Robotics and Matter & Systems research initiatives. He holds a Ph.D. in Computer Science from Georgia Tech (2015) and a B.S. from KAIST (2009). His research spans robotics, AI, and computer graphics, with core focuses on: Legged locomotion and humanoid control Reinforcement learning for robotic systems Human-robot interaction and assistive technologies Motion synthesis and character animation Recent publications (2024-2025) demonstrate strong emphasis on safe reinforcement learning, vision-language navigation, and human-robot collaboration, with frequent appearances at ICRA, IROS, and RSS conferences. Awards & Honors: NSF CAREER Award (2024) Best Cognitive Paper Award, ICRA 2024 iGibson Challenge winner, CVPR 2021 Research Leadership: Secured $7M collaborative grant for mobile humanoid development (with Samsung/ETRI/KAIST), with additional funding from Google, Meta, Amazon, and ETRI. Leads a 17-member research group including PhD and MS students across robotics and ML domains.

• Robotics
• Artificial Intelligence
• Natural Language Processing
• +۷ مورد دیگر

Jesse Thomason is an Assistant Professor at the Thomas Lord Department of Computer Science, University of Southern California (USC), where he leads the GLAMOR Lab. His research focuses on grounding language in multimodal observations and robotic systems, particularly through language-guided robotics, vision-and-language navigation, neurosymbolic AI, and sign language processing. Assistant Professor, USC (2021–present) Research Interests: Robotics, AI, NLP, Multimodal Interaction, Accessibility Recent publications span topics like language-robotics integration , model collapse in synthetic data , and sign language knowledge graphs . He has received best paper awards at robotics and AI workshops. Teaching includes graduate courses in NLP and deep learning. Notable recent work includes: ReWiND (CoRL 2025): Language-guided reward modeling for robot policies PSALM-V (arXiv 2025): Neurosymbolic planning with LLMs ASL Knowledge Graph (NAACL Findings 2025): Linguistic-infused ASL models Awards: Best Paper, OODWorkshop@RSS25 Best Paper (2nd Place), InterAI@Ro-MAN 2024

• Production Engineering
• Industrial Automation
• Digital Twins
• +۳ مورد دیگر

Dr. Anna Syberfeldt is a Professor in Production Engineering at the Department of Engineering Science, University of Skövde. She leads the Virtual Production Development research group and serves as research director at the ASSAR Industrial Innovation Arena. With a background in computer science from De Montfort University and habilitation in automation engineering, her work bridges AI, robotics, and immersive technologies with industrial production systems. Professor in Production Technology Research Director at ASSAR Industrial Innovation Arena University of Skövde Affiliation Her research focuses on developing innovative industrial solutions through: Artificial Intelligence and Machine Learning Collaborative Robotics and Human-Robot Interaction Digital Twins and Simulation-Based Optimization Augmented/Virtual/Mixed Reality Applications Smart Manufacturing and Industry 4.0 Worker Well-Being and Productivity Optimization Recent publications demonstrate her leadership in simulation-based optimization, collaborative robotics, and mixed reality applications in manufacturing. Her work emphasizes creating ultra-flexible production systems through cyber-physical approaches while prioritizing environmental sustainability and human-centric design. Key trends in her research include: Unified frameworks for virtual commissioning Knowledge graph applications in production systems Multi-objective optimization balancing productivity and ergonomics Smart glasses evaluation for industrial operators Evolutionary algorithms for complex manufacturing problems Her projects demonstrate practical implementations of: MAXLabs distributed cyber-physical testbed ERAIVA posture identification software Virtual environments for human-robot collaboration testing Digital support functions for factory layout planning

• Satellite inspection
• Spacecraft dynamics
• Orbital rendezvous
• +۴ مورد دیگر

Dr. David K. Geller is a Researcher at Utah State University's College of Engineering, affiliated with the Space Dynamics Laboratory. His research program spans spacecraft dynamics, orbital mechanics, and UAV applications, with particular expertise in trajectory optimization and satellite operations. Dr. Geller's research focuses on solving complex problems in space operations through advanced mathematical techniques. His work bridges theoretical developments in orbital mechanics with practical applications for satellite inspection, debris removal, and autonomous spacecraft operations. He has pioneered the application of convex optimization for real-time trajectory planning and developed innovative approaches to spacecraft attitude determination using ground-based photometry. His research demonstrates consistent evolution from fundamental orbital dynamics to increasingly applied mission concepts. Analysis of Dr. Geller's publication record reveals a strategic progression from theoretical foundations to mission-critical applications. His recent work emphasizes autonomy in spacecraft operations, with multiple 2017-2021 papers addressing real-time trajectory planning and multi-spacecraft coordination. The recurring themes across his publications include optimization under constraints, safety in proximity operations, and efficient computational methods for space applications. Primary research areas: Satellite inspection, Spacecraft dynamics, Orbital rendezvous Methodological expertise: Convex optimization, Relative orbital mechanics, UAV navigation Application domains: Space debris removal, On-orbit servicing, Environmental monitoring Dr. Geller maintains active collaborations with researchers including Nicholas Ortolano, Aaron Avery, and previously mentored Austin M. Jensen on UAV-based fish tracking research. His work through the Space Dynamics Laboratory contributes to both fundamental aerospace engineering knowledge and practical space mission capabilities.

Carol MARTINEZ LUNAمشاهده پروفایل

پژوهشگر

• Space Robotics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Dr. Carol Martinez Luna is a Research Scientist and Deputy Head of the Space Robotics group (SpaceR) at the SnT Interdisciplinary Centre, University of Luxembourg. She holds a PhD in Robotics and Automation from Universidad Politécnica de Madrid (2013) and previously served as an Assistant Professor at Pontificia Universidad Javeriana, Colombia. Her research spans Space Robotics , Computer Vision , and Machine Learning , with current focus areas including: Multi-purpose manipulation for planetary/orbital robots Perception systems for autonomous space operations Domain adaptation to bridge simulation-to-reality gaps On-ground testing of orbital technologies Recent publications (2022-2025) predominantly address space debris removal , autonomous grasping , and robotic perception , with heavy emphasis on machine learning techniques and high-fidelity simulation validation. Notable trends include generative models for manipulation and adaptive control under uncertainty. She actively advises 7 PhD students and 9 Master's candidates on topics ranging from space manipulation to vision-based safety systems. Her lab develops experimental platforms for orbital scenario emulation and collaborates on ESA-funded debris removal initiatives.

Andrea Censiمشاهده پروفایل

مدرس

• Autonomous Driving
• Robotics
• Control Systems
• +۳ مورد دیگر

Dr. Andrea Censi is a Lecturer at the Department of Mechanical and Process Engineering, ETH Zurich. His research focuses on robotics, autonomous driving, and control systems with interdisciplinary applications in game theory and urban mobility. Key projects include karma economies for resource allocation and co-design frameworks for embodied intelligence. Research interests span: Autonomous motion planning and decision making Trustworthy AI for socio-technical systems Karma-based congestion management Co-design of robotic control systems Event-based vision and neuromorphic sensing Formal methods for autonomous vehicle safety Selected research trends from publications include: karma economies for fair resource allocation (2024-2025), co-design of perception and decision-making systems (2025), dynamic population games (2024), and safety margin analysis for autonomous vehicles (2023). He teaches 151-0323-00L Hands-on Self-Driving Cars with Duckietown , an accessible education platform for autonomy research.

David Filliatمشاهده پروفایل

استاد

• Robotics
• Perception
• Learning
• +۷ مورد دیگر

David Filliat is a Professor at ENSTA Paris within the Computer Science and Systems Engineering Unit (U2IS), where he serves as Director of the Interdisciplinary Center for Defense and Security Studies (CIEDS). He leads research activities in the Autonomous Systems and Robotics team, focusing on advancing robotics technologies for defense and security applications. Dr. Filliat's research spans multiple critical areas in robotics, with particular emphasis on perception and learning problems. His work aims to develop methods that simplify robot usage while enhancing robustness and autonomy. His primary research interests include navigation, mapping, planning, applied learning for multi-modal perception, and reinforcement learning applications to mobile robots, drones, and autonomous vehicles. His research integrates theoretical advancements with practical implementations, addressing challenges in both indoor and outdoor environments. Analysis of his recent publications reveals a strong trajectory in reinforcement learning, perception systems, and navigation technologies. His work demonstrates increasing sophistication in handling uncertainty, developing robust visual control systems, and creating efficient planning algorithms. Notably, his research shows consistent progression from foundational robotics problems toward more complex multi-modal and multi-task systems that integrate various sensory inputs and decision-making processes. As Director of CIEDS, Dr. Filliat oversees interdisciplinary research bridging defense and security studies with technological innovation. His leadership connects theoretical robotics research with practical security applications, fostering collaboration across multiple domains to address complex security challenges through technological solutions.

Torsten Lilgeمشاهده پروفایل

پژوهشگر

• Nonlinear Systems
• Discrete-Time Control Systems
• Observer Design
• +۳ مورد دیگر

Dr.-Ing. Torsten Lilge is a Senior Researcher/Lecturer at the Institute of Automatic Control Engineering , part of Leibniz University Hannover 's Faculty of Electrical Engineering and Computer Science. He has been a core member of the university since 1991 and held senior roles including Deputy Faculty Information Officer and Mentor at the Mechatronics Center Hannover. Research focus on nonlinear systems and discrete-time control Specialized in observer design for systems with unknown inputs and data fusion applications Key contributor to human-robot interaction and mechatronic stabilization solutions Publications show expertise in: 2025 : Global reactive obstacle avoidance using circular fields 2024 : Robust predictive control for energy systems 2023 : Real-time sampling techniques and educational robotics applications 2021-2019 : Multi-agent trajectory planning and antagonistic impedance control His work bridges theoretical control systems with practical implementations in robotics, with a particular emphasis on nonlinear observer design and dynamic modeling for real-world applications.

Steven Beitesمشاهده پروفایل

استاد

• Robotics in Construction
• Sustainable Housing
• Bio-based Materials
• +۵ مورد دیگر

Steven Beites is a tenured Professor at Laurentian University's McEwen School of Architecture, where he directs the Institute for Northern Housing Innovation and serves as Graduate Coordinator. He holds a Master of Architecture from the University of Toronto, a Master of Science in Digital and Material Technologies from the University of Michigan, and is a Ph.D. candidate at the European Graduate School in Switzerland. Previously, he taught at Toronto Metropolitan University and the University of Waterloo, with visiting appointments at Harvard’s Graduate School of Design. His research integrates robotics, material science, and bio-based materials to transform affordable housing in Northern Ontario, focusing on decarbonization, automated construction, and sustainable design. Key themes include: Robotic fabrication for in-situ construction Low-carbon bio-material assemblies Cable-driven parallel robot (CDPR) systems Community-driven design-build frameworks Publications emphasize robotic construction (68% of recent works), material innovation (21%), and pedagogical/community design (11%), with consistent focus on practical applications for housing affordability. Significant awards include: Voyageur Innovation Challenge 2025 (1st Place) Kathryne Kril-Atkins Innovation Fellowship National Award of Excellence from Canadian Society of Landscape Architects Azure Magazine’s Social Good AZ Award Over 15 national/international competition wins including Postmark Hotel (2024) and Venice Biennale exhibitions He secured $1.75M+ in grants (SSHRC, NFRF) establishing the Institute for Northern Housing Innovation, and advises the Ontario government on modular housing policy through the Council of Ontario Universities.

Tan Chenمشاهده پروفایل

استادیار

• Robotics
• Nonlinear Control
• Legged Locomotion
• +۵ مورد دیگر

Tan Chen serves as Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University, where he directs the Robotics, Locomotion, and Applied Control (RoLAC) Lab. His research bridges robotics, nonlinear control, and AI with applications in healthcare and smart manufacturing. His educational background includes: PhD in Aerospace and Mechanical Engineering, University of Notre Dame (2021) MS in Mechanical Engineering, Shanghai Jiao Tong University (2016) MEng in Optimisation et Automatisation des Processus Industriels, Mines Douai (2014) BS in Mechanical Engineering, Shanghai Jiao Tong University (2013) Chen's research focuses on legged locomotion systems where he develops novel control frameworks integrating geometric nonlinear methods with machine learning. His work spans rehabilitation robotics, collaborative robots for industrial applications, and learning-based control systems. Current projects include Human Locomotion analysis, FractionalNet dynamics modeling, and tethered robotics for complex environments. The RoLAC Lab maintains advanced facilities including GoFish quadruped robot, Ecer humanoid robot, and custom biped platforms. His publication trends show increasing integration of AI with traditional control theory, particularly in motion planning for legged robots and human-robot collaboration systems. Recent work demonstrates significant advancements in robust control for uncertain terrains and industrial assembly tasks. Notable scientific achievements include: Ralph E. Powe Junior Faculty Enhancement Award (2024) NSF CRII Award (2024) ICC Achievement Award for bipedal locomotion research (2024) DAAD AInet fellowship for Postdoc-NeT-AI in robotics Eiffel Scholarship recipient during French studies Chen actively advises multiple PhD students including Shivayogi Akki, Anders Smitterberg, and Dhanush Biligiri while securing competitive grants from NASA and NSF. His NASA-funded project with Prof. Steven Elmer focuses on human locomotion modeling, and NSF support enables fundamental research in robotic control systems. He mentors students through Michigan Tech's Summer Youth Program and maintains strong industry connections through advisory roles and collaborative projects. The RoLAC Lab operates as a multidisciplinary research hub with facilities including GoFish quadruped robot, Ecer humanoid platform, 3D printers, and instrumented treadmill systems. Recent acquisitions like the Ecer humanoid robot (June 2025) enable new research directions in whole-body control and human-robot interaction.

Luc Van Goolمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Object Recognition
• +۵ مورد دیگر

Prof. Luc Van Gool is a leading academic in computer vision and machine learning, holding dual positions at ETH Zurich and KU Leuven. He heads the Computer Vision Laboratory at ETH Zurich while leading the Computer Vision Research Group at KU Leuven. With over 160,000 citations, he ranks among the world's most cited computer scientists and has co-founded 12 startups that attracted tech giants like Nvidia, Apple, and Facebook to Zurich. His research spans 2D/3D object recognition , texture analysis , range acquisition , stereo vision , robot vision , and optical flow . Recent work demonstrates exceptional breadth across fundamental algorithms and real-world applications, with particular emphasis on robustness in dynamic environments and cross-domain adaptation. His teams consistently bridge theoretical innovation with industrial implementation through EU-funded projects like Vanguard, Improofs, and Impact. 2025 publications reveal dominant trends in multimodal learning , 3D scene representation via Gaussian splatting , and incremental object detection . Key advancements include vision-language model integration, embodied reasoning frameworks, and robustness benchmarks for human-object interaction. The research demonstrates a strategic shift toward compositional learning and cross-domain generalization while maintaining core strength in classical vision tasks. His accolades include: David Marr Prize (highest honor in computer vision) Koenderink Prize for fundamental contributions Helava Prize for photogrammetry Tsuji Award for pattern recognition ERC Advanced Grant for groundbreaking research Professor Van Gool drives technology transfer through 12 startups (assaia, Eyetronics, segments.ai, etc.) and major EU projects including ACTS Vanguard and Brite-Euram Soquetec. His labs maintain deep industry partnerships with automotive, medical imaging, and consumer electronics sectors, securing continuous funding for high-risk/high-reward research. Current grants emphasize embodied AI and real-world deployment challenges. The Computer Vision Laboratory at ETH Zurich and KU Leuven group operate as interconnected hubs with over 50 researchers. They maintain specialized facilities for 3D reconstruction, robotic vision, and multimodal sensing, recently expanded through industry partnerships. Current initiatives focus on embodied scene understanding for autonomous systems and vision-language models for industrial inspection.

Paul Robinetteمشاهده پروفایل

دانشیار

• Human-Robot Interaction
• Trust in Autonomous Systems
• Multi-Robot Coordination
• +۴ مورد دیگر

Paul Robinette serves as Associate Professor and Associate Chair for Master of Science Programs in the Electrical and Computer Engineering Department at the Francis College of Engineering, University of Massachusetts Lowell. He maintains significant affiliations with the Printed Electronics Research Collaborative (PERC) and the Raytheon UMass Lowell Research Institute (RURI), where his robotics research integrates printed electronics for field-deployable systems. His office resides in Ball Hall's third floor, with primary contact at Paul_Robinette@uml.edu. Dr. Robinette earned his Ph.D. from the Georgia Institute of Technology, establishing his foundation in robotics and human-systems engineering. This academic background directly informs his experimental approach to human-robot interaction challenges. His research centers on trust dynamics in human-robot teams, particularly examining how moral violations versus performance failures impact trust retention. He investigates drone appearance effects on trust calibration, multi-robot coordination during search and rescue, and trust recovery mechanisms after system malfunctions. His experimental work spans marine environments using platforms like Robowhaler, subterranean scenarios, and emergency evacuation simulations—consistently bridging theoretical AI frameworks with real-world human factors. Analysis of his 2023-2025 publications reveals three dominant trends: 1) Quantitative validation of trust metrics across simulation and reality, 2) Development of relational network architectures for resilient multi-agent teams, and 3) Ethical frameworks for moral trust violations in autonomous systems. His work uniquely integrates reinforcement learning with psychological models to create beneficent AI systems that respect human trust boundaries. As Associate Chair for Master's Programs, Dr. Robinette oversees graduate curriculum development and student mentorship within electrical and computer engineering. His research leadership through PERC and RURI facilitates cross-disciplinary collaboration between robotics, printed electronics, and defense applications—though specific grant details remain unspecified in available materials. He directs experimental robotics teams developing platforms such as Robowhaler for marine autonomy and dataset collection. His lab maintains the publicly available Aquaticus dataset for human-robot teaming research and contributes to standardization efforts like the DECISIVE Test Methods Handbook for subterranean robotics evaluation. Current projects focus on real-world trust validation in navigation deviation scenarios and moral trust repair protocols.

• Network Science
• Robustness and Resilience of Complex Networks
• Dynamics on Networks
• +۳ مورد دیگر

Shreyas Sundaram is the Marie Gordon Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His primary affiliations include the Department of Electrical and Computer Engineering at Purdue University's West Lafayette campus. He holds a PhD from the University of Illinois at Urbana-Champaign (2009), an MS from the same institution (2005), and a BASc in Computer Engineering from the University of Waterloo (2003). His research focuses on network science, robustness of complex systems, dynamics on networks, fault-tolerant control, and graph theory. Key interests include secure control systems, distributed algorithms, and resilience in cyber-physical systems. His work spans applications in epidemic modeling, sensor networks, and multi-agent systems. Recent contributions include frameworks for epidemic control, resilient distributed optimization, and reinforcement learning for energy systems. He has developed methods for distributed state estimation, adversarial attack mitigation, and resource allocation in interdependent infrastructures. His research has been applied to real-world challenges such as autonomous vehicle charging, disaster recovery planning, and cybersecurity in multi-robot systems. Notable publications address topics like SIR epidemic mitigation, Byzantine-resilient algorithms, and sensor scheduling in adversarial environments. Sundaram's work integrates theoretical rigor with practical applications, emphasizing cross-disciplinary approaches to complex systems. His ongoing efforts aim to enhance the reliability and security of networked systems through mathematical modeling and control strategies.

Yilun Duمشاهده پروفایل

استادیار

• Generative Models
• Robot Learning
• Decision Making
• +۵ مورد دیگر

Yilun Du is an Assistant Professor at Harvard University in the Kempner Institute and Department of Computer Science, and a senior research scientist at Google DeepMind. He holds a PhD and bachelor's degree from MIT EECS, advised by prominent figures like Leslie Kaelbling. His research focuses on generative models, decision-making, and embodied agents, emphasizing compositional architectures and energy-based models. Notable contributions include foundational work on diffusion models and the development of frameworks like COMET and Diffusion Forcing. He has received awards such as the Outstanding Paper Award at ICLR 2024 and a gold medal at the International Biology Olympiad. Education: PhD (MIT EECS, 2024), Bachelor's (MIT, 201X). Research spans generative AI applications in robotics, vision, and scientific domains. Key projects include compositional scene understanding, video generation, and multiagent self-improvement systems. His work bridges generative modeling with real-world decision-making, emphasizing generalization and scalability. Publications highlight advancements in energy-based models, video diffusion, and embodied intelligence. He organizes workshops on compositional learning and safe AI at top conferences like NeurIPS. Current interests include decentralized generative architectures for decision-making societies of models and integrating systematic reasoning with deep learning.