جستجوی استاد راهنمای دانشگاهی

• Robotics
• Autonomous Systems
• Spatial Cognition
• +۶ مورد دیگر

Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.

Martin Magnussonمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +۵ مورد دیگر

Martin Magnusson is a Professor at the Department of Natural Sciences and Technology, Örebro University, leading the Center for Applied Autonomous Sensor Systems (AASS) and the Robot Navigation and Perception Lab . His research focuses on robotics and artificial intelligence , particularly 3D mapping, localization, radar-based navigation, and human-robot interaction . Email: martin.magnusson@oru.se Phone: +46 19 303870 Location: Room T1222 His work addresses fundamental challenges in achieving robust autonomy through innovations like the 3D Normal Distributions Transform (3D-NDT) and methods for scan registration in dynamic environments. Recent research extends to radar-based navigation and heterogeneous map data integration , with ethical implications regarding military applications of autonomous systems. Key research themes include: Autonomous Perception: Radar and lidar sensor fusion for localization Dynamic Mapping: Flow-aware and quality-assessed environmental models Human-Aware Robotics: Predictive modeling for safe shared-space navigation Professor Magnusson teaches Computer Graphics , connecting academic principles (e.g., ray tracing, light scattering) to applied research in radar simulation models and neural rendering . His research projects span DARKO (agile production robots), NiCE (changing environment navigation), and Radarize (underground autonomous vehicles).

• Deep Learning
• Robustness in Machine Learning
• Label Noise
• +۴ مورد دیگر

Erik Englesson is a Researcher at the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology. His work is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP-AI/MLX). He holds a PhD from KTH Royal Institute of Technology, focusing on label noise in image classification from an aleatoric uncertainty perspective. His research interests center on robustness and uncertainty quantification in deep learning, particularly addressing label noise and combining aleatoric and epistemic uncertainties. He teaches Probabilistic Graphical Models (DD2420) at KTH. Recent publications explore topics like gradient-based explanation methods, autoencoder parameterization, and noise-robust classification strategies. His work frequently intersects with computer vision and theoretical deep learning principles. Englesson collaborates with prominent researchers like Hossein Azizpour and receives funding from strategic initiatives in AI and autonomous systems. His research bridges foundational theory with practical applications in healthcare imaging and robust model design.

• Expressive Speech Synthesis
• Multimodal Interaction
• Prosody Control
• +۴ مورد دیگر

Éva Székely is an Assistant Professor and Researcher at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. Her work focuses on expressive speech synthesis, multimodal interaction, and the application of synthetic speech in human-robot and conversational systems. She teaches the course 'Human Perception for Information Technology (DM2350)'. Her research interests include adapting synthetic speech to situational context, analyzing prosody and pragmatic functions, and investigating ethical implications of synthetic voice design. Notable projects involve developing tools like ConnecTone for modular AAC systems and CreakVC for voice modulation. She has contributed to foundational studies on gender diversity in synthetic voices and the impact of disfluencies on speaker perception. Recent work explores spontaneous speech synthesis, controllable TTS with neural HMMs, and the integration of gesture and speech synthesis using flow matching techniques. Her research often bridges technical innovation with human-centered design principles.

Zoe Falomirمشاهده پروفایل

دانشیار

• Spatial Reasoning
• Knowledge Representation
• Human-Machine Interaction
• +۳ مورد دیگر

Zoe Falomir is an Associate Professor at the Department of Computing Science, Umeå University, Sweden, and a WASP (Wallenberg AI, Autonomous Systems and Software Program) faculty member. Her research focuses on multidisciplinary areas including Spatial Reasoning, Knowledge Representation, Human-Machine Interaction, Machine Learning, Cognitive Systems, and Creative Problem Solving. Her work bridges symbolic and non-symbolic AI approaches to develop robotic systems capable of learning through multi-modal interaction. Key trends in her publications include spatial problem-solving algorithms, educational applications of AI, and cognitive modeling for navigation and user experience analysis. Research Highlights: Integration of symbolic and non-symbolic AI for spatial reasoning Development of educational games for spatial skill assessment Analysis of environmental complexity in route perception Scientific Awards and Recognition: Extraordinary PhD Award in Experimental Sciences and Technology (University Jaume I) City of Castellón Experimental Science Award Marie Curie Fellow Junior Fellow at Hansewissenschaftkolleg Ramon-y-Cajal Postdoc Fellowship With over 40 journal articles and 50+ conference papers, her collaborations span 50+ co-authors, and her Erdős number is 4. She has contributed to 2 books, 5 proceedings books, and 19 book chapters. Her current research involves the Wallenberg AI program, focusing on autonomous systems.

Sam Thellmanمشاهده پروفایل

استادیار

• Cognitive Science
• Human-Robot Interaction
• Artificial Intelligence
• +۳ مورد دیگر

Sam Thellman is an Assistant Professor in Cognitive Science at Linköping University, affiliated with the Department of Computer and Information Science (IDA) and the Human-Centered Systems (HCS) division. His research focuses on folk-psychological interpretations of AI systems, particularly how humans attribute mental states and intentions to robots and autonomous vehicles. He investigates human-AI interaction to design more predictable and explainable systems, with interdisciplinary work on human-vehicle interaction in self-driving car contexts. Education: Ph.D. in Cognitive Science (2021). Teaching responsibilities include the Bachelor’s program in Cognitive Science, courses on Distributed and Situated Cognition, and Applied Cognitive Science. His research spans human-robot interaction, mental state attribution, and autonomous vehicle behavior perception. Publications highlight topics such as robot emotional capabilities, driving simulation studies, and societal impacts of AI. His work emphasizes ethical and human-centric approaches to technology development. No scientific awards are explicitly listed, but his contributions to explainable AI and human-centered computing are significant.

• Robotics
• Machine Learning
• Tactile Sensing
• +۴ مورد دیگر

Yasemin Bekiroglu is an Associate Professor at Chalmers University of Technology in the Control Engineering department. Her research focuses on data-efficient learning from multisensory data for robotics applications. PhD from Royal Institute of Technology (KTH) in 2012 Her work spans Robotics , including Grasp Planning , Adaptive Control , and Gaussian Processes . Recent publications emphasize Tactile Sensing and Multisensory Integration . Key trends in her research include Motion Planning , Safe Trajectory Generation , and Implicit Surface Representation for robotic systems. She has served as a reviewer and Associate Editor for leading robotics conferences and journals. Best Paper Award at RAHA 2016 Best Manipulation Paper Award at ICRA 2013 IROS CoTeSys Cognitive Robotics Best Paper Finalist in 2013 Yasemin contributes to projects like Dexterous Robot Assistant , advancing everyday object manipulation technologies.

Todor Stoyanovمشاهده پروفایل

مدرس ارشد

• Autonomous Mobile Robots
• Robot Perception Algorithms
• Motion Synthesis for Manipulation
• +۳ مورد دیگر

Todor Stoyanov is a Senior Lecturer in Computer Science and an affiliated faculty member of the WASP program at Örebro University. He serves as the subject manager in computer science and is part of the Centre for Applied Autonomous Sensor Systems (AASS). His research focuses on autonomy for mobile robots, particularly perception algorithms and motion synthesis for manipulation. He holds a PhD in Computer Science from Örebro University (2012), specializing in autonomous robot navigation. Education: PhD in Computer Science, Örebro University, 2012 (Thesis: Reliable Autonomous Navigation in Semi-Structured Environments Using the 3D Normal Distributions Transform) Research Interests: Dr. Stoyanov's work spans autonomous mobile robots, robot perception, motion planning, and manipulation. Key areas include behavior trees for control, deformable object tracking, and reinforcement learning for knowledge transfer. His research often integrates advanced algorithms with real-world robotics applications in logistics, manufacturing, and environmental monitoring. Research Projects: Ongoing: Labour market effects of AI in knowledge-intensive services Ongoing: Dynamic Agile Production Robots (DARKO) Ongoing: TeamRob - Teams of Robots Working for and with Humans Completed: Action and Intention Recognition in Human-Robot Interaction (AIR) Completed: Autonomous Wheeled Loaders for Material Handling (ALL-4-eHam) Labs/Groups: He leads the Autonomous Mobile Manipulation Lab, focusing on full-body mobile manipulation and human-robot collaboration.

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Louise Rixon Fuchs is an Industry doctoral student at KTH Royal Institute of Technology's Division of Robotics, Perception and Learning. She specializes in advanced sonar imaging techniques, machine learning applications, and signal processing for robotics. Her research focuses on enhancing sonar data analysis through deep learning methodologies, optimization algorithms, and compressive sensing models. Her work bridges robotics and computer vision, addressing challenges in low-resolution sonar image processing, object detection, and beamforming optimization. Key contributions include GAN-based sonar image simulations and transfer learning for object recognition in sonar environments. Louise has published extensively on sonar technology advancements, with recent articles emphasizing dense point correspondence algorithms and wide-beam sonar system improvements. She currently holds no recorded scientific awards but is actively contributing to robotics and marine sensing research.

• Human-Robot Interaction (HRI)
• Cognitive Ergonomics
• Eye-Tracking Technology
• +۳ مورد دیگر

Dr. Erik Billing is a researcher at the University of Skövde , actively contributing to the School of Informatics and affiliated with the Interaction Lab (ILAB) . His work focuses on human-robot interaction (HRI), cognitive ergonomics, and extended reality (XR) applications in industrial and educational contexts. Research Pillars : Human-Robot Interaction, Cognitive Ergonomics, Eye-Tracking, Virtual/Augmented Reality, Digital Human Modeling Key Projects : RO-LIV (social robots for elderly), EWASS (industrial HRI), OKAVIM (XR competency) Funding : Supported by AFA Insurance and Vinnova grants His recent work includes investigating body language in social robots, expectation formation in HRI, and gaze-based worker behavior prediction. Dr. Billing serves as editor for SweCog conference proceedings and contributes to VR/XR motor learning research.

Umang Bhattمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Responsible Artificial Intelligence
• Human-AI Collaboration
• +۳ مورد دیگر

Umang Bhatt is an Assistant Professor & Faculty Fellow at New York University's Center for Data Science and a Senior Research Associate at the Alan Turing Institute. His research focuses on trustworthy machine learning, responsible AI, and human-AI collaboration. He holds a PhD from the University of Cambridge's Machine Learning Group, advised by Adrian Weller, and has held fellowships at Harvard's Center for Research on Computation and Society, Mozilla, and the Partnership on AI. Education: MS/BS in Electrical and Computer Engineering from Carnegie Mellon University. Research interests include algorithmic transparency, human decision support systems, and governance mechanisms for AI. Notable contributions include work on 'algorithmic resignation' for responsible AI use and frameworks for stakeholder feedback integration. Awards: JP Morgan PhD Fellowship, Mozilla Fellowship, Harvard SEAS Research Fellowship. Supervision: Leads a team including postdocs, MS/BS students, and Cambridge graduates. Active in academic service as ICML/FAccT program chair and CDT Non-Resident Fellow. Labs/Teams: Works across multi-institutional collaborations including Turing Institute's Safe and Ethical AI group and NYU's Data Science initiatives.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۶ مورد دیگر

Thomas Hellström is a Professor at the Department of Computing Science at Umeå University and is also affiliated as a professor at the Centre for Transdiciplinary AI. He leads the Intelligent Robotics group, focusing on the application of artificial intelligence for developing robots for social and industrial applications, with a particular emphasis on Human-Robot Interaction (HRI). His research spans multiple domains including social robotics, field robotics, and robot ethics. Hellström's research interests center around making robots more understandable and effective in human environments. His work in Human-Robot Interaction explores how robots can communicate their intentions and actions clearly to humans, while his field robotics research focuses on unmanned forest vehicles and agricultural robotics. He has made significant contributions to robot ethics, particularly regarding military applications of robotics, and has published extensively on bias in machine learning. His approach combines theoretical frameworks with practical implementations, often resulting in tangible robotics systems. The analysis of Hellström's recent publications reveals a consistent focus on making robotic systems more transparent, understandable, and socially competent. His work increasingly integrates multiple modalities of interaction, combines cognitive models with practical robotics, and addresses the ethical implications of deploying AI systems in real-world contexts. There's a clear trajectory toward more sophisticated human-robot collaboration frameworks that respect social norms while maintaining technical effectiveness. Hellström has successfully supervised numerous PhD students including Michele Persiani, Ahmad Ostovar, Benjamin Fonooni, Mostafa Pordel, and Ola Ringdahl, while also serving as co-supervisor for several others. His research has been generously funded through multiple EU projects including INTRO (2010-2014), SOCRATES (2016-2020), CROPS, and SWEEPER, along with grants from Vetenskapsrådet, VINNOVA, and various foundations. At Umeå University, Hellström leads the Intelligent Robotics group which maintains strong connections with both academic and industrial partners. The group has developed notable systems including an intelligent rollator for stroke patients and harvesting robots for agricultural applications. Their work often bridges theoretical AI research with practical implementations in real-world environments, particularly in healthcare and agriculture.

Malintha Fernandoمشاهده پروفایل

پژوهشگر ارشد

• Multi-Robot Systems
• Distributed Control
• Autonomous Systems
• +۴ مورد دیگر

Malintha Fernando is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology in Stockholm, Sweden. His current project focuses on Designing Interaction-Aware Heterogeneous Multi-Robot Systems , aiming to develop scalable distributed control policies for multi-robot systems in dynamic environments. He holds a PhD from Indiana University (2023) and an undergraduate degree from the University of Moratuwa, Sri Lanka. Prior to KTH, he served as a visiting lecturer in Machine Learning at Indiana University and completed an internship at Open Robotics. Education: PhD in Robotics/Control Systems, Indiana University, Bloomington, USA (2023) Bachelor’s degree in Engineering, University of Moratuwa, Sri Lanka Research Interests: Malintha’s work emphasizes cooperative control policies for multi-drone systems, particularly in scenarios with communication failures or dynamic environments. His research addresses challenges in humanitarian aid, disaster response, and urban air mobility (UAM) applications. He explores frameworks to balance task prioritization in real-time, minimizing human bias in system design. Key areas include: Decentralized decision-making in multi-agent systems Swarm intelligence and UAV coordination Robust control under uncertainty Grants & Supervision: His fellowship is hosted by KTH’s Department of Mathematics, under the supervision of Assistant Professor Silun Zhang (Optimization and System Theory) and Professor Petter Ögren (Robotics, Perception and Learning). The project aligns with Digital Futures’ mission to address societal challenges through digital innovation. Labs & Teams: Malintha is affiliated with the Division of Optimization and System Theory and the Robotics, Perception and Learning (RPL) group at KTH, contributing to cross-disciplinary research in robotics and control systems.

Ignacio Torroba Balmoriمشاهده پروفایل

پژوهشگر

• Underwater Robotics
• Simultaneous Localization and Mapping (SLAM)
• Autonomous Underwater Vehicles (AUVs)
• +۳ مورد دیگر

Ignacio Torroba Balmori is a postdoctoral researcher at KTH Royal Institute of Technology, affiliated with the Department of Aerospace, Mobility and Naval Architecture and the Robotics, Perception and Learning (RPL) division. He holds a PhD from RPL under the supervision of John Folkesson, focusing on Simultaneous Localization and Mapping (SLAM) for autonomous underwater vehicles (AUVs). His current research emphasizes underwater SLAM with sonar and camera systems, path planning, control strategies, and system identification for AUVs operating in open waters and confined spaces. His work centers on applications such as autonomous seabed surveying, algae farm monitoring, and seaweed mapping. He is actively involved in developing the AUV SAM, Kongsberg Hugin, BlueROV2, USV FloatSAM, and the AUV Lolo. As a mentor, he has supervised master’s theses on topics including underwater SLAM algorithms and bathymetric informative path planning. He teaches courses like 'Introduction to Robotics' (assistant) and 'Underwater Technology' (teacher). Key technical contributions include system identification for hydrobatic AUVs using physics-informed machine learning and tools for bathymetric SLAM, such as the SubmapsRegistration repository. His research is hands-on, prioritizing field robotics and problem-driven solutions.

Virginia Dignumمشاهده پروفایل

استاد

• Responsible Artificial Intelligence
• AI Ethics and Governance
• Human-Agent Teamwork
• +۲ مورد دیگر

Virginia Dignum is Professor of Responsible Artificial Intelligence and Director of the AI Policy Lab at Umeå University's Department of Computing Science. She serves as senior advisor for AI policy to the Wallenberg Foundations and is an active member of the UN High Level Advisory Body on AI. Her institutional affiliations include the Faculty of Technology Policy and Management at Delft University of Technology. Her research centers on the complex interdependencies between people, organizations, and technology. Key interests include: Responsible AI: Moral/ethical issues in autonomous agent teams and regulatory frameworks Social interaction formalization: Developing computational architectures for agent deliberation based on social practices Human-agent teamwork: Investigating negotiation, trust, and collaboration dynamics in socio-technical environments Her work bridges theoretical AI foundations with real-world policy implementation. Analysis of her recent publications reveals strong focus on AI governance frameworks, explainability mechanisms, and fairness operationalization. Key trends include development of context-sensitive ethical assessment tools, regulatory sandbox implementations, and formal models for human-AI coevolution. Her research increasingly addresses planetary-scale AI impacts and children's rights in digital environments. Major recognitions include: Election to Swedish Royal Academy of Engineering Sciences (IVA), 2020 Fellowship in European Artificial Intelligence Association (EURAI), 2018 Veni Fellowship from NWO for agent-based organizational frameworks, 2006 AI Ethics Professional of the Year award, 2022 Dignum actively shapes global AI policy through leadership roles in the UN High Level Advisory Body, Global Partnership on AI (GPAI), and World Economic Forum Council on AI. She chairs major conferences including AAMAS'24 and serves as Ethics Chair for AAAI'24. Her advisory work extends to the European Commission, IEEE Global Initiative, and UNESCO AI Ethics Recommendation implementation. Current initiatives include the Vatican lecture series on AI ethics and regional AI policy development in Västerbotten. She directs Umeå University's AI Policy Lab, focusing on translating ethical principles into actionable governance frameworks. The lab develops tools for regulatory compliance assessment and stakeholder engagement in AI deployment. Recent projects address digital contact tracing governance and AI's impact on democratic processes.