Find Academic Supervisors

• Sensor Fusion
• Visual-Inertial SLAM
• Optical Flow
• +7 more

Zoran Sjanic is an Adjunct Associate Professor at Linköping University, affiliated with the Department of Electrical Engineering (ISY), working in the field of Automatic Control. His research primarily focuses on sensor fusion, visual-inertial navigation, and robotics perception systems. His research interests include: Visual-Inertial SLAM and Odometry Dense Optical Flow using Deep Learning Multi-sensor Image-based Navigation State Estimation and Filtering Robotic Perception and Autonomous Navigation Collaborative Environment Mapping The recent publications indicate a strong trend in integrating deep learning with classical estimation frameworks for improved robustness in navigation systems, particularly in GPS-denied environments. His work bridges computer vision, control theory, and robotics. Scientific contributions include advancements in optical flow evaluation, sliding window estimation, and collaborative qualitative mapping. Notable collaborations include researchers such as Gustaf Hendeby, Martin Skoglund, and Patrick Doherty. He has not listed any formal advisees or awards in the provided material. No information about grants or advising activities is available. There is no mention of lab or research team leadership in the current text.

Javier Civera SanchoView profile

Associate Professor

• Computer Vision
• Robotics
• Artificial Intelligence
• +5 more

Javier Civera Sancho is an Associate Professor at the University of Zaragoza, where he serves as Deputy Director of the Institute of Research in Engineering of Aragon (I3A). He is affiliated with the School of Engineering and Architecture in the Department of Computer Science and Systems Engineering, where he leads research in the RoPeRT group (Robotics, Computer Vision and Artificial Intelligence). With a background in Industrial Engineering and a PhD in Systems and Computer Engineering, both from the University of Zaragoza, Civera has established himself as a prominent researcher in computer vision and robotics. Industrial Engineering degree (2004, University of Zaragoza) PhD in Systems and Computer Engineering (2009, University of Zaragoza) Civera's research primarily focuses on computer vision, with special emphasis on SLAM (Simultaneous Localization and Mapping), 3D reconstruction, and spatial artificial intelligence. His work aims to provide computers with human-like visual capabilities, including object recognition, 3D structure estimation, spatial context understanding, and tracking of moving objects. He has maintained a long-standing research line in scene localization and mapping, making significant contributions to visual SLAM methodologies and applications. His publication trends reveal a strong focus on advancing visual SLAM techniques, with recent work exploring neural rendering integration, semantic mapping, robust camera calibration, and applications in challenging environments like agriculture. His research bridges theoretical computer vision with practical robotics applications, showing increasing integration with deep learning approaches while maintaining strong geometric foundations. 2 research sexenios (with last granted for 2012-2017) 2 teaching quinquenios h-index: 21 (Google Scholar) 397 citations in last 5 years (Google Scholar) Civera has directed 2 doctoral theses and is currently supervising 4 PhD students. His research has been supported through various institutional frameworks at the University of Zaragoza, including his role in the I3A which provides critical research infrastructure. He is actively involved in guiding the next generation of researchers in computer vision and robotics, emphasizing both theoretical understanding and practical implementation. As a member of the RoPeRT research group, Civera collaborates with a multidisciplinary team working at the intersection of robotics, computer vision, and artificial intelligence. His work often involves developing systems that integrate multiple sensor modalities and create robust spatial understanding for autonomous agents.

Thomas WaltherView profile

Research Fellow

• Computational Neuroscience
• Spatial Navigation
• Memory and Learning
• +5 more

Dr. Thomas Walther is a postdoctoral researcher at the Institute of Neuroinformatics (INI), Faculty of Computer Science, Ruhr University Bochum. His work centers on computational neuroscience with a focus on modeling cognitive processes in mammals, particularly spatial navigation, learning, and memory extinction through computational approaches. His educational background includes: Diploma in Electrical Engineering and Information Technology from the University of Dortmund, with thesis research on medical image interpretation, tumor tracking, and robot-assisted radiotherapy Ph.D. in Electrical Engineering and Information Technology from Ruhr University Bochum, dissertation on autonomous visual model learning based on Organic Computing principles Dr. Walther's research spans computational neuroscience, virtual reality systems, robotics, computer vision, computational auditory scene analysis, and Organic Computing. He investigates hippocampal circuit mechanisms underlying spatial cognition and memory processes, employing deep reinforcement learning and computational modeling to bridge neural mechanisms with behavioral outcomes. His work integrates biological plausibility with artificial intelligence techniques to unravel complex cognitive functions. His publication record shows consistent focus on applying reinforcement learning to neuroscience questions, particularly spatial navigation and extinction learning phenomena, while maintaining strong contributions to computer vision (human body modeling) and robotics (RatSLAM optimization). This interdisciplinary trajectory demonstrates evolving integration between machine learning methodologies and neurobiological inquiry. Scientific awards: None mentioned in available information. Dr. Walther has supervised multiple Master's theses including research on deep reinforcement learning algorithms across environments, virtual reality applications, context representation in learning systems, and transfer of learned associations. His advising emphasizes computational approaches to cognitive questions within the INI's collaborative framework. As a core member of the Computational Neuroscience group at INI, he contributes to the institute's mission of understanding natural cognitive systems through experimental psychology, neurophysiology, and AI to develop novel solutions for artificial cognitive systems within interdisciplinary collaborations.

John LeonardView profile

Professor

• Autonomous Mobile Robotics
• Simultaneous Localization and Mapping (SLAM)
• Self-Driving Cars
• +3 more

John J. Leonard is the Samuel C. Collins Professor of Mechanical and Ocean Engineering at MIT, serving as Associate Department Head for Research in the Department of Mechanical Engineering. His work focuses on navigation and mapping for autonomous systems, particularly self-driving cars and underwater robotics. He pioneered research in Simultaneous Localization and Mapping (SLAM), addressing uncertainties in robotic navigation. Leonard holds a B.Eng. from the University of Pennsylvania (1987) and a Ph.D. from the University of Oxford (1991). His research interests include long-term visual SLAM in dynamic environments, autonomous vehicle safety, and human-robot collaboration. He leads projects funded by institutions like Toyota, exploring Level 2.99 autonomous systems that enhance safety through advanced perception and decision-making. Notable contributions include work on trajectory prediction, underwater scene reconstruction, and certifiable robot perception algorithms. Leonard’s research bridges academia and industry, addressing societal challenges like traffic safety and underwater exploration. He emphasizes ethical considerations in autonomous systems and collaborates with policymakers to shape regulations. His educational efforts focus on training the next generation of robotics engineers through courses and mentorship.

Simone FrintropView profile

Professor

• Computer Vision
• Robotics
• Machine Learning
• +4 more

Prof. Dr. Simone Frintrop is the Head of the Computer Vision Group at the Department of Informatics, University of Hamburg. His research focuses on computationally modeling human visual system principles to enhance machine vision systems, emphasizing robustness, flexibility, and intuitive interaction. Key interests include object discovery, visual attention, saliency detection, and SLAM. He leads a research group exploring topics such as visual tracking, robot localization, and multi-sensor systems. His work bridges theoretical models with applications in robotics and cultural heritage analysis (e.g., CT scan-based cuneiform inscription analysis). Research Themes : Object Discovery & Tracking Visual Attention Systems 3D Scene Understanding Autonomous Robot Perception Publications from 2017–2021 highlight advancements in deep learning, point cloud processing, and attention-driven systems. His team's work on 6D object pose estimation and reinforcement learning in visual search demonstrates cutting-edge contributions to robotics and computer vision. No awards or grants are explicitly listed, but his extensive publication record reflects sustained academic impact. He oversees the CV Research Group at the university’s Informatics Department.

Ray GosineView profile

Professor

• Robotics
• Machine Vision
• Industrial Automation
• +4 more

Ray Gosine is a Professor at Memorial University of Newfoundland, holding the J.I. Clark Chair in Intelligent Systems. He specializes in robotics, machine vision, and industrial automation, particularly in natural resource industries. His research also addresses socio-economic impacts of technology and public policy reviews. Gosine has held significant administrative roles, including Dean of Engineering and Vice-President (Research) at Memorial University. He currently serves on boards such as the Canadian Engineering Accreditation Board and ACENET. Notable contributions include leading a public review of hydraulic fracturing in Newfoundland and collaborative research on digitalization in mining and oil/gas sectors. Education: B.Eng. in Electrical Engineering, Memorial University PhD in Robotics, University of Cambridge Research Interests: Automation in natural resources, socio-economic impacts, public policy, and university intellectual property Administrative Roles: Vice-President (Research), Memorial University (2008–2010, 2014–2015, 2016–2017) Dean of Engineering and Applied Science (2003–2008) Awards: Fellow of Canada Academy of Engineering Petro-Canada Young Innovator Award Current Projects: Visiting Scholar at U of Toronto's Innovation Policy Lab (2017 sabbatical) Research on automation in Canada's extractive industries

John CrassidisView profile

Professor

• Spacecraft attitude determination & control
• Formation flying guidance & navigation
• Robust nonlinear control
• +3 more

John Crassidis is the Moog Professor of Innovation and SUNY Distinguished Professor at the University at Buffalo, Department of Mechanical and Aerospace Engineering. He serves as Executive Director of the Collaborative Institute for Multisource Information Fusion and Director of the Center for Space Cyber Strategy and Cyber Security. His research focuses on spacecraft attitude control, formation flying, nonlinear control, and vehicle dynamics. Crassidis holds a PhD (1993), MS (1991), and BS (1989) in Mechanical and Aerospace Engineering from the University at Buffalo. His work emphasizes advanced navigation systems, including inertial navigation, Kalman filtering, and multi-sensor fusion. Key contributions include optimizing spacecraft maneuvers, developing algorithms for attitude estimation using light curves, and analyzing space object characteristics via polarimetric data. He has pioneered applications of particle swarm optimization and total least squares methods in aerospace engineering. Crassidis has received numerous awards including the UB Meyerson Award for Teaching (2017), NASA iTech Semifinalist recognition (2017), and the AIAA Best Presentation Award (2017). His research bridges theoretical advancements with practical implementations in satellite systems and space situational awareness.

Robert MahonyView profile

Professor

• Robotics
• Control Systems
• Computer Vision
• +7 more

Robert Mahony is a Professor in the School of Engineering at the Australian National University (ANU) and an IEEE Fellow. His research focuses on nonlinear systems theory, geometric optimization, and robotics applications, with particular emphasis on control systems, vision-based navigation, and autonomous systems. He holds a PhD in Systems Engineering (1995) and a BSc in Applied Mathematics and Geology (1989), both from ANU. His work spans robot vision, mobile robotics, Lyapunov methods, and nonlinear control, with contributions to inertial navigation, event cameras, and equivariant filtering. He teaches courses such as ENGN4627/ENGN6627 Robotics and leads the Control, Information, Intelligence, Communication, Automation, Decision, and Autonomy Laboratory (CII-CADA Lab) at ANU. Key research themes include observer design for nonlinear systems, sensor fusion, and real-time control algorithms for autonomous vehicles. His publications address challenges in SLAM (Simultaneous Localization and Mapping), multi-object tracking, and robust state estimation under sensor limitations. Awards: IEEE Fellow Labs/Teams: CII-CADA Lab (ANU) Grants/Advising: Course convener for robotics modules; extensive collaborations on fire detection technology and aerospace systems.

Simon HadfieldView profile

Associate Professor

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

Dr Simon Hadfield is an Associate Professor (Reader) in Robot Vision and Autonomous Systems at the University of Surrey, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. His research focuses on advancing computer vision and machine learning techniques for real-world applications, particularly in robotics. He holds a PhD and certifications such as FHEA (Fellow of the Higher Education Academy) and AUS (Advanced University Skills). Research Interests: - Effective exploitation of novel visual sensors - Practical application of computer vision to robotics - Efficient dynamic reconstruction techniques - Real-time robotics applications - Sensor fusion and calibration systems Recent projects include the NIMROD initiative exploring analogue visual sensors and contributions to multimillion-pound research efforts in sustainable packaging and agricultural robotics. His work emphasizes bridging the gap between lab-based techniques and real-world deployment, with publications in top-tier conferences and journals. Notable achievements include developing paradigms for low-complexity 3D reconstruction and proposing methods for depth estimation and localization. His research has been funded by grants such as a £1m award for robotic applications in farming. Collaborations and lab affiliations include CVSSP, where he leads projects at the intersection of vision, robotics, and autonomous systems. Future work continues to focus on scalable vision techniques and ethical AI practices in representation learning.

Konstantinos DaniilidisView profile

Professor

• Computer Vision
• Robotics
• 3D Perception
• +7 more

Konstantinos Daniilidis is the Ruth Yalom Stone Professor at the Department of Computer and Information Science, University of Pennsylvania, within the School of Engineering and Applied Science. He holds an adjunct position at Archimedes, Athena Research Center, Greece. His research focuses on computer vision, robotics, and geometric methods in AI, with a strong emphasis on 3D perception, event-based vision, and neural networks for motion analysis. Education : - PhD in Computer Science, University of Karlsruhe (1992), advised by Hans-Hellmut Nagel. - Diploma (Master's equivalent) in Electrical Engineering, National Technical University of Athens (1986). Research Interests : Daniilidis explores equivariant neural networks for structure from motion and robotics, event-based vision systems for low-latency perception, and 3D human motion capture using monocular and multi-sensor approaches. His work integrates geometric priors with deep learning to address challenges in SLAM, object pose estimation, and autonomous navigation. He also investigates robotic systems for tasks like object catching and fluid dynamics modeling, applying uncertainty-aware methods for robust decision-making. Articles Trends : Recent publications highlight advancements in event camera processing (e.g., EV-TTC, EqNIO), equivariant architectures for shape and motion analysis (SE(3)-Equivariant Networks), and multi-agent robotics with distributed perception systems. Collaborations with institutions such as ETH Zurich and Boston University reinforce his contributions to scalable and geometrically grounded AI solutions. Advising & Grants : He advises over 30 PhD students and postdocs, many now faculty or industry leaders. His grants include NSF awards (NCS-FO 2124355, CPS 2038873), ARL DCIST, and ONR funding, focusing on robotics, vision, and AI. His work often bridges theoretical insights (e.g., matrix permanent for localization) with practical robotic systems like the Tiercel drone. Labs & Teams : Affiliated with the GRASP Laboratory at Penn, a premier robotics & vision research center. Collaborates widely, including with the Robotics Lab at ETH Zurich and researchers at the University of Coimbra. His group develops open-source tools like EV-FlowNet and EventGAN for event-based vision.

• Robotics
• Autonomous Systems
• Dynamic Modeling
• +3 more

Pekka Forsman is a University Lecturer at Aalto University's Department of Electrical Engineering and Automation. His research focuses on robotics, autonomous systems, dynamic modeling, and motion planning. He has contributed to advancing robot dynamics, obstacle-crossing capabilities, and localization systems for both industrial and forest applications. His work spans theoretical modeling and practical implementations in areas like ball-shaped robots, climbing robots, and forest harvester automation. Key research interests include gyroscopic effects in robot motion, decoupled dynamic models for pendulum-driven systems, and simultaneous localization and mapping (SLAM) techniques. He has collaborated on educational initiatives to enhance working life competencies through project-based learning in master's programs. Notable contributions include publications on obstacle-negotiation for ball-shaped robots, multi-step motion planning under kinodynamic constraints, and tree measurement systems for forestry. His research groups include the Autonomous Systems team. He actively publishes in robotics and automation conferences such as IEEE International Conference on Robotics and IASTED Robotics Applications. Professional activities include developing educational methodologies for engineering students and advancing SLAM technologies for real-world robotics applications in challenging environments like forests and industrial settings.

Augusto Luis BallardiniView profile

Researcher

• Autonomous Vehicles
• Sensor Fusion
• Computer Vision
• +3 more

Augusto Luis Ballardini is a Researcher at the Department of Automatic Control, Universidad de Alcalá (UAlcalá), Spain, within the INVETT Research Group focusing on Intelligent Vehicles and Traffic Technologies. He holds a PhD in Computer Science from the University of Milano-Bicocca (2017) and an MSc from the same institution (2012). His work integrates machine learning, computer vision, and sensor fusion for autonomous vehicle localization and decision-making systems. Key awards include a Marie Skłodowska-Curie Actions grant (GET-COFUND fellowship) and a Spanish Ministry grant under Maria Zambrano/NextGenerationEU. His research spans LiDAR-based localization, intersection classification, and uncertainty-aware neural networks. Education: PhD in Computer Science, University of Milano-Bicocca, 2017 MSc in Computer Science, University of Milano-Bicocca, 2012 Research Interests: Autonomous vehicles, sensor fusion, LiDAR and vision-based systems, machine learning for localization, traffic modeling, and safety-critical decision-making. His work emphasizes practical applications such as fail-aware odometry, knowledge graph-driven prediction, and urban scene understanding. Awards & Grants: Marie Skłodowska-Curie Actions research grant (2019) Maria Zambrano/NextGenerationEU project grant (2022) Lab & Collaborations: His research is conducted within the INVETT group at UAlcalá, focusing on interdisciplinary projects that bridge robotics, computer vision, and transportation engineering. Recent work includes benchmarking point cloud registration algorithms and developing explainable AI for autonomous driving.

• Intelligent Vehicles
• Traffic Technologies
• Advanced Driver Assistance Systems (ADAS)
• +7 more

Iván García Daza is a Professor at the Department of Automática, University of Alcalá, Spain. He leads research in Intelligent Vehicles and Traffic Technologies (INVETT group), focusing on autonomous systems, digital twins, and ADAS technologies. His work integrates computer vision, deep learning, and human-vehicle interaction analysis. Education: PhD in Systems Engineering from University of Alcalá (2011), with a thesis on "Driver Fatigue Detection via ADAS Fusion" supervised by Rafael Barea Navarro and Luis Miguel Bergasa Pascual. Research interests include autonomous vehicle algorithms, real-time sensor fusion, virtual reality simulations for human-factor testing, and ecological conservation projects. His recent work emphasizes digital twin technology to bridge simulation-to-reality gaps in autonomous systems. Publications span autonomous driving validation frameworks, LiDAR-based navigation, and ecological studies. Notable contributions include VR-based human-vehicle interaction testing and synthetic data generation for ADAS systems. Labs/Teams: Director of the INVETT lab at the University of Alcalá, collaborating on EU-funded projects involving automotive industries and environmental agencies.

Dimitra PanagouView profile

Associate Professor

• Motion planning
• Coordination and control
• Robotic networks
• +10 more

Dimitra Panagou is an Associate Professor with the Department of Robotics and the Department of Aerospace Engineering at the University of Michigan. She directs the Distributed Aerospace Systems and Control Laboratory (DASC Lab) located in the Ford Robotics Building, where she leads research in safe and resilient autonomy for robotic systems. Her research interests focus on motion planning, coordination and control of robotic networks, autonomous multi-vehicle systems, nonlinear systems and control, navigation and guidance, distributed/decentralized control, and dynamic coverage. Professor Panagou's work emphasizes the development of planning, learning and control methods to address real-world, safety- and time-critical problems through provably correct solutions. Her research spans nonlinear systems, decision making under constraints and uncertainty, estimation and learning, mission planning, and networked control systems with applications across aerial, ground, marine, and space domains. Professor Panagou's recent publications (2024-2025) demonstrate a strong focus on control barrier functions, safety-critical control, resilient multi-robot systems, and risk-aware navigation. Her work bridges theoretical foundations with practical applications in autonomous systems, with particular emphasis on providing mathematical guarantees for safety and performance. The research spans multiple domains including aerial vehicles, marine systems, and ground robotics, addressing challenges in uncertain environments and under various constraints. Professor Panagou has received several prestigious awards including: NASA Early Career Faculty Award AFOSR Young Investigator (YIP) Award NSF CAREER Award IEEE TCAC Best Student Paper Award (for work from her lab) She has been invited to deliver a plenary talk at CDC 2024 and a keynote talk at ICRA 2025, highlighting her prominence in the control and robotics communities. Her research is supported by multiple grants from agencies including NASA, NSF, and AFOSR. The Distributed Aerospace Systems and Control Laboratory (DASC Lab) is equipped with state-of-the-art facilities including a fleet of UAVs (Hummingbirds, Firefly, and Solos), a high-precision motion capture system with 14 VICON cameras, drone LiDAR, and various sensors. The lab conducts both indoor and outdoor flight experiments, with permission for outdoor flights over the Wave Field at the University of Michigan's North Campus.

Xipeng WangView profile

Lecturer

• Robotics
• Machine Learning
• Computer Science
• +2 more

Xipeng Wang is a Lecturer in the Department of Computer and Information Science at the University of Michigan-Dearborn's College of Engineering. He holds a Ph.D. in Computer Science from the University of Michigan-Ann Arbor (2019), an M.S. from the University of Michigan-Dearborn, and a B.S. from Xi'an Jiao Tong University. Ph.D. (2019): University of Michigan-Ann Arbor M.S.: University of Michigan-Dearborn B.S.: Xi'an Jiao Tong University His research focuses on robotics, with specific expertise in robot localization and mapping, autonomous vehicles, and algorithm optimization. He has developed open-source tools like AprilSAM, FLAG, and MOSS for applications in transportation and exploration robots. His work bridges theoretical advancements in SLAM algorithms with practical implementations in autonomous systems. Notable projects include: Zippy: Autonomous low-speed mail delivery vehicle CyberSees: Long-term localization for landfill robots SquadMATE: Battlefield localization system for heterogeneous robots SmartCarts: Campus autonomous shuttle He contributes to open-source robotics frameworks and maintains a strong focus on real-time performance and cross-platform integration of localization algorithms.