Find Academic Supervisors

Joan Vallvé NavarroView profile

Researcher

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Mobile Robot Exploration
• +6 more

Joan Vallvé Navarro is a post-doctoral researcher at the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of CSIC and Universitat Politècnica de Catalunya (UPC). He is part of the Mobile Robotics group, where his work focuses on state estimation, Simultaneous Localization and Mapping (SLAM), and autonomous navigation systems. His research is deeply technical, centered on algorithm development for mobile robots, particularly using factor graph optimization frameworks. His research interests lie at the intersection of robotics, computer vision, and mathematical optimization. Key areas include SLAM, visual and inertial odometry, sensor fusion, and efficient graph-based estimation. He is particularly known for his contributions to sparsification techniques in pose-graph SLAM, enabling faster and more scalable robotic mapping. His work often appears in top robotics conferences such as IEEE/RSJ IROS and journals like IEEE Robotics and Automation Letters. The trend in his recent publications shows a strong focus on improving computational efficiency in SLAM systems through factor graph sparsification, preintegration of IMU data, and modular software design. He also contributes to autonomous delivery robots and intelligent vehicle systems, indicating applied research directions with real-world deployment goals. Scientific Awards: No scientific awards or honors were mentioned in the provided text. Advising and Grants: While no formal students or advisees are listed, Vallvé has co-authored multiple publications with researchers and students at IRI, suggesting active collaboration and mentorship. He has contributed to major research projects in mobile robotics, likely supported by institutional or national grants, though specific funding sources are not detailed in the text. Labs and Teams: He is a core developer of the WOLF (modular estimation framework) and the yaml-schema-cpp library, both developed at IRI. These tools support robust, maintainable software in robotics research, particularly in configuration validation and state estimation. His work is embedded within a collaborative robotics research environment focused on autonomy and intelligent systems.

• Computer Vision
• Machine Learning
• Visual SLAM
• +3 more

Xin Su is a scientific staff member at the Chair of Media Technology, Technical University of Munich (TUM). He earned his B.Sc. in Electrical Engineering and Information Technology (ETIT) from Karlsruhe Institute of Technology (KIT) in 2019 and his M.Sc. in Electrical Engineering and Information at TUM in 2022. Since January 2023, he has been working on the 6G-ANNA project. Research Focus: Visual SLAM, sensor fusion, and machine learning for robotics. Projects: Involved in initiatives like 6G-ANNA, Centre for Tactile Internet (CeTI), and DFG Teleoperation over 5G. Publications: Recent work includes frameworks for visual-inertial odometry, CAD model retrieval, and point cloud registration, reflecting expertise in robotics and 3D perception. Collaborations: Co-authored papers with researchers such as Adam Misik, Driton Salihu, and Eckehard Steinbach.

• Robotics
• Autonomous Systems
• Sensor Fusion
• +6 more

Marco Camurri is an Associate Professor at the Department of Industrial Engineering, University of Trento. His research focuses on robotics, sensor fusion, and autonomous systems with applications in legged locomotion, SLAM, and dynamic environments. He teaches courses in robotics and mechatronics. DARPA Subterranean Challenge Winner (Team CERBERUS, 2022) His work spans robotics perception, control systems, and terrain adaptation. Key projects include haptic localization, visual-inertial navigation, and GNSS-integrated SLAM. Recent publications emphasize legged robot state estimation and collaborative agricultural robotics. Marco contributes to mechatronics education through courses like Robotic Perception and Action and Electronics and Robotics , collaborating with colleagues such as Mariolino De Cecco and Alessandro Luchetti.

Jochen TrumpfView profile

Professor

• Control Theory
• Observer Theory
• Optimization on Manifolds
• +3 more

Prof Jochen Trumpf is a Professor in the School of Engineering at the Australian National University (ANU). He holds an ORCID identifier and has an h-index of 22 with over 2,500 citations. His research focuses on control theory, observer theory, optimization on manifolds, and applications in robotics, computer vision, and wireless communication. He completed his PhD in Mathematics at the University of Würzburg (2002) and held postdoctoral positions at Ben-Gurion University of the Negev and the University of Notre Dame prior to joining ANU in 2003. His research interests emphasize geometric approaches to nonlinear systems, including equivariant filter design, attitude estimation, and SLAM (Simultaneous Localization and Mapping). He has led or co-investigated multiple projects, including the National Facility for Electricity Grid Security and Resilience Research (2023–2025) and studies on distributed collaborative localization and control. He has collaborated widely, with notable contributions to sensor fusion, inertial navigation, and observer-based control strategies. Prof Trumpf’s work integrates mathematical rigor with practical engineering challenges, with over 90 publications in peer-reviewed journals and conferences. His projects often involve cross-disciplinary teams addressing issues in autonomous systems, navigation, and sensor technology. Despite no explicit awards listed, his citation metrics and project leadership reflect significant academic impact. He supervises research students and has been involved in doctoral training programs, such as the Defence Staff PhD Agreement with Joyce Mau (2018–2022). His research extends to applications in robotics, environmental sensing, and smart grid systems, reflecting a balance between theoretical innovation and real-world problem-solving.

• 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.

Niclas VödischView profile

Researcher

• Continual Learning for Robotics
• Machine Perception
• Simultaneous Localization and Mapping (SLAM)
• +3 more

Niclas Vödisch is a Ph.D. student and researcher at the Autonomous Intelligent Systems Lab in the Department of Computer Science at the University of Freiburg. Supervised by Prof. Dr. Wolfram Burgard and co-supervised by Prof. Dr. Abhinav Valada, he is actively contributing to the field of robotics and AI as a member of the ELLIS Society. His research focuses on enhancing machine perception and SLAM systems using deep learning methods, with primary applications in mobile robotics and autonomous driving. Education: Ph.D. Student at University of Freiburg (June 2021 - June 2025, thesis submitted, defense pending) Visiting Ph.D. Student at University of Zurich (June 2024 - December 2024) M.Sc. in Computational Science and Engineering at ETH Zurich (September 2018 - May 2021) Visiting Undergraduate Student at Carnegie Mellon University (August 2016 - May 2017) B.Sc. in Computational Engineering Science at RWTH Aachen University (September 2014 - June 2018) Vödisch's research interests center around Continual Learning for Robotics, Machine Perception, and Simultaneous Localization and Mapping (SLAM). His work addresses the critical challenge of reducing dependency on extensive annotated training data in robotic perception systems. Through innovative approaches leveraging foundation models, he has developed methods that achieve high performance with minimal supervision, making robotic systems more adaptable to real-world environments. His research spans theoretical advances in deep learning and their practical implementation in autonomous systems. An analysis of his publication record reveals a clear progression from foundational SLAM techniques to sophisticated continual learning frameworks and foundation model applications. A unifying theme across his work is data efficiency in robotic perception, with increasing emphasis on collaborative multi-agent systems and cross-modal information integration for robust performance in challenging conditions. His recent publications demonstrate growing influence in the robotics community, evidenced by the IROS 2024 Best Paper Award for his BEVCar work. Scientific Awards: IROS 2024 Best Paper Award on Cognitive Robotics (BEVCar) IROS 2024 Best Student Paper Award Finalist (BEVCar) Dean's List at Carnegie Mellon University (fall 2016) DAAD full scholarship for CMU studies (2016-2017) Niclas has mentored numerous Master's students on projects spanning LiDAR panoptic segmentation, collaborative scene graph generation, and autonomous driving systems. His teaching portfolio includes co-organizing seminars on Robot Learning and Learning with Limited Supervision, as well as leading the FreiCAR practical autonomous driving course across multiple semesters. His research has received substantial funding from the German Research Foundation (DFG) Emmy Noether Program, NVIDIA academic grants, and Qualcomm Technologies Inc., reflecting the significance and potential impact of his work. As a key contributor to the Autonomous Intelligent Systems group at Freiburg, Vödisch collaborates closely with the Robot Learning group and has extended his research network through his visiting position at the University of Zurich's Robotics and Perception Group. His interdisciplinary approach bridges computer vision, robotics, and machine learning, positioning him at the forefront of research in AI-powered autonomous systems with practical real-world applications.

Samir RawashdehView profile

Associate Professor

• Robotics
• Computer Vision
• Embedded Systems
• +3 more

Samir Rawashdeh is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. He directs research in robotics, computer vision, and autonomous systems through his DAIR Lab. His educational background includes a Ph.D. and M.Sc. in Electrical Engineering from the University of Kentucky, and a B.Sc. from the University of Jordan. Research Focus: Dr. Rawashdeh leads projects in autonomous navigation, humanoid robotics, and sensor systems. His lab develops computer vision algorithms for visual odometry, object tracking, and event-based perception. Current projects include autonomous snowplows, humanoid manipulation systems, and unmanned aerial vehicles, with applications in transportation, space systems, and healthcare. Publications: His research spans computer vision, robotics, and aerospace systems, with recent work focusing on high-temporal-resolution tracking using hybrid vision sensors and lightweight odometry algorithms for mobile robots. His publications demonstrate consistent themes of real-time processing, sensor fusion, and practical implementation in autonomous systems. Awards and Recognition: Advised student teams to win top positions in AUVSI IGVC and ION Snowplow competitions Honorable Mention in Frank J. Redd Student Scholarship (2011, 2012) First Place in Kentucky Academy of Sciences Graduate Research Competition (2008) IEEE Computer Society Design Competition Honorable Mention (2006) Research Infrastructure: Manages robotics facilities including a full-size humanoid robot, autonomous vehicle platforms, and sensor systems. Develops open-source tools like the Lightweight Visual Tracking (LVT) package and maintains public datasets for event-based vehicle detection.

Zeeshan RanaView profile

Research Fellow

• Autonomous Systems
• Computational Fluid Dynamics
• Computing, Simulation & Modelling
• +4 more

Dr. Zeeshan Rana MSc, PhD, FHEA, MIET, MPEC is a Research Fellow at Cranfield University with expertise spanning Computational Fluid Dynamics , Autonomous Systems , and Artificial Intelligence Applications . His research focuses on high-speed aerodynamics , drone tracking systems , and renewable energy fluid dynamics . Education : MSc in Mechanical Engineering PhD in Computational Fluid Dynamics & Aerodynamics (Cranfield University) His work bridges hypersonic flow analysis with practical applications in Formula One aerodynamics , planetary rover navigation , and textile wastewater treatment . Recent research integrates machine learning with CFD simulations to solve complex engineering problems. Scientific trends in his publications (2025–2013) reveal consistent focus on: Computational Aerodynamics (ILES, WENO schemes, SU2 validation) Autonomous Systems (drone tracking, visual SLAM, sign language modeling) Renewable Energy (VAWT turbines, microbial fuel cells) Scientific Contributions: Fellow of the Higher Education Academy (FHEA) Member of the Institution of Engineering and Technology (MIET) Member of the Professional Engineering Community (MPEC) Current affiliations include the Digital Aviation Research and Technology Centre (DARTeC) , contributing to reinforcement learning-based surveillance and multi-sensor fusion systems for drone tracking.

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

• Autonomous Robots
• Networked Control Systems
• Mechatronic Systems
• +1 more

Holger Voos is Full Professor in Engineering Science at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where he leads the Automatic Control Laboratory. His research focuses on distributed networked control, autonomous robotic systems, safety-critical applications, and mechatronic system design. His recent publications demonstrate strong research emphasis on: Advanced SLAM algorithms integrating visual, inertial and wireless technologies Optimal control strategies for spacecraft formations and aerial robots Neuromorphic vision systems and sensor fusion techniques Reinforcement learning approaches for robotic manipulation Constraint-based optimization methods for robotic perception and control

Magnus JanssonView profile

Professor

• Signal Processing
• Machine Learning
• Navigation and Positioning
• +3 more

Magnus Jansson is a Professor of Signal Processing at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Information Science and Engineering. He holds a Ph.D. from KTH (1997) and has held academic positions at KTH since 1998, progressing from Assistant Professor (1998-2003) to Associate Professor (2003-2013) before becoming a full Professor in 2013. His research focuses on statistical signal processing, machine learning, navigation systems, sensor array processing, and system identification, with applications in radar, communication systems, and sensor fusion. Education: M.Sc. (1992), Licentiate (1995), and Ph.D. (1997) in Electrical Engineering (Automatic Control) from KTH. Postdoctoral work at the University of Minnesota (1998-1999). Served as an editor for IEEE Signal Processing Letters, Elsevier Signal Processing, and EURASIP Journal on Advances in Signal Processing. Current roles include Associate Editor for Elsevier Signal Processing (since 2024). Research emphasizes model selection, low-rank matrix reconstruction, and Bayesian methods, with recent contributions to drone classification via CNNs, robust model selection in high-dimensional data, and navigation algorithms leveraging inertial and vision systems. His work bridges theoretical signal processing with practical applications in robotics, wireless localization, and radar systems. Teaching responsibilities include courses on estimation theory, stochastic signals, and adaptive signal processing. Active in collaborative research on positioning systems and sensor networks, with contributions to IMU-camera calibration and visual-inertial navigation. No specific grants or awards listed, but recognized for editorial roles and academic leadership.

Juho KannalaView profile

Associate Professor

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +5 more

Juho Kannala is an Associate Professor of Computer Vision at Aalto University and an Adjunct Professor at the University of Oulu. He co-founded Spectacular AI, a company focused on spatial AI and sensor fusion solutions. His primary affiliation is with the Department of Computer Science at Aalto University, where he leads research in computer vision, machine learning, and robotics. Education: PhD in Computer Science, University of Oulu (2010) Master's Thesis: 'Measuring the Shape of Sewer Pipes from Video,' Helsinki University of Technology (2004) Research Interests: Juho's work spans 3D reconstruction , visual localization , deep learning , and robotics applications . His research emphasizes practical systems like real-time visual-inertial odometry (HybVIO) and large-scale datasets (TBPos). He explores interdisciplinary areas such as medical imaging and atomic force microscopy automated analysis. Key Projects: Co-developed the ADVIO dataset for visual-inertial odometry Advanced multi-view stereo techniques with Gaussian splatting Co-founded Spectacular AI for spatial AI solutions Teaching: Responsible for the Computer Vision (CS-E4850) course at Aalto University. Advises on bachelor/master/doctoral thesis topics in computer vision and machine learning.

• Robotics
• Nonlinear Control
• System Identification
• +5 more

Professor Ian Manchester is a faculty member in the Department of Mechatronic Engineering at the University of Sydney. He holds a B.Eng (Electrical) Honours and a PhD from the University of New South Wales. His academic roles include Director of the Australian Centre for Robotics and Director of the Australian Robotic Inspection & Asset Management Hub (ARIAM). He has held leadership positions such as Director of Research at the Australian Centre for Field Robotics and co-Director of the Sydney Institute for Robotics and Intelligent Systems. His research focuses on robotics, nonlinear control, system identification, and machine learning, with applications in biomedical engineering, forestry, mining automation, and aviation. He has supervised numerous PhD students working on projects like acrobatic legged robots, surgical robotics, and robotic asset management. Recent publications emphasize robust control methodologies, nonlinear observer design, and stable machine learning models. His work often bridges theoretical advances with practical applications, such as SLAM algorithms and dynamics of walking robots. Prof Manchester has received the Sydney Research Accelerator Fellowship (SOAR) in 2019. He serves on editorial boards for IEEE journals and international conferences. His labs and collaborations include the Australian Centre for Robotics and the ARIAM Hub, focusing on robotic inspection and asset management solutions.

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.

• Resilient Robotic Autonomy
• Aerial Robotics
• Underwater Robotics
• +6 more

Konstantinos Alexis is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). He leads the Autonomous Robots Lab and serves as Principal Investigator for major international projects including the DARPA Subterranean Challenge. His research focuses on developing resilient autonomous systems capable of operating in extreme environments through resourcefulness, robustness, and redundancy. His research interests span resilient robotic autonomy with emphasis on aerial robotics , underwater robotics , robot control , path planning , robot learning , and Simultaneous Localization and Mapping (SLAM) . He takes a holistic approach across these disciplines to enable autonomous systems to navigate challenging environments including subterranean spaces, underwater operations, and extreme terrestrial conditions. Recent work demonstrates significant advances in collision-tolerant navigation, degradation-resilient state estimation, and semantic-aware inspection planning. Professor Alexis has secured substantial funding from diverse sources including US agencies (DARPA, NSF, DOE, USDA), EU Horizon programs, and the Research Council of Norway. His research portfolio includes field deployments in nuclear environments, aquaculture operations, and planetary exploration scenarios. Principal Investigator for DARPA Subterranean Challenge Major grants from NSF, DOE, USDA, and EU Horizon programs Research Council of Norway funding for multiple projects He actively supervises numerous PhD and Master's students, with recent graduates leading publications in top robotics venues. His lab maintains strong collaborations with international research groups and industry partners for real-world deployment of autonomous systems. Current initiatives include the ResiFarm project for underwater operations in fish farms and advanced exploration systems for Martian lava tube environments.