Find Academic Supervisors

Zachary ManchesterView profile

Assistant Professor

• Robotics
• Control Systems
• Optimization
• +3 more

Zachary Manchester is an Assistant Professor in the Robotics Institute at Carnegie Mellon University, with a courtesy appointment in Electrical and Computer Engineering. His research focuses on enabling robotic systems to achieve human/animal-like agility and robustness through control theory, optimization, and physics-based algorithms. Key areas include legged robots, drone swarms, and space systems. He leads the Robotic Exploration Lab, which develops both hardware and software for advanced robotic applications. Current PhD students include Arun Bishop, Swaminathan Gurumurthy, and Jeong Hun Lee, among others. His work spans theoretical foundations and practical implementations, with applications in space exploration, terrestrial robotics, and deployable structures. Education: Not explicitly detailed in text. Advising: Supervises multiple PhD and master’s students, with past advisees now at institutions like SpaceX and NASA. Grants/Labs: Leads the Robotic Exploration Lab, collaborating on projects like PyCubed (open-source cubesat framework) and deployable space structures.

• Robotics
• Mechatronics
• Artificial Intelligence
• +3 more

Eelis Peltola is a Doctoral Researcher at the Department of Energy and Mechanical Engineering under the School of Engineering , specializing in mechatronics and robotics. Their work integrates sensor fusion, neural networks, and control systems to solve engineering challenges in autonomous and mechatronic systems. Current Role: Doctoral Researcher, Mechatronics Department: Energy and Mechanical Engineering School: School of Engineering Research Focus: Eelis's research centers on robotics , mechatronics , and sensor fusion , particularly for heavy-duty vehicles and robotic arms. They apply neural networks and low-cost IMUs to improve terrain segmentation, localization, and attitude estimation in off-road applications. Publication Trends: Their recent work (2017–2022) emphasizes inertial measurement unit (IMU) applications , convolutional neural networks , and sensor fusion algorithms for robotics, with interdisciplinary links to computer science and control systems.

• Artificial Intelligence
• Robotics
• Control Systems
• +3 more

Jan Steinbrener is an Associate Professor at the University of Klagenfurt's Institute for Intelligent System Technologies and serves as Vice Rector for Research and International Affairs, leading the Research Council. His roles include overseeing research strategy, international collaborations, and faculty research activities. Research Interests: Steinbrener focuses on robotics, artificial intelligence, and autonomous systems. His work emphasizes sensor systems, UAV navigation, and embedded AI applications. Projects include drone-based wind turbine inspection and AI-driven control systems for industrial robotics. Grants & Projects: He leads initiatives funded by the Austrian Research Promotion Agency (FFG), including 'Wind Turbine Blade Inspection Using Multimedia Drones' and 'Embedded AI - Shaping the Future of Power Electronics.' Collaborative projects with Carinthian Economic Development Fund and Infineon Technologies highlight his industry-academia partnerships. Academic Contributions: Steinbrener's research spans robotics, control systems, and sensor technology. Key publications include studies on UAV navigation algorithms, light-field imaging, and reinforcement learning applications in robotics.

• Robotics
• Navigation Systems
• Sensor Fusion
• +5 more

Chan Gook Park is a prominent researcher in robotics, navigation systems, and sensor fusion with an extensive publication record spanning over two decades. His work primarily focuses on visual-inertial odometry, LiDAR-based navigation, indoor positioning systems, and applications in both terrestrial and space environments. Dr. Park's research interests encompass robotics, sensor fusion, computer vision, and navigation systems. His work demonstrates a consistent focus on developing robust algorithms for visual-inertial odometry under challenging conditions, including low-light environments and indoor settings. He has made significant contributions to pedestrian navigation using wearable devices, lunar landing navigation systems, and infrared target detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward deep learning integration in traditional navigation systems, with increasing focus on event-based vision sensors, multi-sensor fusion techniques, and applications in wearable technology. His work spans both theoretical algorithm development and practical implementations for real-world navigation challenges. Dr. Park has mentored numerous researchers who frequently appear as first authors on his publications, including Jae Hong Lee, Hanyeol Lee, and In Ho Lee. His collaborative network extends across various Korean institutions, with frequent co-authorship patterns suggesting a well-established research laboratory focused on navigation and sensor systems. His research group appears to maintain strong connections with the aerospace and robotics communities, regularly publishing in top-tier IEEE conferences and journals including IEEE Transactions on Robotics, IEEE Access, and proceedings from ICRA and PLANS conferences. The laboratory seems particularly focused on practical implementations of navigation systems for both terrestrial and space applications.

Mert GÜRTÜRKView profile

Researcher

• Geospatial Engineering
• UAV Technology
• Photogrammetry
• +5 more

Mert GÜRTÜRK is a Researcher at Adiyaman University's Faculty of Engineering, specializing in geospatial technologies and UAV applications. He holds a Doctorate in Geodesy and Photogrammetry Engineering from Yildiz Technical University (2016-2019), along with a Master's degree (2014-2016) and Bachelor's degree (2007-2012) in related fields from the same institution. His research focuses on cutting-edge geospatial technologies including UAV photogrammetry, precise positioning systems, LiDAR applications, and machine learning integration in geospatial data processing. His work spans various applications from transportation infrastructure assessment to public health monitoring through geospatial analysis. GÜRTÜRK's recent publications demonstrate a strong trend toward integrating advanced computational methods with traditional geospatial techniques, particularly in UAV-based mapping systems and precise positioning technologies. His work shows significant contributions to accuracy assessment methodologies in GNSS processing and UAV georeferencing. 2016-2019: Doctorate in Geodesy and Photogrammetry Engineering, Yildiz Technical University 2014-2016: Master's in Geodesy and Photogrammetry Engineering, Yildiz Technical University 2007-2012: Bachelor's in Surveying Engineering, Yildiz Technical University His current research projects include the DEVELOPMENT OF DOMESTIC SOFTWARE FOR SWARM UAV DEMONSTRATIONS (2024-present) and SWARM UAV SYSTEM AND SOFTWARE FOR DEMONSTRATION PURPOSES (2021-2023). GÜRTÜRK has also contributed to the analysis of university campus digitalization processes and precise kinematic point positioning research. His technical expertise spans English language proficiency and extensive experience with geospatial software systems.

Sergio RapuanoView profile

Professor

• Instrumentation and Measurement
• Analog-to-Digital Conversion
• Biomedical Measurement Systems
• +2 more

Sergio Rapuano is a Full Professor in the Department of Engineering at the University of Sannio, Italy, specializing in Instrumentation and Measurement. With approximately 270 publications including journal papers, conference proceedings, book chapters, and IEEE standards, he coordinates nationally and internationally funded research projects focused on measurement systems and signal processing. His research spans analog-to-digital conversion characterization, biomedical measurement systems, distributed measurement networks, and telecommunications instrumentation. Recent work shows a pronounced shift toward compressed sensing applications for cable diagnostics, spectrum monitoring, and biomedical signal processing, reflecting his focus on efficient data acquisition in constrained environments. He also contributes significantly to distance learning methodologies in electrical measurement education. Prof. Rapuano actively shapes the field through IEEE Technical Committee 10 (Waveform Generation, Measurement, and Analysis), where he drives standardization efforts and conference organization for the Instrumentation and Measurement Society. His leadership in waveform measurement standards and traceability frameworks demonstrates his commitment to advancing measurement science globally.

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.

• 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

Oscar De SilvaView profile

Assistant Professor

• Robotics
• Control Systems
• Sensor Design
• +6 more

Dr. Oscar De Silva is an Assistant Professor in the Department of Mechanical Engineering at Memorial University of Newfoundland. He holds a B.Sc. from the University of Moratuwa, Sri Lanka, and a PhD from Memorial University. His expertise spans instrumentation, controls, mechatronics, and robotics, with a focus on sensor design, state estimation, and navigation systems. Dr. De Silva's research interests include state estimation, control systems, nonlinear dynamics, navigation systems, sensor design, localization, mapping, and intelligent prosthetics. He has contributed to projects such as ice detection systems for marine safety and computer vision for robotics applications. Before his current role, he worked as a research fellow at the American Bureau of Shipping-Harsh Environment Technology Centre and taught at Memorial University as a sessional instructor. His work emphasizes practical applications in autonomous systems, industrial inspection, and environmental monitoring. Notable achievements include the IMechE UK Award for Outstanding Achievement and a Gold Medal in Mechanical Engineering. His research trends focus on integrating AI, LiDAR, and radar technologies for navigation and safety systems in robotics and marine environments. Awards: IMechE UK Award for Outstanding Achievement Gold Medal in Mechanical Engineering (University of Moratuwa) Fellow of Graduate Studies (Memorial University) Advising and Grants: While no specific student names are listed, his role as an Assistant Professor involves academic supervision. His grants and collaborations likely align with his research in robotics and sensor systems, though specific details are not provided here. Labs: He is associated with the Intelligent Systems Lab at Memorial University and previously contributed to the ABS HETC. His work often involves multidisciplinary teams focused on real-world engineering challenges.