Thomas Haslwanter is a Professor at the University of Applied Sciences Upper Austria , affiliated with the Linz Center of Excellence for Medical Engineering/TIMed Center . His research focuses on biomedical engineering, particularly in 3D kinematics, prosthetic development, and sensor-based motion analysis. Key projects include FeeL (Feedback for Leg Protheses) and TC-Sturztraining-Assessment , combining engineering with medical applications. Research Interests : Biomedical signal processing, inertial measurement unit (IMU) applications, ocular motor research, and 3D motion analysis. Technical Contributions : Developed wearable systems for exercise supervision, automatic classification algorithms for fitness movements, and advanced prosthetic feedback mechanisms. Recent Publications highlight trends in Python-based signal analysis, visual rehabilitation for macular degeneration, and IMU integration in physical activity tracking.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۱۵۱-۱۶۵ از ۲۰۸ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Xin Suمشاهده پروفایل
پژوهشگر
- Computer Vision
- Machine Learning
- Visual SLAM
- +۳ مورد دیگر
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.
Xiaofan Jiangمشاهده پروفایل
دانشیار
Xiaofan (Fred) Jiang is an Associate Professor of Electrical Engineering at Columbia University and an Affiliate in the Computer Science Department. He serves as Co-Chair of the Smart Cities Center at the Data Science Institute and Vice Chair of ACM SIGEnergy. His research spans intelligent embedded systems, mobile/wearable computing, Internet of Things, and connected health. Education B.Sc. (2004), M.Sc. (2007), Ph.D. (2010) in Electrical Engineering and Computer Science from UC Berkeley His work focuses on City-scale energy footprint tracking with real-time analytics Privacy-preserving wearable systems for urban safety AI-driven thermal comfort estimation in buildings Magnetic-based indoor localization Smart plug meters and energy analytics LLM integration for medical diagnostics Recent publications highlight trends in Reconfigurable drone systems for physical-digital interfaces Transformer architectures for speech and sensor data Generative AR for sensor data visualization Multi-modal physiological data analysis Programless smart home interfaces Scientific Awards NSF Graduate Fellowship Vodafone-US Foundation Fellowship NSF CAREER Award Students Jingping Nie (Ph.D., UNC Chapel Hill faculty from 2025) Minghui (Scott) Zhao (Ph.D. candidate, ACM SRC winner) Qijia Shao (Ph.D., now at HKUST) and active lab members contributing to ACM HumanSys, SenSys, and MobiCom publications.
- Robotics
- Computer Vision
- Embedded Systems
- +۳ مورد دیگر
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 Ranaمشاهده پروفایل
پژوهشگر ارشد
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.
Lagoudakis Michael is a Professor at the School of Electronic & Computer Engineering of the Technical University of Crete since 2005. His academic journey includes a PhD in Computer Science from Duke University (2003), a Master's from the University of Louisiana, Lafayette (1998), and a Diploma in Computer Engineering and Informatics from the University of Patras (1995). Education: PhD in Computer Science, Duke University, 2003 MSc in Computer Science, University of Louisiana, Lafayette, 1998 Diploma in Computer Engineering and Informatics, University of Patras, 1995 Research interests span Machine Learning, Reinforcement Learning, Decision Making under Uncertainty, Multi-Agent Systems, Robotics (particularly robotic team coordination), Complex Systems, and DNA Computing. His work has produced significant publications in top venues like Robotics: Science and Systems , IEEE IROS , Journal of Machine Learning Research , and NIPS . He has contributed to projects such as EURECA-PRO (2020–present), DialogRL (2016–2020), and LSPI (2000–2003). Scientific awards include the Outstanding Dissertation Award from Duke University's Department of Computer Science and the Outstanding Teaching Assistant Award received twice. He is a member of AAAI, IEEE, and ACM.
Armin Alaghiمشاهده پروفایل
استادیار
- Embedded Systems
- Digital Circuits
- Mathematics
- +۷ مورد دیگر
Armin Alaghi serves as a Research Scientist at Oculus Research (Redmond, WA) and holds an Affiliate Assistant Professor position at the University of Washington. His dual affiliation enables valuable knowledge transfer between cutting-edge industrial research and academic pursuits in computer systems engineering. Dr. Alaghi's research spans the intersection of embedded systems, digital circuits, and mathematics. His primary focus involves building low-power augmented reality (AR) and virtual reality (VR) systems while developing novel computation methods for unreliable beyond-CMOS technologies. His previous research contributions include significant work in stochastic computing (where he developed the STRAUSS synthesis methodology), reliable Network on chip (NoC) design, FPGA testing methodologies, NoC testing techniques, artificial neural networks implementations, asynchronous circuit design, and multi-valued logic systems. He has made his spectral-transform-based synthesis tool publicly available on GitHub, demonstrating commitment to open research. Analysis of Dr. Alaghi's publication record reveals a clear research trajectory from foundational circuit-level work toward practical applications in AR/VR systems. His publications from 2020-2025 demonstrate expertise spanning computer architecture, security for immersive technologies, neural network compression techniques, and homomorphic encryption methods. A recurring theme throughout his work is the exploration of quality-energy tradeoffs and error-resilient computing approaches, with increasing focus on security aspects of AR/VR systems in his most recent work. Dr. Alaghi maintains active connections with the broader research community, as evidenced by his Erdős number of 3 (Armin Alaghi John P. Hayes Frank Harary Paul Erdős) and his ongoing contributions to open-source research tools. His GitHub repository for stochastic computing synthesis shows community engagement with multiple contributors. At Oculus Research, Dr. Alaghi applies his theoretical expertise to practical challenges in next-generation AR/VR system development. His work bridges academic research with real-world product development, particularly in addressing energy efficiency challenges for wearable computing platforms through innovative circuit design approaches.
Marco Camurriمشاهده پروفایل
دانشیار
- Robotics
- Autonomous Systems
- Sensor Fusion
- +۶ مورد دیگر
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.
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.
Zoran Sjanicمشاهده پروفایل
دانشیار مدعو
- Sensor Fusion
- Visual-Inertial SLAM
- Optical Flow
- +۷ مورد دیگر
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 Vödischمشاهده پروفایل
پژوهشگر
- Continual Learning for Robotics
- Machine Perception
- Simultaneous Localization and Mapping (SLAM)
- +۳ مورد دیگر
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.
Michele Taragna is a Tenured Associate Professor in the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, actively teaching across degree programs: Experimental Modeling for PhD students in Electrical, Electronic and Communications Engineering (2019-2025), Estimation and System Identification for Mechatronic Engineering Master's program (2019-2026), and Automatic Control for Computer Engineering Bachelor's program (2019-2026) as course holder or collaborator. His research centers on Systems and Control Engineering , with primary interests in data-driven control for autonomous vehicles and fleets, direct virtual sensors, and machine learning-enhanced system identification. Key areas include Set Membership methods for robustness under bounded noise, computational complexity reduction in Nonlinear Model Predictive Control (NMPC), sensor fusion for robotics, and applications in automotive suspensions. This work aligns with ERC sectors PE7_1 (Control engineering), PE1_20 (Control theory), and PE6_12 (Scientific computing). Trends in his publications (2024-2004) reveal sustained innovation in applying Set Membership identification to NMPC for autonomous vehicles, achieving real-time feasibility through search domain reduction. Sensor fusion techniques using Kalman filters for mobile manipulators and data-driven filter design for uncertain LTI systems with bounded noise are recurring themes, emphasizing practical implementation and computational efficiency. Scientific awards: None documented in provided materials. Advising and research funding: Supervised PhD student Mattia Boggio (2020-2024) in Electrical, Electronic and Communications Engineering; thesis on Real-time Nonlinear Model Predictive Control with domain reduction. Led the nationally funded PRIN project Controllo ad alte prestazioni a partire dai dati sperimentali (2007-2009) as Scientific Responsible. He is a core member of the Automatica research group within DET, focusing on system identification, control design, and validation for dynamic systems with applications in automotive and robotics domains.
- Computer Vision
- Robotics
- Artificial Intelligence
- +۵ مورد دیگر
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.
- Autonomous Systems
- Machine Learning
- Robotics
- +۹ مورد دیگر
Alexander Amini is a Postdoctoral Researcher at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) , working under the guidance of Prof. Daniela Rus. He earned his PhD (2022), Master of Science (2018), and Bachelor of Science (2017) in Computer Science from MIT, with a minor in Mathematics. Amini co-founded Liquid AI and Themis AI , and serves as a lead organizer and lecturer for MIT's deep learning course 6.S191 . His research focuses on the science and engineering of autonomy , particularly for safe decision-making in uncertain environments. Key contributions include Developing end-to-end control systems for autonomous agents Formulating confidence metrics in neural networks Creating mathematical models for human mobility analysis Innovating inertial refinement systems Amini's publications reveal trends across autonomous vehicle control , robust machine learning , and uncertainty quantification . His work spans continuous-time neural models, sensor optimization for soft robotics, and applications in molecular discovery and financial modeling. Notable collaborations include projects with NVIDIA and Harvard Medical School. Scientific recognition includes 2011 EU Contest for Young Scientists Grand Prize 2011 BT Young Scientist overall winner 2017 NSF Graduate Fellowship 2020 MIT Outstanding Mentor Award 2021 JP Morgan Fellowship As part of MIT's Distributed Robotics Laboratory , Amini works on systems that combine robotics with everyday life applications, supported by grants from FinTech@CSAIL and MachineLearningApplications@CSAIL for bias mitigation in financial and clinical domains.
- Robotics
- Machine Vision
- Industrial Automation
- +۴ مورد دیگر
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