Find Academic Supervisors

• Automatic Speech Recognition
• Conversational User Interfaces
• Human-Computer Interaction
• +6 more

Prof. Hans-Peter Hutter is a Professor of Computer Science at the ZHAW School of Engineering, specializing in Deep Learning-based Automatic Speech Recognition, Conversational User Interfaces, and Human-Centered Computing. He leads the Human-Centered Computing research group at InIT/ZHAW and has held this position since 2005. His work focuses on accessibility technologies, mobile usability, and inclusive design for visually impaired users. Education: Dr. sc. techn. ETH in Computer Engineering (ETH Zurich, 1996) Dipl. El.-Ing. ETH in Electrical Engineering (ETH Zurich, 1986) Research Interests: Advancing accessibility in digital systems (e.g., accessible PDFs, navigation aids for visually impaired users) Speech recognition and dialogue systems Mobile application design principles Service engineering and platform development His recent work emphasizes multimodal interaction, accessible document remediation, and SLAM systems for navigation assistance. Projects: Leading the InCrowd-VI dataset project for indoor navigation Developing MathNet for mathematical expression recognition Creating accessible tourism services in Lake Constance region Grants & Labs: Active in EU-funded and industry collaborations, leading the InIT Institute founded in 2002. Collaborates with organizations like SwissICT and ACM.

• Computer Vision
• Deep Learning
• Medical Imaging
• +3 more

Dr. Mecocci Alessandro is a Full Professor at the Department of Information Engineering and Mathematical Sciences of the University of Siena . He specializes in computer vision, deep learning, and medical imaging, with a focus on applications in healthcare, robotics, and IoT systems. His research addresses challenges in automated diagnostics, real-time surveillance, and environmental monitoring. Teaching Roles : He leads courses such as Advanced Digital Image Processing (Master’s in Electronics & Communications Engineering) and Image and Multimedia Laboratory (Bachelor’s in Computer & Information Engineering). His academic activities emphasize practical training in computer vision and signal processing. Research Interests : His work spans deep learning , medical image analysis , UAV navigation , and IoT infrastructure . Notable projects include real-time ambulance detection systems, automated urine culture analysis, and smart waste management frameworks. Publications : Over 50 peer-reviewed articles highlight his contributions to fields like retinal imaging , glomeruli segmentation , and coastal management systems . Recent trends focus on integrating clinical data with AI for medical diagnostics and optimizing IoT sensor networks for environmental applications. Advising & Grants : While no specific grants are listed, his research collaborations span academic and industrial partners. He mentors students in advanced imaging techniques and IoT applications. Labs & Teams : His work is conducted within the University of Siena’s engineering departments, with contributions to cross-disciplinary projects in healthcare technology and robotics.

• Computer Vision
• Deep Learning
• Biomedical Imaging
• +4 more

Meng Xu is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Her research focuses on computer vision, deep learning, and biomedical imaging applications within robotics and healthcare domains. She contributes to advancing localization systems, image processing techniques, and neural network architectures for real-world challenges. Her work bridges theoretical advancements with practical implementations, such as end-to-end visual localization networks (e.g., Bev-locator) and unsupervised methods for biomedical image enhancement. She also explores multi-modal fusion strategies (e.g., MCAPR) and optimization techniques for efficient deep learning models. Key research directions include improving camera pose estimation accuracy, developing robust feature matching algorithms, and applying deep learning to body shape classification and human pose estimation. Her publications reflect a strong emphasis on solving technical challenges in both robotics and medical imaging through innovative computational approaches.

Kailai LiView profile

Assistant Professor

• Robotic Perception
• Sensor Fusion
• State Estimation
• +2 more

Kailai Li is a tenure-track Assistant Professor at the University of Groningen's Bernoulli Institute, where he leads the Agile Sensing and Intelligence Group (ASIG). His research develops novel methods for robotic perception, including continuous-time state estimation, sensor fusion, and visual navigation. Recent publications focus on Gaussian process representations for motion estimation and multi-robot collaboration using vision-language models. Dr. Li's lab maintains open-source projects like LiLi-OM (LiDAR-inertial odometry) and SFUISE (UWB-inertial fusion). Collaborations include Linköping University and industry partners. Current projects investigate trustworthy perception for autonomous systems under uncertainty and efficient representations for high-dimensional state estimation.

Sajad Saeedi GharahbolaghView profile

Assistant Professor

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Focal-plane Sensor-processor Arrays (FPSP)
• +6 more

Sajad Saeedi Gharahbolagh is an Assistant Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University and an Honorary Research Fellow at Imperial College London's Department of Computing. His research spans robotics, SLAM, focal-plane sensor-processor arrays (FPSP), and deep learning for autonomous systems. Education : PhD in Electrical and Computer Engineering (2014) from the University of New Brunswick. Prior Roles : Dyson Research Fellow (2018-2019) at Imperial College London; Postdoctoral Fellow at University of New Brunswick (2014); R&D Engineer at 2G Robotics (2015). His research focuses on Simultaneous Localization and Mapping (SLAM) for single/multi-robot systems Focal-plane Sensor-Processor Arrays (FPSP) for high-speed, low-power vision processing Autonomous aerial/underwater robotics Deep learning integration with traditional robotics algorithms Control systems for heterogeneous robotic platforms His work addresses challenges in computational efficiency, robustness in GPS-denied environments, and real-time multi-sensor data fusion. Recent publications highlight advancements in Distributed NeRF for collaborative mapping MR.CAP multi-robot control/planning BIT-VIO visual-inertial odometry WiFi-based geometric mapping FPSP-optimized CNNs PathBench benchmarking framework Scientific Awards : Dyson Research Fellowship (2018-2019) Best Robotics Paper (CRV 2021) Best Student Presentation (IROS 2023) Research Team : PhD Students: Christopher Kolios, Navid Zarrabi, Messiah Esfahani, Ishaan Mehta, Mahboubeh Asadi, Jack Saunders MASc Students: Georgia Jovanovic, Hussein Ali Jaafar, Austin Vuong, Roni Sherman, Matthew Lisondra, Glenn Shimoda, Ali Babaei, Robel Efrem, Messiah Ataey, Christopher Kolios, Nikolas Kourtzanidis Laboratory Facilities : Robotics and Computer Vision Lab (RCVL) with Vicon motion capture system 14 TurtleBot 3 platforms (Waffle Pi/Burger variants) Germicidal UVC-equipped G-Robots Jetbots with onboard GPU processing OpenMANIPULATOR robotic arms

KHOO Boo CheongView profile

Professor

• Fluid Mechanics
• Computational Fluid Dynamics
• Numerical Methods
• +7 more

Professor KHOO Boo Cheong is a faculty member in the Department of Mechanical Engineering at the National University of Singapore (NUS), affiliated with the College of Design and Engineering. He holds a BA (Hons.) from the University of Cambridge (1980), an MEng from NUS (1984), and a PhD from MIT (1989). His research focuses on Computational Fluid Dynamics (CFD) , Numerical Methods , and Fluid Mechanics , with applications to aerodynamics, turbulence, and fluid-structure interaction. Recent work emphasizes machine learning integration in CFD, bio-inspired UAV design, and icing mechanics for aerospace systems. Key research trends include advancing high-fidelity simulations for complex flows (e.g., turbulence modeling, cavitation suppression), optimizing fluid-structure systems (e.g., lightweight shells, train aerodynamics), and developing novel experimental techniques (e.g., light field PIV, wind spectra analysis). His contributions bridge theoretical CFD innovations with practical engineering challenges in transportation, renewable energy, and robotics. Professor Khoo collaborates on projects involving pressure gain combustion systems , ventilated supercavitating models , and quantum algorithms for fluid dynamics . His lab’s work often combines advanced numerical modeling with experimental validation, addressing real-world issues like wind effects on trains, ice accretion on airfoils, and autonomous navigation systems.

• Robotic Vision
• SLAM Systems
• 3D Reconstruction
• +1 more

Viorela Ila is Senior Lecturer at University of Sydney's School of Aerospace, Mechanical and Mechatronic Engineering. Her research develops perception algorithms for robotics, specializing in visual SLAM and 3D reconstruction. She contributes to the Centre for Robotics and Intelligent Systems. Research advances robust state estimation in dynamic environments. Recent work provides convergence guarantees for visual-inertial SLAM systems. Awarded Best Paper honors and MICINN/FULBRIGHT fellowship for contributions to robotic vision.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Jia Liu is a Professor at Tsinghua University's School of Information Science and Technology, Department of Computer Science, with a distinguished research career spanning multiple disciplines in computer science and engineering. Her extensive publication record demonstrates leadership in artificial intelligence, machine learning, and their applications across diverse domains. Dr. Liu's research interests encompass the development and application of advanced computational techniques to solve complex real-world problems. She has pioneered innovative approaches in neural network architectures, medical imaging analysis, remote sensing applications, and multi-agent systems. Her work on Asym-UNet and ThreeF-Net has significantly advanced breast lesion segmentation techniques, while her contributions to digital twin technology have enhanced nuclear reactor operations and Martian exploration systems. Analysis of Dr. Liu's recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications in healthcare, environmental monitoring, defense technology, and education. Her work demonstrates consistent innovation in algorithm development, particularly in adapting machine learning techniques to domain-specific challenges. Dr. Liu maintains an active research program with numerous collaborations across academia and industry. Her work on federated learning for dual carbon initiatives reflects her commitment to addressing global sustainability challenges through technological innovation. As an educator, Dr. Liu has developed innovative teaching methodologies that integrate virtual reality and game-based learning, particularly in language education. Her research on interactive e-learning environments demonstrates her dedication to improving educational outcomes through technology.

• Computer Vision
• Robotics
• 3D Reconstruction
• +3 more

Luis Puig is an academic researcher specializing in computer vision and robotics, focusing on omnidirectional imaging systems, 3D reconstruction, and sensor calibration. His work bridges theoretical advancements with practical applications in navigation assistance for visually impaired individuals, intelligent tutoring systems, and multi-camera system integration. Key contributions include: Pioneering research in omnidirectional vision systems, including calibration techniques and feature extraction. Development of algorithms for 3D tracking, deformable surface modeling, and RGB-D sensor fusion. Advances in visual SLAM (Simultaneous Localization and Mapping) and stereo visual odometry. His interdisciplinary approach also extends to educational technology, designing adaptive learning systems for mathematical problem-solving. Over 23 publications in top venues like IEEE Trans. PAMI, CVIU, and ICRA reflect his expertise in both technical and applied domains.

Andry Maykol PintoView profile

Assistant Professor

• Robotics
• Autonomous Vehicles
• Machine Learning
• +5 more

Andry Maykol Pinto is a Senior Researcher at the Center for Robotics and Autonomous Systems at INESC TEC and an Assistant Professor at the Faculty of Engineering of the University of Porto. He earned a Ph.D. (2014) and M.Sc. (2010) in Electrical and Computer Engineering from the University of Porto, focusing on Robotics. Research Interests: His work spans Robotics, Autonomous Vehicles, Machine Learning, Underwater Imaging, 3D Reconstruction, and Marine Robotics. He develops advanced perception systems for UAVs and AUVs, focusing on multimodal data fusion, visual transformers, and reinforcement learning to address navigation and reconstruction challenges in offshore and underwater environments. Supervised Theses (2023): Eduardo Jorge Pereira Gonçalves: Deep learning for visual odometry in ASVs Daniel Filipe Barros Campos: Multi-domain awareness for offshore wind farm inspection João Afonso Borges Carvalho: AI for marine growth classification Francisco Soares Pinto da Silva Neves: RL-based UAV inspection of windmills João Basto do Rosário: Ego motion analysis from video data Gabriel da Silva Martins Loureiro: Edge intelligence for seafloor perception João Francisco Sengo Guimarães: Machine vision for deep-space images Maria Inês Rodrigues Pereira: Multi-agent RL for maritime perception João Rodrigues Mina: Quality assessment for underwater imaging Rafael Marques Claro: Robust UAV landing perception Contact: andry.m.pinto@inesctec.pt | +351228340554

Raimund EdlingerView profile

Researcher

• Robotics
• Autonomous Systems
• Rescue Robotics
• +2 more

Raimund Edlinger is a research-focused academic at the University of Applied Sciences Upper Austria, Wels campus, affiliated with the Research Center Wels. He leads the Center of Excellence for Automotive/Mobility and Smart Production, with specialized focus areas in Digital Transformation, Rail Automation, Telematics, and Smart Automation and Robotics. His interdisciplinary work bridges engineering and practical robotics applications across diverse environments. Edlinger's research centers on advancing robotics for real-world challenges, particularly in GNSS-denied scenarios and hazardous environments. His expertise spans autonomous navigation, sensor fusion (LiDAR, thermal imaging, tactile systems), and human-robot interaction, with significant contributions to rescue robotics, underground mapping, and space analog missions. Recent work emphasizes digital twin applications for Mars simulations and tactile terrain assessment to enhance robot mobility in complex terrains. Analysis of his publication trends reveals a strategic shift toward multi-sensor integration for reliability in adverse conditions (e.g., smoke, narrow tunnels, Mars analogs). His 2022-2025 output shows concentrated effort in practical deployment challenges: casualty evacuation systems, nuclear disaster response, and cost-effective sensing solutions, reflecting strong industry and emergency response collaboration. His competitive robotics achievements include: 1st place EnRicH 2021 (Manipulation category) Multiple RoboCup podium finishes (2015-2022) in Rescue League and Best-in-Class Manipulation German Open championship recognitions across five consecutive years Edlinger directs significant research initiatives including the active MOBES project (Mobile Erdstall Scanner, 2025-2026), MARK Racing Team, and Autility platform development, funded by Austria's Climate and Energy Fund and ICT programs. He supervises student research while contributing to national robotics strategy through activities like ÖBFV Führungsseminar workshops on digitalization. His work is operationalized through the RRT-Roboto-Racing Team and specialized labs within Research Center Wels, featuring underground structure simulators, Mars analog testbeds, and sensor calibration facilities for extreme environments. Current projects integrate 5G communication and AI-driven manipulation for next-generation rescue systems.

• Robotics
• Autonomous Systems
• Control Systems
• +5 more

Jonay Tomás Toledo Carrillo is a Full Professor at the University of La Laguna, affiliated with the Department of Computer Science and Systems Engineering within the School of Computer Science and Systems Engineering. He leads the GRULL (Robotics Group of the University of La Laguna), focusing on robotics, control systems, and autonomous systems. He earned his PhD in 2008 with a thesis on nonlinear control strategies for quadrotor helicopters, advised by Dr. Leopoldo Acosta Sánchez. His research interests span robotics, autonomous navigation, sensor fusion, and assistive technologies. Key projects include developing mobility aids for visually impaired individuals and enhancing localization systems through adaptive algorithms. Recent work emphasizes real-time sensor processing (e.g., LSTM networks for odometry), magnetic field control for accessibility, and Kalman filter variants for multi-sensor integration. Publications highlight contributions to robotics applications, including autonomous vehicles, wheelchair navigation, and BCI systems. His work bridges theoretical control methods with practical implementations in unstructured environments. Collaborations include interdisciplinary efforts in computer vision, ontology-based navigation, and educational simulators like MNEME for memory hierarchy teaching. Dr. Toledo Carrillo’s research aligns with the university’s engineering and automation priorities, with a focus on real-world impact through low-cost, high-precision solutions. His team’s innovations address challenges in both robotics engineering and accessibility technologies.

• Neuromorphic Computing
• Neural Dynamics
• Robotics
• +4 more

Dr. Yulia Sandamirskaya is the Head of the Research Center 'Cognitive Computing in Life Sciences' at Zurich University of Applied Sciences (ZHAW), where she leads a research group focused on neuromorphic cognitive architectures for embedded AI systems. Her work integrates neural dynamics with robotic control , spanning real-time tasks such as sensing, planning, decision-making, learning, and motor control for assistive robots. She collaborates extensively with institutions like ETH Zurich and KTH Royal Institute of Technology, utilizing neuromorphic hardware such as dynamic vision sensors (DVS) and robotic platforms. Her research encompasses neuromorphic architectures for obstacle avoidance, path integration, and sequence learning, as well as theoretical frameworks like Dynamic Neural Fields (DNFs) for cognitive modeling. She explores autonomous learning mechanisms, including unsupervised and reinforcement learning, and applies these to robotics in domains such as spatial language grounding and haptic exploration . Her group has published extensively on topics like event-based vision , resonator networks , and hardware implementations of neural algorithms. Dr. Sandamirskaya has supervised multiple MSc theses on neuromorphic systems and robotic applications, with advisees working on projects like UAV obstacle avoidance, tactile object recognition, and neural-dynamic sequence generation. She actively contributes to workshops and conferences, including Science Robotics , CVPR , and IEEE conferences , and her work addresses challenges in low-power, high-speed robotic agents operating in real-world environments.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +5 more

Zhe Xu is an Assistant Professor in the Department of Computer Science at the Hong Kong University of Science and Technology's School of Engineering. His research spans multiple interdisciplinary domains with a strong focus on artificial intelligence applications in medical imaging, computer vision, and robotics. Dr. Xu's research interests center on medical image analysis, computer vision, and machine learning with applications spanning medical diagnostics, robotics, and natural language processing. His work demonstrates particular expertise in developing novel deep learning architectures for medical image segmentation, domain adaptation techniques for cross-domain medical applications, and multimodal AI systems that bridge vision and language understanding. His recent publications show increasing interest in large language model applications for medical reasoning and report generation. Analysis of Dr. Xu's publication trends reveals a strong emphasis on medical AI applications, with approximately 40% of his recent work focused on medical image analysis and diagnostics. Another significant portion (around 30%) addresses computer vision challenges, particularly in object detection and image segmentation. His more recent work (2024-2025) shows a growing interest in multimodal large language models and their application to medical reasoning tasks. Active participant in major medical imaging conferences including MICCAI Regular contributor to IEEE Transactions on Medical Imaging Collaborates extensively with medical researchers and clinicians Recipient of multiple research grants supporting AI for healthcare initiatives Dr. Xu leads a research group focusing on AI for healthcare, with several PhD students working on medical image analysis projects. His lab maintains strong collaborations with hospitals and medical research institutions in Hong Kong and internationally. Current research directions include developing foundation models for medical imaging, creating AI systems for automatic radiology report generation, and exploring the application of large language models in clinical decision support.

• Multi-Robot Systems
• Robotic Swarms
• Autonomous Technologies
• +5 more

Spring Berman is an Associate Professor of Aerospace and Mechanical Engineering at Arizona State University (ASU), with affiliations in the School for Engineering of Matter, Transport and Energy, the Global Security Initiative, and the Center for Human, Artificial Intelligence, and Robot Teaming. She also holds an adjunct professorship in the Sandra Day O’Connor College of Law. Berman’s research focuses on multi-robot systems, swarm robotics, and biologically-inspired control strategies. She earned her Ph.D. (2010) and M.S. (2008) in Mechanical Engineering from the University of Pennsylvania and a B.S.E. (2005) in Mechanical and Aerospace Engineering from Princeton University. Her work includes grants from DARPA, NSF, and industry partnerships. She has led projects on swarm robotic control frameworks, boundary coverage, and cooperative manipulation. Berman teaches advanced engineering courses on system dynamics, control theory, and research methodologies. Her recent publications emphasize decentralized control strategies, swarm intelligence, and ethical considerations in autonomous systems. Her research activities span robotic swarm dynamics, autonomous vehicle testbeds, and human-swarm interaction. Key collaborations include projects on stochastic coverage strategies and multi-agent systems. Berman’s contributions bridge robotics, control theory, and societal applications through interdisciplinary initiatives.