جست‌وجوی استاد و پژوهشگر

• Neuroscience
• Olfactory Systems
• Sensory Processing
• +۳ مورد دیگر

Dr. Katrin Vogt is a researcher at the University of Konstanz, specializing in neuroethology and sensory systems. Her work focuses on understanding recurrent neural circuits governing state-dependent behavior, particularly in olfactory systems of Drosophila larvae. She leads a subproject investigating hunger-dependent serotonergic modulation in the antennal lobe, collaborating with a doctoral student. Her research bridges vertebrate and invertebrate models to identify conserved network principles across species. Dr. Vogt is part of a DFG-funded interdisciplinary team examining recurrent circuits' roles in flexible behavioral responses to environmental changes. Her research interests include neural circuitry modulation, sensory integration, and behavioral plasticity. Key projects involve analyzing how sensory inputs are modulated by internal states like hunger, and how recurrent connections enable adaptive responses. She has contributed to studies on visual and olfactory memory formation, multisensory integration, and navigational strategies in Drosophila. Publications highlight work on dopamine signaling in taste punishment, social behavior in larvae, and cross-species odor coding principles. Her findings aim to uncover fundamental mechanisms underlying sensory-driven behavior and neural plasticity in both vertebrates and invertebrates.

• Computer Vision
• Machine Learning
• General AI
• +۶ مورد دیگر

Dr. Maya Aghaei is a Lecturer and Researcher in Computer Vision & Data Science at NHL Stenden University of Applied Sciences, part of the Academy Technology & Innovation. She holds a M.Sc. in Artificial Intelligence and a Ph.D. in Computer Vision from the University of Barcelona. Her academic role includes supervising Minor and Master students while focusing on applying cutting-edge AI techniques to real-world challenges. Prior to her current position, she served as a Postdoctoral Researcher at the Italian Institute of Technology, developing AI solutions for industrial applications. Her research spans Computer Vision, Machine Learning, and General AI with a focus on surveillance systems, autonomous drones, hyper-spectral imaging for environmental analysis, and social signal processing through egocentric data. Notable projects include crime scene classification via trajectory analysis, obstacle detection for BVLOS drones, and psychological trait prediction based on clothing analysis. Dr. Aghaei's work emphasizes real-world applicability, bridging theoretical advancements with practical implementations in industries like agriculture, waste management, and public safety. Her interdisciplinary approach combines technical innovation with societal relevance, addressing challenges from plastic recycling to social distancing compliance through computer vision systems.

• Cyber-Physical Systems
• Machine Learning
• Autonomous Systems
• +۵ مورد دیگر

Radu Grosu is a Professor at Technische Universität Wien (TU Wien), leading the Forschungsbereich Cyber-Physical Systems . His research focuses on Cyber-Physical Systems (CPS), Machine Learning, and autonomous robotics, with notable contributions to neural network architectures like Liquid Time-Constant Networks (LTC) and their applications in robotics and medical imaging. He is affiliated with the Network Lab and has supervised numerous PhD and Master's students, including Sebastian Michael Bittner, Daniel Scheuchenstuhl, and Sophie Neubauer. His work spans topics such as reinforcement learning, autonomous driving, and IoT ecosystems. Recent projects include developing robust AI systems for healthcare and robotics, such as tumor delineation using PET imaging and neuromorphic IoT architectures for smart villages. Grosu has published extensively on CPS, with over 146 contributions across peer-reviewed journals and conferences. His research emphasizes bridging theory and practice, addressing challenges in safety, scalability, and real-time control in autonomous systems. Key research interests include robotic perception, neural network robustness, and CPS/IoT integration. He has pioneered methods like DeepSTL for translating temporal logic requirements into neural network training objectives and developed frameworks like NimbleAI for neuromorphic sensing-processing systems. His team also explores distributed control algorithms for multi-agent systems, such as flocking drones and formation control using relative distance measurements. Recent work examines the generalization properties of deep filters in CNNs and quantum-classical reinforcement learning models for game AI. Grosu has advised over 20 students on topics ranging from deep learning in wafer defect analysis to bio-inspired neural circuits for auditable autonomy. His lab collaborates on interdisciplinary projects, such as applying AI to battery health estimation and prostate cancer diagnostics. He actively contributes to academic communities, editing special issues on AI in healthcare and CPS resilience, and has organized summer schools on CPS and IoT systems.

Philip Damesمشاهده پروفایل

دانشیار

• Robotics
• Probabilistic Robotics
• Multi-Robot Systems
• +۷ مورد دیگر

Philip Dames is an Associate Professor in the Department of Mechanical Engineering at the College of Engineering, Temple University, where he leads the Temple Robotics and Artificial Intelligence Lab (TRAIL). His research focuses on enabling robots to operate effectively in complex, real-world environments to meet societal needs. Research Interests: His work spans robotics, probabilistic reasoning, multi-robot systems, active sensing, mapping, and target tracking. He develops algorithms for distributed coordination, uncertainty-aware navigation, and semantic perception, with applications in autonomous systems and human-robot interaction. Publication Trends: His recent publications (2020–2025) appear in top robotics journals like IEEE Transactions on Robotics and Autonomous Robots . The research emphasizes probabilistic methods, deep reinforcement learning, large language models for planning, and real-time navigation in dynamic and uncertain environments, reflecting a strong trend toward intelligent, adaptive robotic systems. Scientific Awards: NSF CAREER Award Advising and Grants: While specific advisees are not listed, he leads a research lab and has secured competitive funding such as the NSF CAREER award. He mentors students through research in robotics and advises on projects related to autonomous navigation and multi-robot systems. Labs and Teams: He directs the Temple Robotics and Artificial Intelligence Lab (TRAIL), which focuses on developing advanced robotic capabilities through collaborative, interdisciplinary research in perception, planning, and control.

Federico Boniardiمشاهده پروفایل

پژوهشگر

• Robotics
• Mobile Robot Navigation
• Localization and Mapping
• +۶ مورد دیگر

Federico Boniardi is a researcher at the Autonomous Intelligent Systems group in the Department of Computer Science at Albert-Ludwigs-Universität Freiburg, Faculty of Engineering. He holds advanced degrees in Mathematics and Artificial Intelligence and has been actively contributing to robotics research since 2014. Bachelor’s in Mathematics, University of Milano (2006–2010) Master’s in Mathematics, University of Milano (2010–2012) MSc in Artificial Intelligence, University of Edinburgh (2013–2014) PhD Research Assistant, University of Freiburg (2014–2019) His research centers on mobile robot navigation, focusing on robust localization and mapping in indoor environments using LiDAR and architectural or hand-drawn floor plans. He explores long-term autonomy and human-robot interfaces, particularly in sketch-based map interpretation and shared control systems. His work integrates sensor calibration, topometric mapping, and deep learning for room layout extraction. The most recent publications reflect a strong trend in leveraging CAD and sketched maps for robot localization, combining probabilistic filtering, graph-based optimization, and semantic understanding. His contributions span key robotics venues such as IEEE/RSJ IROS, IEEE ICRA, and Robotics and Autonomous Systems. Scientific Contributions: Developed pose graph-based localization for long-term navigation Advanced LiDAR-based localization in architectural plans Explored Bayes filters for shared autonomy Enabled robot navigation using hand-drawn sketch interfaces Federico has served as a Teaching Assistant for Theoretical Computer Science and supervised student seminars on robot navigation. He has contributed to EU-funded research projects including RobDREAM and SQUIRREL, focusing on robot performance optimization and clutter management. He is based in Freiburg, Germany, and continues active research in autonomous intelligent systems.

Shaohui Foongمشاهده پروفایل

دانشیار

• Robotics
• Aerospace Engineering
• Control Systems
• +۴ مورد دیگر

Shaohui Foong is an Associate Professor in the Engineering Product Development (EPD) pillar at Singapore University of Technology and Design (SUTD). He previously served as a Visiting Assistant Professor at MIT's Department of Mechanical Engineering (2011). His research focuses on innovative robotics and UAV systems, medical technology, and design-centric pedagogy. Core projects include magnetic localization for medical devices, nature-inspired aerial robotics (e.g., THOR hybrid UAV), and competitive engineering design activities like Designettes. Research interests span biomimetic flight systems, autonomous navigation, and interdisciplinary educational frameworks. Notable innovations include the Transformable HOvering Rotorcraft (THOR), which merges fixed-wing and rotor-wing capabilities, and magnetic localization for nasogastric tube tracking. His work emphasizes practical applications in healthcare, infrastructure inspection, and environmental monitoring. Teaching emphasizes ethical engineering practice and hands-on design. He actively mentors students across PhD, master’s, and undergraduate levels, offering research opportunities in robotics, control systems, and medical technology. Collaborative projects with industry and academic partners are encouraged, with a focus on sustainability and technological innovation. Current research trends reflect a blend of bio-inspired design, advanced control algorithms, and cross-disciplinary problem-solving. Recent publications highlight advancements in monocopter dynamics, hybrid UAV architectures, and sensor integration for medical and industrial applications.

Jennifer Nelsonمشاهده پروفایل

مدرس ارشد

• Virtual Reality
• Human-Machine Interaction
• Human Movement Behavior
• +۳ مورد دیگر

Jennifer Nelson is a Senior Lecturer in the Department of Earth, Atmospheric, and Planetary Sciences at Purdue University, located in West Lafayette, Indiana. Her office is situated in HAMP 4284. She is affiliated with the College of Science and maintains active research in human-virtual interaction systems. Her research focuses on understanding how humans interact with virtual environments, particularly in crowded scenarios. Key themes include analyzing avoidance behaviors in response to virtual characters, studying the effects of rendering styles on human movement, and developing frameworks to model human-crowd interactions in immersive virtual reality settings. Recent work emphasizes the impact of virtual character dynamics (speed, density, direction) on human navigation patterns, as well as perceptual factors like gesture recognition and proximity effects. Her studies often involve creating experimental frameworks to quantify behavioral responses under varying virtual conditions. No scientific awards or grants are explicitly listed in the provided materials. While no advising information is available, her publications suggest involvement in collaborative research teams focused on virtual reality applications. She contributes to Purdue's initiatives in computational behavioral studies and immersive technology development.

• Robotics Perception
• Alternative Sensing
• Inertial Fusion
• +۶ مورد دیگر

Teresa Vidal-Calleja is an Associate Professor and Research Director at the Robotics Institute, University of Technology Sydney (UTS). She holds a Deputy Head of School (Research) role in the School of Mechanical and Mechatronic Engineering. Her research focuses on robotics perception, alternative sensing, inertial fusion, SLAM, and applications in manufacturing, agriculture, mining, and construction. Education: B.Sc. Mechanical Engineering (UNAM, 2000), M.Sc. Electrical Engineering (CINVESTAV-IPN, 2002), Ph.D. Automatic Control (Polytechnic University of Catalonia, 2007). Postdoctoral Experience: LAAS-CNRS (France), Australian Centre for Field Robotics (Sydney). Research Interests: Teresa’s work spans robotics perception, sensor fusion, autonomous navigation, and collaborative robotics. She leads projects on next-gen infrastructure robotics (ARC Discovery Grant) and is co-lead of the Australian Cobot Centre. Her innovations target hazardous environments, infrastructure maintenance, and human-robot collaboration. Funded Research: Includes grants for robotic sensors, satellite docking systems, and multi-robot collaboration. Recent projects involve Navantia, the NSW Space Research Network, and industry partnerships. Professional Roles: IEEE Senior Member, Treasurer of the Australian Robotics and Automation Association, and Associate Editor for IEEE Transactions on Robotics and other journals. She chairs conferences like the Australasian Conference on Robotics and Automation. Labs/Teams: UTS Robotics Institute, collaborating with global institutions like ETH Zürich and DLR. Her work integrates robotics with industries such as agriculture and construction, emphasizing sustainability and safety.

Won Dong Shinمشاهده پروفایل

پژوهشگر

• Robotics
• Unmanned Aerial Vehicles (Drones)
• Vision-Based Control
• +۵ مورد دیگر

Won Dong Shin is a Postdoctoral Researcher at the Laboratory of Intelligent Systems (LIS) within the School of Engineering (STI) at EPFL. His research focuses on bio-inspired robotics, particularly in the design and control of drones and multi-modal robots. He explores topics such as avian-inspired flight mechanisms, morphological control systems, and autonomous navigation strategies. His work integrates vision-based control, biomimetic principles, and reinforcement learning to enhance the agility and accuracy of winged drones in urban environments. He has contributed to developing robots capable of transitioning between ground and air, perching, and adapting to complex terrains through energy-efficient designs. Selected projects include studies on elastic actuation for repetitive hopping, multi-modal locomotion systems, and bio-inspired claws for perching. His research often emphasizes practical applications in robotics for civilian tasks, such as urban exploration and environmental monitoring. Shin collaborates with the LIS group, which is part of EPFL’s Institute of Microengineering (IGM). His contact information includes the email won.shin@epfl.ch and a physical office at MED 1 1612 in Lausanne.

Andrew Heinمشاهده پروفایل

استادیار

• Computational Biology
• Theoretical Ecology
• Behavioral Ecology
• +۳ مورد دیگر

Andrew Hein serves as Assistant Professor in the Department of Computational Biology at Cornell University's College of Agriculture and Life Sciences (CALS), where his research bridges behavior, ecology, and neurobiology through computational frameworks. His lab investigates how organisms process environmental information and how these mechanisms scale to influence ecological interactions and ecosystem dynamics, with significant emphasis on developing AI-driven tools for behavioral data acquisition. Hein earned his PhD in theoretical and computational biology from the University of Florida under Jamie Gillooly (Biology) and Scott McKinley (Mathematics), followed by a James S. McDonnell postdoctoral fellowship at Princeton University with Simon Levin and Iain Couzin. From 2016-2022, he founded and directed the Ecology of Information Lab jointly operated by NOAA's Santa Cruz Laboratory and UC Santa Cruz' Institute of Marine Sciences. His research focuses on computational models of information flow in biological systems, particularly examining how socially transmitted misinformation spreads in wildlife, behavioral responses to environmental change, and neural mechanisms underlying collective behavior. The lab pioneers computer vision applications for ecological monitoring and integrates fluid dynamics with machine learning to decode animal migration strategies, emphasizing real-world data applicability. Recent publications demonstrate a trajectory toward AI-enhanced ecological research, with studies in PNAS and Current Biology addressing misinformation suppression in animal groups, machine learning transparency for marine science, and behavioral feedback loops in changing ecosystems. This work reveals growing interdisciplinary convergence between computational methods and ecological theory. Hein's scientific recognition includes: Presidential Early Career Award for Scientists and Engineers (2025) NSF CAREER award (2024) US National Academy of Science Cozzarelli Prize James S. McDonnell Postdoctoral fellowship National Science Foundation IGERT fellowship National Science Foundation graduate research fellowship The Hein Lab actively mentors graduate and undergraduate researchers through Cornell's Computational Biology program, with current projects funded by multiple agencies: NSF Rules of Life grant on "multivalent" biological interactions NOAA initiative developing underwater machine vision tools NSF research on attack/evasion behavior robustness Based in Cornell's Atkinson Hall, the Hein Lab operates as a collaborative hub where biologists, computer scientists, and engineers develop computational frameworks to decode behavioral algorithms in natural systems, with ongoing work expanding AI applications for ecological forecasting and conservation.

Jonathan Gammellمشاهده پروفایل

استادیار

• Autonomous Systems and Control
• Robotics
• Artificial Intelligence
• +۴ مورد دیگر

Jonathan Gammell is an Assistant Professor at Queen's University's Department of Electrical and Computer Engineering and a member of the Ingenuity Labs Research Institute. He holds an adjunct fellowship at the Oxford Robotics Institute (University of Oxford), where he previously taught. His expertise spans autonomous systems, robotics, and AI, with a focus on motion planning for diverse applications, including medical devices, self-driving cars, and aerial robotics. He leads the Estimation, Search, and Planning (ESP) group, which develops algorithms for autonomous systems. Educations: BASc in Mechanical Engineering and Physics from the University of Waterloo MASc and PhD in Robotics from the University of Toronto Institute for Aerospace Studies Research Interests: Dr. Gammell's work addresses fundamental motion planning challenges in robotics, emphasizing asymptotically optimal algorithms like BIT*, AIT*, and FCIT*. His research integrates robotics with medical applications (e.g., knee replacement implants) and autonomous systems (e.g., aerial mapping for emergency response). His algorithms are widely adopted in industry and academia, including collaborations with NASA JPL and Oxford orthopaedic surgeons. Publications: His recent work explores advanced motion planning frameworks (e.g., AORRTC, Osprey), multimotion visual odometry (MVO), and medical robotics applications. These contributions highlight his dual focus on theoretical algorithm development and practical, high-impact applications. Awards: CIPPRS Award for Best Canadian PhD Thesis (Medical Imaging/Robotics) NASA Group Achievement Award (2021, 2022) IROS Best Paper Shortlist (2020) Advising & Grants: He advises on projects involving autonomous systems, medical robotics, and AI. His grants support collaborations with industry and academic partners, including NASA JPL and Oxford's robotics and medical teams. The ESP group's open-source tools (e.g., Planner Developer Tools) enable reproducible motion-planning research. Labs/Teams: He leads the ESP research group at Queen's University and collaborates with the Oxford Robotics Institute and Ingenuity Labs, focusing on cutting-edge robotics solutions for real-world challenges.

Brett Gibsonمشاهده پروفایل

استاد

• Animal Cognition
• Behavioral Neuroscience
• Spatial Memory
• +۱ مورد دیگر

Brett Gibson is Professor and Department Chair of Psychology at University of New Hampshire. He holds a Ph.D. from University of Nebraska and M.S. from Bucknell University, with research focusing on animal cognition and behavioral neuroscience. Research examines: Spatial memory and navigation across species Neurophysiological basis of learning and decision-making Comparative cognition in birds and rodents Visual perception and memory systems His publications demonstrate consistent focus on neural mechanisms of spatial cognition, particularly thalamocortical pathways and prefrontal function, with significant work on avian cognitive abilities in nutcrackers and pigeons.

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +۸ مورد دیگر

Paolo Buono is Associate Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. He holds a PhD in Computer Science with specialization in Visual Data Analysis. His research focuses on Information Visualization, Visual Analytics, Human-Computer Interaction, and Mobile Applications. Co-founder and CEO of LARE (2010), a university spinoff providing real-time surgical support through audio-video telestration Member (since 2002) and computer science coordinator at METEA Research Center for environmental protection Visiting scientist at AVIZ (France), University of Maryland (USA), and Fraunhofer IPSI (Germany) His work spans multiple application domains including: Cultural Heritage through interactive exploration systems Healthcare with smart therapeutic devices Environmental Monitoring via CET system IoT-based Smart Interactive Experiences He has contributed to: Dynamic hypergraph visualization techniques End-User Development frameworks (EUDroid) Usability evaluation methodologies Mobile health applications As project leader, he has coordinated: EU-funded VisMaster Coordination Action (2008-2010) Italian Ministry-funded LOGIN project (2014-2015) Apulia Region environmental projects His professional engagements include: Co-chair roles at INTERACT, AVI, IS-EUD conferences Program committee participation in VIS series and HCI conferences Member of ACM, IEEE, and SIGCHI Italy

Bin Huمشاهده پروفایل

استادیار

• Resilient Cyber-Physical Systems (CPS)
• AI-Human Collaboration Safety & Security
• Big Data & Machine Learning-Based Control
• +۵ مورد دیگر

Dr. Bin Hu is an Assistant Professor in the Department of Engineering Technology at the University of Houston (2022–present) and previously held the same position at Old Dominion University (2019–2022). He earned a Ph.D. in Electrical Engineering from the University of Notre Dame (2016), an M.S. in Control Science from Zhejiang University (2010), and a B.S. in Electrical Engineering from Hefei University of Technology (2007). His research focuses on resilient Cyber-Physical Systems (CPS), AI-human collaboration safety, machine learning-driven control, and vehicular networks. Key projects include NASA-funded work on adaptive human-autonomy responsibility allocation (2023–2026) and NSF-supported AI-human collaboration in autonomous vehicles (2020–2023). He has secured over $1.2 million in grants, including a $700k NASA grant (pending) and an ONR Rapid Solutions grant ($42k). Dr. Hu’s work bridges control theory, machine learning, and human factors. Recent contributions include safer leader-follower robotic systems, fault-tolerant UAV control, and studies on driver trust in AI-driven vehicles. His research has been recognized through awards like the 2024–2025 APeX Excellence Speaker Series. Teaching experience includes courses on C++ programming and electrical engineering fundamentals at both the University of Houston and Old Dominion University. He advises multiple undergraduate and graduate students in projects ranging from LiDAR systems to cyber-physical security. Key Labs/Teams : NAIL Lab (Networked Autonomous Intelligence & Learning), part of the University of Houston’s Cybersecurity and Robotics initiatives. Grants : NASA TTT Grant ($700k), NSF CHS Grant ($500k), ONR RSLP Project ($42k).

Chengzhou Tangمشاهده پروفایل

استادیار

• 3D Computer Vision
• Deep Learning
• Robotics
• +۳ مورد دیگر

Chengzhou Tang is an Assistant Professor in the Department of Computer Science at the University of Manitoba. His research focuses on 3D computer vision, deep learning, robotics, and embodied artificial intelligence. He leads the Spatial Intelligent System (SPINS) Lab, exploring topics such as 3D object reconstruction, camera localization, and video processing techniques. His work spans applications in robotics, virtual reality, and computational imaging. Dr. Tang's research interests include developing novel algorithms for multi-view diffusion models, deep recurrent optimizers, and subspace methods. He has contributed to advancements in video depth estimation, point cloud processing, and stabilization of 360-degree videos. His recent work emphasizes generative models and embodied AI, addressing challenges in sparse-view 3D reconstruction and scene-agnostic camera localization. His publications reflect a trend toward integrating deep learning with traditional computer vision problems, such as structure-from-motion and SLAM (Simultaneous Localization and Mapping). The SPINS Lab collaborates on projects involving global bundle adjustment networks and initialization-robust monocular SLAM systems. While no awards or grants are explicitly listed, his active publication record indicates sustained research engagement in these areas. Dr. Tang’s academic profile highlights his contributions to foundational techniques in computer vision and their practical applications in robotics and AI systems. His work bridges theoretical advancements with real-world challenges in imaging and spatial intelligence.