جست‌وجوی استاد و پژوهشگر

Yiming Dengمشاهده پروفایل

استاد

• Nondestructive Evaluation (NDE)
• Sensors and Sensing Systems
• Artificial Intelligence (AI)-enabled NDE/SHM
• +۶ مورد دیگر

Yiming Deng is a Professor of Electrical and Computer Engineering (ECE) at Michigan State University (MSU), serving as the Associate Chair for Graduate Studies. He leads the Nondestructive Evaluation Laboratory (NDEL), focusing on advanced sensor technologies, AI-driven NDE/SHM systems, and uncertainty quantification. His research addresses critical infrastructure safety, energy systems, and aerospace applications. Education: Ph.D., Electrical Engineering, Michigan State University, 2009 B.E., Electrical Engineering, Tsinghua University, Beijing, China, 2003 Research Interests: Yiming Deng specializes in NDE 4.0/5.0 systems, AI-enhanced sensor fusion, and multi-physics simulations. His work bridges theoretical models with experimental validations, emphasizing practical applications like pipeline inspection, composite materials analysis, and food quality assessment. Recent projects include robotic NDE systems for cylindrical structures and eddy current-based temperature monitoring in additive manufacturing. Publications & Trends: His 190+ peer-reviewed articles span AI-driven defect detection, multi-modal sensing, and sensor automation. Notable themes include fusion of heterogeneous data, ML-based material characterization, and real-time monitoring systems for industrial and agricultural applications. Awards: ASNT Fellowship Award (2021) Best Paper/Poster Awards (ISEM, ENDE, ANTEC) FAA Better Way Award (2005) Advising & Grants: Deng has advised over 20 graduate students and secured funding from DOE, NSF, DOT/PHMSA, Office of Naval Research, and industry partners like Con Edison. His lab collaborates on projects such as AI-enabled robotic inspection robots and multi-modal pipeline assessment systems. Labs & Teams: The NDEL group develops cutting-edge tools like the Nonlinear Eddy Current Inspection System and Wireless Sensor Networks for real-time structural health monitoring. Ongoing efforts include miniaturized sensing systems and hybrid NDE frameworks for complex material analysis.

• Multi-modal Sensor Fusion
• Smart Surveillance
• 3D Reconstruction
• +۵ مورد دیگر

Egor Bondarau is an Associate Professor at Eindhoven University of Technology (TU/e), leading the Video Coding & Architectures group. His primary research focuses on multi-modal sensor fusion, smart surveillance systems, and photorealistic 3D reconstruction. He leads a research cluster on real-time data fusion from thermal, depth, laser, and RGB sensors, supervising five PhD students and multiple MSc students. Bondarau is a project leader in EU initiatives like PaSSANT, PS-CRIMSON, and APPS, addressing multi-camera surveillance challenges. Education: MSc in Robotics and Informatics from Belarus Polytechnic University (1997); PhD in Computer Science from TU/e (2009). Academic roles include teaching Computation I (BSc) and Computer Vision AI & 3D Data Analysis (MSc). Research interests span real-time computer vision, algorithm valorization (deployed at ST Microelectronics, ViNotion, etc.), and smart surveillance innovations. His work contributes to UN Sustainable Development Goals, particularly through traffic analysis and urban monitoring. Scientific Awards: ITEA Award of Excellence 2020, Best Lecturer EE Department 2017, ITEA 2024 Excellence Award. Advising & Grants: Supervised 21 academic works. Led 9 EU-funded projects, including SMART ITEA and Interreg initiatives. Labs/Teams: Coordinates TU/e’s internal multi-modal sensor fusion cluster and collaborates with global tech partners.

Xi Longمشاهده پروفایل

دانشیار

• Signal Processing
• Machine Learning
• Health Monitoring
• +۵ مورد دیگر

Xi Long is an Associate Professor in the Biomedical Diagnostics (BM/d) Lab and Signal Processing Systems (SPS) group at Eindhoven University of Technology (TU/e). He specializes in signal processing, machine learning, and time series analysis applied to healthcare technologies such as unobtrusive vital signs monitoring, sleep studies, neonatal/maternal health, and clinical decision support systems. His work bridges technology and healthcare to create practical solutions for improving patient outcomes. Education: BEng (Hons) in Electronic Information Engineering, Zhejiang University (2002–2006) MSc in Electrical Engineering, TU/e (2007–2009) PhD in Signal Processing Systems (cum laude), TU/e & Philips Research (2011–2015) Research Interests: His research focuses on innovative technologies for health monitoring, including unobtrusive sensing for vital signs, sleep disorder analysis, neonatal care, and AI-driven clinical support. Key areas include: Machine learning for healthcare data analysis Non-invasive physiological signal processing Wearable and remote monitoring systems Recent Trends in Articles: Recent work emphasizes multi-modal data fusion for sleep apnea detection, deep learning models for neonatal sleep staging, and AI applications in sepsis prediction. His publications highlight advancements in sensor technology, clinical algorithm validation, and translational healthcare solutions. Awards and Grants: Best Student Paper Award, IEEE BIBE 2012 First Runner-Up Paper Award, IEEE BHI 2012 Peer reviewer for grants in multiple European countries Labs and Collaborations: He leads projects in the Biomedical Diagnostics Lab and collaborates with Philips Research and industry partners. His work is part of initiatives like the MEDEIA and PISANO projects, focusing on medical engineering innovations and neonatal care technologies.

• Robotics
• Artificial Intelligence
• Control Theory
• +۷ مورد دیگر

Roya Firoozi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. She holds a Ph.D. in Control Theory from UC Berkeley (2021) and a Postdoctoral Research position at Stanford University's Multi-Robot Systems Lab (2024). Her research focuses on advancing safe robot autonomy in interactive environments through generative AI, distributed optimization, and game theory. Key areas include multi-agent systems, autonomous vehicles, and perception-driven control. Education: Ph.D. in Control Theory, UC Berkeley (2021) Bachelor's in Mechanical Engineering, UC Berkeley (2014) Postdoctoral Research, Stanford University (2024) Research Interests: Robotics-aided generative AI, multi-modal perception, interactive autonomy in multi-agent systems, game-theoretic optimization, and safe navigation in dynamic environments. Her work bridges theoretical foundations with real-world experiments in robotics and autonomous systems. Awards: NSF Postdoctoral Research Fellowship ASEE Fellowship (2021-2023) Rising Stars in Aerospace Engineering (2022) Outstanding Graduate Instructor Award (2021) Advising & Grants: Currently supervising graduate students (PhD/Master's) focused on robotics and control systems. Research is supported by grants from NSF and industry collaborations. She has pioneered algorithms for distributed multi-vehicle coordination and occlusion-aware navigation systems. Labs & Teams: Leads the Autonomous Systems Lab at Waterloo, focusing on robotics, AI, and control. Collaborates with Stanford's Multi-Robot Systems Lab and UC Berkeley's MPC Lab on projects involving autonomous vehicles and fault detection systems.

Willem Waegemanمشاهده پروفایل

دانشیار

• Machine Learning
• Data Science
• Multi-target Prediction
• +۷ مورد دیگر

Willem Waegeman is an Associate Professor at the Department of Data Analysis and Mathematical Modelling within the Faculty of Bioscience Engineering at Ghent University. He is affiliated with the Knowledge-based Systems research unit KERMIT. Research Focus: Machine Learning, Data Science, Multi-target Prediction, Deep Learning, Sequence Models, Time Series Analysis His research spans theoretical developments and practical applications in life sciences, addressing challenges in uncertainty quantification, hierarchical classification, and multi-target learning. Recent work emphasizes epistemic uncertainty , conformal prediction , and transformer architectures for biomedical data. Publications from 2025 highlight applications in drug discovery, microbial analysis, and climate modeling, with methodological innovations in uncertainty-aware learning and multi-modal data integration.

Seongkook Heoمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Haptics
• Sensing Technologies
• +۳ مورد دیگر

Seongkook Heo is the Alfred Weaver Career Enhancement Assistant Professor in the Department of Computer Science at the University of Virginia. His primary research focuses on Human-Computer Interaction (HCI), with an emphasis on developing novel sensing techniques, haptic feedback systems, and interactive technologies. He directs the Ultimate User Interface Lab, aiming to bridge physical and digital worlds through advanced interaction methods. He holds a Ph.D. in Computer Science from KAIST (2017), supervised by Prof. Geehyuk Lee. Prior roles include postdoctoral research at the University of Toronto's DGP Lab under Prof. Daniel Wigdor and industry internships at Samsung Advanced Institute of Technology, Microsoft Research, and Autodesk Research. Key research areas include tactile interfaces, gesture recognition, and assistive technologies. His work has been recognized with awards such as the ACM CHI Best Paper Award, ACM MobileCHI Best Paper Award, and the Meta Research Award. He teaches courses on HCI and interactive technologies at UVA. His Ultimate User Interface Lab explores cutting-edge projects like MoiréTag for tangible interactions, Thing2Reality for XR communication, and Take My Hand for visually impaired navigation. Collaborative tools like Openmic and ThingShare enhance remote collaboration through spatial and object-sharing innovations.

• Control Systems
• Robotics
• Structural Health Monitoring
• +۶ مورد دیگر

Carcaterra Antonio is a Full Professor at Sapienza University of Rome, specializing in control systems, robotics, and structural engineering. His research focuses on advanced control methodologies, nonlinear dynamics, and innovative applications in mechanical engineering. Key research areas include: vibration control, autonomous robotics, metamaterials, structural health monitoring, and energy harvesting systems. Recent projects involve developing novel algorithms for damage detection in infrastructure and optimizing gait patterns for quadruped robots. His work integrates machine learning techniques with traditional engineering principles, exemplified by projects like the SUNMARE marine vehicle and the Auto-sapiens autonomous driving system. He has pioneered methods for wave propagation analysis in nonlocal materials and developed adaptive control strategies for automotive suspensions. Notable contributions include the ADACTA damage detection technique and the VFC variational feedback controller. His research bridges theoretical developments with practical applications in transportation systems, environmental monitoring, and disaster response technologies.

Andreas Wieserمشاهده پروفایل

استاد

• Geospatial Engineering
• Geomatics
• Geodetic Monitoring
• +۳ مورد دیگر

Prof. Andreas Wieser is a Full Professor of Geosensorics and Engineering Geodesy at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, leading the Institute of Geodesy and Photogrammetry. His work focuses on advanced measurement systems for high-precision positioning, navigation, and geodetic monitoring of infrastructure and natural environments. Notable contributions include parameter estimation methods, quality control frameworks, and sensor optimization for applications like avalanche monitoring, railway deformation analysis, and CERN's Futur Circular Collider project. Education: PhD in Surveying from TU Graz (2001), Thesis: 'Robust and fuzzy techniques for parameter estimation and quality assessment in GPS' Habilitation at TU Graz (2007), Thesis: 'GPS based velocity estimation and its application to an odometer' Postdoc at University of Calgary (2003-2004) Research Interests: Development of multi-spectral LiDAR systems for material probing and distance measurement Integration of sensor technologies for environmental monitoring (e.g., snow depth analysis, hydroelectric dam deformation) High-precision geodetic infrastructure for large-scale projects like CERN's FCC Algorithmic advancements in point cloud registration, radiometric calibration, and uncertainty quantification Honors: 2006 Karl-Rinner Award (Austrian Geodetic Commission) 2003 Erwin-Schrödinger Fellowship (Austrian Science Fund) 2002 Josef Krainer Sponsorship (State of Styria) Grants & Labs: Alpine Metrology Lab: Long-range TLS and permanent GNSS for geomonitoring Leadership on CERN FCC Geodesy project Development of Comb-based multispectral LiDAR systems His work bridges geomatics with engineering challenges, emphasizing practical implementations of advanced sensor technologies across civil infrastructure, environmental science, and heritage preservation.

• Biofluid Dynamics
• Biomechanics
• Medical Device Development
• +۴ مورد دیگر

Morteza Gharib is the Hans W. Liepmann Professor of Aeronautics and Medical Engineering at the California Institute of Technology (Caltech), holding the Booth-Kresa Leadership Chair in the Center for Autonomous Systems and Technologies (CAST). He serves as Director of the Graduate Aerospace Laboratories (GALCIT) and CAST, overseeing research in autonomous systems, fluid dynamics, and biomedical engineering. His academic roles include Vice Provost at Caltech (2010-2016) and leadership in institutes like the Linde Institute (2014-2015). Education: B.S. from Teheran University (1976), M.S. from Syracuse University (1978), and Ph.D. from Caltech (1983). His career has spanned roles from Assistant Professor (1992) to his current named chair. Research interests focus on biofluid dynamics, biomedical device innovation (e.g., heart valves, cardiovascular monitoring), and autonomous robotics. He develops technologies inspired by biological systems, such as aquatic propulsion and aerial morphing robots. His lab also advances flow diagnostics like real-time PIV systems and machine learning-driven pressure waveform analysis. Key achievements include the Ronald J. Adrian Award (2023) for contributions to flow visualization. His work bridges aerospace and medical engineering, emphasizing cross-disciplinary solutions. Current projects involve bioinspired robots, turbulence control, and non-invasive health monitoring systems. Labs/Teams: Directs GALCIT and CAST, leading interdisciplinary teams in robotics, fluid dynamics, and biomedical engineering. Collaborates with industry and academic partners on advanced systems like the ATMO morphing robot and cardiovascular device prototypes.

• Remote Sensing
• Geographic Information Systems (GIS)
• Environmental Data Science
• +۳ مورد دیگر

Bo Zhou is a Lecturer in the Department of Geography at the University of California, Los Angeles (UCLA). His current work involves a NASA-funded project collaborating with the U.S. Geological Survey (USGS) and the Bureau of Land Management (BLM) to develop a web-based tool that integrates NASA satellite data with field assessments for spatial and temporal analysis of environmental variables. This research focuses on enhancing ecological monitoring and resource management through advanced data fusion techniques. Dr. Zhou holds a Ph.D. in Forestry (2013) and an M.A. in Geography (2007), both from the University of Missouri Columbia. His research interests span remote sensing, geographic information systems (GIS), environmental data science, and the application of AI-driven methods to environmental challenges. His recent projects emphasize the development of generative models, autonomous systems, and urban simulation tools to address complex spatial and temporal data problems. His publications from 2022–2025 highlight contributions in AI, robotics, and computer vision, particularly in generative models, autonomous navigation, and data-efficient learning. These works reflect a focus on advancing technologies for urban mobility, environmental monitoring, and simulator-based research. Despite prolific output, no scientific awards are explicitly mentioned in the provided texts. Zhou’s advising and grant activities are not detailed here, but his collaborations with NASA and federal agencies underscore a strong commitment to applied environmental research. His work bridges academic and practical domains, aiming to improve decision-making in ecological and urban systems through innovative data integration and simulation.

Myounggyu Wonمشاهده پروفایل

دانشیار

• Cyber Physical Systems
• Intelligent Transportation Systems
• Mobile Computing
• +۴ مورد دیگر

Dr. Myounggyu Won is an Associate Professor in the Department of Computer Science at the University of Memphis, leading the Connected Smart Sensor Systems (CS3) Lab. He holds a PhD from Texas A&M University (2013) and a BE from Sogang University, South Korea (2007). His research focuses on cyber-physical systems, intelligent transportation systems, and mobile computing, emphasizing algorithmic innovation for safer, efficient transportation through advanced sensing and communication technologies. Key research contributions include developing reinforcement learning-based solutions for vehicle routing with drones, adaptive cruise control systems using deep learning, and pedestrian safety prediction via wearable sensors. His work bridges theoretical advancements with practical applications, published in top-tier venues like ICRA, MM, and IEEE journals. Dr. Won previously served as an Assistant Professor at South Dakota State University before joining the University of Memphis in 2018. His lab explores cutting-edge topics such as autonomous vehicle coordination, wireless charging optimization, and low-power IoT communication for smart cities. Despite no explicit grants or awards listed, his prolific publication record reflects sustained research excellence in transportation and sensor systems.

Pragathi Praveenaمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Human-Robot Interaction (HRI)
• Computer Vision
• +۳ مورد دیگر

Pragathi Praveena is a Research Fellow at the Robotics Institute , Carnegie Mellon University. Her work focuses on advancing human-robot interaction, teleoperation systems, and robotics applications in collaborative environments. She leads the Reliable Autonomous Systems Lab , exploring topics such as Petri Net-based robotic system design, generative AI in robotics, and socially engaging robot interfaces. Her research spans autonomous systems , computer vision , and medical robotics applications . Notable contributions include Periscope (a robotic camera system for remote collaboration) and Rex (user-centered repair frameworks for robots). She investigates challenges in telemanipulation control systems, motion generation algorithms, and optimizing collaborative workflows between humans and robots. Key technical trends in her work include formal methods for system design, integration of AI/ML in robotics, and human-centered approaches to interface development. Her publications address both foundational theory (e.g., Petri Nets) and applied solutions (e.g., cinematic automation). Current lab projects emphasize robustness in robotic groupware and cross-modal interaction systems. As part of the Robotics Institute, her work intersects with computer science, engineering, and design disciplines. Her lab collaborates on projects involving drone motion optimization, real-time respiration monitoring via video analysis, and enhancing remote collaboration through robotic systems.

Dimitris Spathisمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Mobile Systems
• +۳ مورد دیگر

Dimitris Spathis is a Visiting Researcher at the University of Cambridge's Department of Computer Science and Technology and a Research Scientist at Google. His work focuses on applying machine learning to real-world data from wearables, mobile sensors, and audio signals, emphasizing data efficiency and modality-agnostic approaches. He holds a PhD in Computer Science from the University of Cambridge, supervised by Prof. Cecilia Mascolo, and has collaborated with companies like Microsoft Research and Telefonica Research. His research spans AI in healthcare, human-computer interaction, and signal processing, with publications in top venues such as NeurIPS, KDD, and ICASSP. Notable projects include developing algorithms for physiological monitoring via wearables and improving fairness in machine learning systems. He has organized workshops like Federated Learning for Mobile Sensing (MobiCom 2021) and serves on program committees for AAAI, IJCAI, and KDD. Key themes in his work include human-centered computing, AI ethics, and mobile health sensing. His contributions have been featured in media outlets like BBC, CNN, and Forbes, highlighting their societal impact in healthcare and technology.

Peyman Moghadamمشاهده پروفایل

دانشیار مدعو

• Self-Supervised Learning for Robotics
• Embodied AI
• 3D Multi-Modal Perception (3D++)
• +۴ مورد دیگر

Peyman Moghadam is an Adjunct Associate Professor at the University of Queensland (UQ) and a Principal Research Scientist at CSIRO Data61. He also holds an adjunct professorship at Queensland University of Technology (QUT). His roles include leading the Embodied AI Research Cluster at CSIRO Data61 and overseeing the Spatiotemporal AI portfolio within CSIRO's Machine Learning and Artificial Intelligence (MLAI) Future Science Platform. He has held visiting appointments at ETH Zürich (2022) and the University of Bonn (2019). His research focuses on self-supervised learning for robotics, embodied AI, 3D multi-modal perception, and computer vision applications in robotics and environmental science. Education details are not explicitly provided in the text, but his professional experience and research output suggest advanced academic training in robotics, computer science, and machine learning. Awards include the CSIRO Julius Career Award, National and Queensland iAwards, and the Lord Mayor's Budding Entrepreneurs Award. He has led large-scale interdisciplinary projects and published extensively in top-tier journals and conferences. His research themes span robotics perception, AI-driven environmental modeling, and sensor fusion. Notable contributions include benchmark datasets (e.g., WildScenes), novel algorithms for LiDAR place recognition, and geo-encoded transformers for plant species prediction. His work bridges robotics, machine learning, and real-world applications in agriculture, environmental monitoring, and autonomous systems. Awards: CSIRO Julius Career Award, Collaboration Medal, National/Queensland iAwards, Lord Mayor's Budding Entrepreneurs Award Grants & Projects: Led multidisciplinary projects in robotics, AI, and environmental science funded by CSIRO and industry collaborators Labs/Teams: Embodied AI Research Cluster (CSIRO Data61), Spatiotemporal AI portfolio (CSIRO MLAI)

• Autonomous Vehicles
• 3D Object Detection
• Motion Planning
• +۵ مورد دیگر

Sudip Dhakal is an Assistant Professor in the Department of Computing & Software Engineering at Florida Gulf Coast University's U.A. Whitaker College of Engineering. His research focuses on autonomous systems, computer vision, and AI applications in robotics. He specializes in 3D object detection, real-time motion planning, and cybersecurity for autonomous vehicles. Key research areas include facial emotion recognition using CNNs, sensor fusion for object detection (e.g., Camera-LiDAR integration), and privacy-preserving technologies for vehicular systems. His work addresses challenges like sparsity in 3D data, dynamic environment adaptation, and threat modeling for autonomous driving safety. Publications emphasize cutting-edge solutions in autonomous vehicle motion planning, privacy protection mechanisms, and open-source approaches to self-driving systems. Recent work (2023-2025) highlights advancements in real-time algorithms, differential privacy applications, and multi-modal sensor fusion frameworks. No scientific awards are explicitly listed. Advising and grant details are not provided in available texts. His affiliation with the Whitaker College positions him within a robust engineering research ecosystem focused on innovation in computing and software systems.