جست‌وجوی استاد و پژوهشگر

Meera Radhakrishnanمشاهده پروفایل

پژوهشگر ارشد

• Mobile and Pervasive Computing
• Wearable and IoT Sensing
• Multi-modal Sensing Systems
• +۴ مورد دیگر

Dr. Meera Radhakrishnan is a Research Fellow at the Data Science Institute, University of Technology Sydney (UTS). Her research focuses on mobile and pervasive computing, wearable/IoT sensing systems, and applied machine learning for healthcare. She holds a PhD in Computer Science from Singapore Management University (SMU) and conducted postdoctoral research at Carnegie Mellon University (CMU). Prior to academia, she worked as a Senior Systems Engineer at Infosys and a Research Scientist at SMU and A*STAR's IHPC. Her work includes developing systems like W8-Scope for gym exercise monitoring, ERICA for free-weight mistake detection, and LiLoc for LiDAR-based localization. She has secured grants totaling AUD 315k+ for projects in wearable healthcare and trustworthy digital systems. Dr. Radhakrishnan actively serves on TPCs of top conferences (e.g., IEEE PerCom, IROS) and chairs sessions at IEEE events. She was recognized as one of Australia's top 25 women in data science (2024) and received the Abbe Grant (2023) and Best Paper Award (WristSense 2019).

• Communication Engineering
• Wireless Networks
• 5G Technology
• +۳ مورد دیگر

Kalle Ruttik is a University Lecturer at Aalto University's Department of Information and Communications Engineering. His research focuses on cutting-edge communication technologies, including millimeter-wave networks, IoT integration, and 6G advancements. Doctoral degree in Engineering and Technology, Aalto University Licentiate degree in Engineering and Technology, Helsinki University of Technology Master's degree in Engineering and Technology, Tallin Technological University Ruttik’s work spans wireless network optimization, antenna design, and sustainable connectivity solutions. His recent projects explore: Drone communication via reconfigurable intelligent surfaces 5G/6G millimeter-wave propagation modeling Integration of backscatter IoT devices with cellular networks Wi-Fi and DECT-2020 standardization efforts Cloudification of telecom infrastructure His research outputs show increasing emphasis on: Millimeter-wave communication Internet-of-Things (IoT) scalability Radio frequency (RF) signal processing Hybrid communication technologies Antenna height optimization Path loss prediction models Scientific contributions include the 2024 IEEE Most Downloaded Paper Award in Radio Frequency Identification. He actively contributes to: International collaborations (Finland-Korea, EU RRF) Doctoral thesis examination Workshop organization (MIDICON, 2018) Media outreach on information privacy and air traffic control

Anna Liمشاهده پروفایل

مدرس

• Human Activity Recognition
• Signal Processing
• Wireless Communication
• +۴ مورد دیگر

Dr. Anna Li is a Lecturer (Assistant Professor) at the School of Computing and Communications, Lancaster University since September 2023. She holds a Ph.D. in Computer Science from Queen Mary University of London and an M.Sc. in Physics from the University of Science and Technology of China. Her research focuses on human activity recognition, signal processing, wireless communication systems, and integrated navigation-communication technologies. She leads the EU-funded Marie Skłodowska-Curie Individual Fellowship project (2024-2026) exploring satellite navigation-communication integration in LEO systems. Key achievements include pioneering work on UWB-based fall detection systems, radar health monitoring, and RIS-aided network optimization. She teaches modules like Computer Networks and Human-Computer Interaction, and has supervised projects funded at Lancaster University and through Chinese Scholarship Council (CSC). Recent publications emphasize trajectory-based gesture recognition, STAR-RIS network designs, and INAC systems. Dr. Li actively contributes to IEEE conferences (GLOBECOM, VTC) as TPC member and reviewer for journals like IEEE IoT Journal. She co-leads the 'Empowering Women in Employment' initiative at Lancaster University (2023-2024).

Ignacio Torroba Balmoriمشاهده پروفایل

پژوهشگر

• Underwater Robotics
• Simultaneous Localization and Mapping (SLAM)
• Autonomous Underwater Vehicles (AUVs)
• +۳ مورد دیگر

Ignacio Torroba Balmori is a postdoctoral researcher at KTH Royal Institute of Technology, affiliated with the Department of Aerospace, Mobility and Naval Architecture and the Robotics, Perception and Learning (RPL) division. He holds a PhD from RPL under the supervision of John Folkesson, focusing on Simultaneous Localization and Mapping (SLAM) for autonomous underwater vehicles (AUVs). His current research emphasizes underwater SLAM with sonar and camera systems, path planning, control strategies, and system identification for AUVs operating in open waters and confined spaces. His work centers on applications such as autonomous seabed surveying, algae farm monitoring, and seaweed mapping. He is actively involved in developing the AUV SAM, Kongsberg Hugin, BlueROV2, USV FloatSAM, and the AUV Lolo. As a mentor, he has supervised master’s theses on topics including underwater SLAM algorithms and bathymetric informative path planning. He teaches courses like 'Introduction to Robotics' (assistant) and 'Underwater Technology' (teacher). Key technical contributions include system identification for hydrobatic AUVs using physics-informed machine learning and tools for bathymetric SLAM, such as the SubmapsRegistration repository. His research is hands-on, prioritizing field robotics and problem-driven solutions.

Peer Neubertمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Dr. Peer Neubert is a Professor and Head of the Intelligent Autonomous Systems (INTAS) Working Group at the University of Koblenz. He is affiliated with the Department of Computer Visualistics (Department 4) and serves as Head of the Institute of Computational Visualistics. His academic roles include membership in the Department Council and Library Committee of FB4. His research focuses on robotics, computer vision, and autonomous systems, with particular emphasis on visual place recognition, SLAM, hyperdimensional computing, and adaptive navigation in dynamic environments. Neubert's work spans theoretical advancements and practical implementations, such as benchmarking systems (e.g., HS3-Bench), efficient algorithms (FETCH, LocalSPED), and sensor-based solutions (HealthWalk). His team addresses challenges in long-term navigation, environmental adaptation, and data-efficient learning. Collaborations include external PhD students like Kenny Schlegel and projects like MineSweeper, demonstrating interdisciplinary impact. His contributions are reflected in over 50 publications since 2007, covering topics from hyperspectral segmentation to biomedical applications. While no formal educational details are provided, his extensive research output and leadership roles underscore his expertise in computational visualistics and autonomous systems.

G.J. Vancsoمشاهده پروفایل

استاد

• Polymer Chemistry
• Material Science
• Surface Science
• +۳ مورد دیگر

G.J. Vancso is a Full Professor specializing in Sustainable Polymer Chemistry, recognized for contributions in polymer materials science and nanotechnology. His research focuses on polymer brushes, bio-inspired adhesives, and surface engineering with applications in biomaterials and environmental sustainability. Key research areas include thin film stability, smart hydrogels, and nanomechanical characterization using techniques like atomic force microscopy (AFM). Collaborations span international institutions, emphasizing material innovation in agriculture (e.g., water-harvesting coatings) and biomedical diagnostics. Recipient of two poster prizes at the Xth Dutch Polymer Days (2010) for work on spectroscopy and ellipsometry applications Organized the International Conference on Bioinspired and Zwitterionic Materials (2019) Delivered keynote lectures on smart hydrogels and polymer analysis Over 992 research outputs include articles on copolymer design, AFM-based diagnostics, and innovative material synthesis. His work bridges fundamental polymer science with practical applications in agriculture, healthcare, and environmental technology.

Peng Songمشاهده پروفایل

استادیار

• Computer Graphics
• Geometric Modeling
• Computational Design
• +۵ مورد دیگر

Peng Song is an Assistant Professor of Computer Science at Singapore University of Technology and Design (SUTD), affiliated with the Pillar of Information Systems Technology and Design (ISTD). His research focuses on computer graphics, geometric modeling, computational design, and fabrication, emphasizing the development of algorithms for designing functional real-world objects. He holds a PhD from Nanyang Technological University (2013) and prior research roles at EPFL, University of Science and Technology of China, and Nanyang Technological University. Education: PhD in Computer Science, Nanyang Technological University (2013) Master’s Degree, Harbin Institute of Technology (2010) Bachelor’s Degree, Harbin Institute of Technology (2007) His research interests span computational design of assemblies, 3D printing, robotics, and urban modeling. Notable projects include generative urban design, wireframe mesh modeling, and mechanisms for custom-fit medical devices. He has received awards such as the SMI 2024 Best Paper Award and SIGGRAPH 2022 Honorable Mention. Grants & Professional Roles: Leading Singapore MOE Academic Research Fund Tier 2 grants (2023–2024) Associate Editor for Computers & Graphics and Graphical Models (2024–present) Program Committee Member for SIGGRAPH Asia, Pacific Graphics, and SPM He teaches courses such as 'Graphics and Visualization' and 'Extended Reality' at SUTD. His group’s work is showcased on YouTube and his lab’s website.

• Robotics
• Human-Robot Interaction
• Industrial Automation
• +۳ مورد دیگر

Alexandre Angleraud is a Postdoctoral Research Fellow at Tampere University within the Automation Technology and Mechanical Engineering department under the Faculty of Engineering and Natural Sciences. His research focuses on advancing human-robot collaboration, robotic manipulation, and sensor-based systems in industrial and manufacturing environments. Key areas include knowledge-based planning, tactile feedback learning, and cognitive semantics for dynamic task coordination. His work integrates robotics with machine learning techniques, emphasizing agile production systems, soft robotic grippers, and natural language interfaces for hierarchical task instruction. Notable contributions include innovations in multi-label annotation for visual models and exploration-exploitation mechanisms inspired by neural processes. Recent research trends highlight advancements in co-speech gesture interpretation for human-robot interaction, imitation learning frameworks leveraging sequential data, and comparative studies on sim-to-real learning controllers. His studies often bridge theoretical cognitive models with practical industrial automation challenges. No scientific awards or funded grants are explicitly listed in the provided materials. Advising activities and lab affiliations remain unspecified in the current data.

Dario Lodi Rizziniمشاهده پروفایل

دانشیار

• Robotics
• Autonomous Robots
• Localization, Mapping and Navigation
• +۱۴ مورد دیگر

Dario Lodi Rizzini is an Associate Professor at the Department of Engineering and Architecture, University of Parma, where he conducts research and teaching in robotics, computer vision, and autonomous systems. He has held academic positions including Research Assistant (2015–2020) and Assistant Professor (2021–2023) before becoming Associate Professor. His work spans industrial, agricultural, and underwater robotics applications. Research Interests: Localization, Mapping, and Navigation (SLAM) for mobile robots 3D perception and point cloud processing Robot manipulation and grasping Perception-aware control and pose estimation Applications in industrial automation, precision agriculture, and underwater intervention His recent publications show a strong trend in geometric algorithms for rotation and point cloud registration, particularly using the Angular Radon Spectrum, as well as practical deployments in warehouse automation and agricultural robotics. He has developed scalable systems for variable-rate irrigation and industrial depalletizing, demonstrating real-world impact. Scientific Awards and Recognition: Team leader of the University of Parma team that won Sick Robot Day in 2012 and 2014 Active IEEE member with publications in top-tier journals including IEEE Transactions on Robotics, IEEE RA-L, and IEEE ICRA/IROS Advising and Grants: Dario Lodi Rizzini has mentored numerous PhD students and postdocs, including Riccardo Monica, Ernesto Fontana, and Asad Ullah Khan. He has collaborated on multiple industrial and national research projects focusing on robotics in agriculture, warehouse automation, and underwater intervention, including the Italian MARIS project on underwater robotics. Labs and Teams: He is a key member of the Robotics and Intelligent Machines Lab (RIMLab) at the University of Parma, contributing to research in autonomous systems, perception, and manipulation. He has led teams in robotic competitions and collaborative industrial projects.

• Artificial Intelligence
• Graph Learning
• Computer Vision
• +۶ مورد دیگر

Francesca Pistilli is a Researcher at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is affiliated with the College of Computer, Film, and Mechatronics Engineering and contributes to the VANDAL Laboratory (Visual and Multimodal Applied Learning). Her research focuses on artificial intelligence, robotics, graph learning, and computer vision. She teaches courses including Introduction to Graph Learning (PhD), Robot Learning (MSc), and Programming Techniques (BSc). She supervises PhD student Gaetano Salvatore Falco (Artificial Intelligence, 40th cycle, 2024–ongoing). Her publications highlight expertise in image segmentation, egocentric video understanding, neural architecture search, and point cloud compression. Recent projects include Scene Understanding (2025–2026) funded by commercial contracts.

Edith Tretschkمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Edith Tretschk is a Research Scientist at Meta Reality Labs Research in the San Francisco Bay Area. She completed her Ph.D. in Computer Science at Saarland University and Max Planck Institute for Informatics (2018-2023), advised by Christian Theobalt . Her work bridges computer graphics , computer vision , and machine learning , with a focus on 3D reconstruction and quantum computing applications. Education Ph.D. in Computer Science (2018-2023), Max Planck Institute for Informatics & Saarland University M.Sc. in Computer Science (2017-2023), Graduate School of Computer Science, Saarland University B.Sc. in Computer Science (2014-2017), Saarland University Research Focus Her research explores 3D reconstruction of dynamic scenes, neural rendering , and quantum computing for vision tasks. Recent work includes time-consistent scene flow (SceNeRFlow), quantum auto-encoders (3D-QAE), and physics-driven template matching (φ-SfT). Article Trends Her publications span 3D vision , quantum algorithms , and neural scene modeling . Key themes include non-rigid deformation, quantum-hybrid approaches, and physics-based reconstruction. Scientific Recognition Bachelor Award (2017) for top CS graduates Deutschlandstipendium scholarship (2015-2017) NeurIPS Top Reviewer (2022) Additional Contributions She has delivered invited talks at World Labs, Meta, Nvidia, and Epic Games. Active as a reviewer for CVPR, ECCV, ICCV, and NeurIPS, she has also contributed to open-source projects and datasets.

Francis Engelmannمشاهده پروفایل

استادیار

• Computer Vision
• 3D Scene Understanding
• Machine Learning
• +۴ مورد دیگر

Francis Engelmann is an incoming Assistant Professor at the University of Lugano (USI) Faculty of Informatics, currently completing his postdoctoral research at Stanford University working with Prof. Leonidas Guibas and Prof. Jeannette Bohg. Prior to Stanford, he was a postdoctoral research fellow at ETH Zurich with Prof. Dr. Marc Pollefeys and conducted his PhD in Computer Vision, Machine Learning and 3D Scene Understanding at RWTH Aachen University under Prof. Dr. Bastian Leibe. His research focuses on advancing 3D scene understanding through computer vision, machine learning, and robotics. Engelmann's work explores open-vocabulary 3D scene understanding, functional scene analysis, 3D reconstruction, and semantic segmentation. He has made significant contributions to open-vocabulary 3D instance segmentation, 3D scene graph construction, and language-augmented 3D vision. His research bridges theoretical computer vision with practical robotics applications, particularly in scene understanding for robotic manipulation. Analysis of his recent publications reveals a strong trajectory in developing methods for open-vocabulary 3D scene understanding, with increasing focus on functional understanding, language integration, and practical robotics applications. His work spans from foundational 3D representation learning to applied robotics systems, with a consistent emphasis on making 3D scene understanding more accessible, scalable, and semantically rich. ETH Zurich Career Seed Award (2022) Outstanding Reviewer for CVPR 2024 (top 2%) Top Reviewer for NeurIPS 2023 Best Paper Award at ICRA'24 MOMA.v2 workshop Multiple oral presentations at top-tier conferences (ICCV, CVPR, ECCV) Engelmann actively mentors PhD students and postdocs, with notable collaborations including Valentin Bieri, Rui Huang, Elisabetta Fedele, and Ayca Takmaz. He has received an NVIDIA Academic grant and serves as an area chair for major computer vision conferences including CVPR'25, WACV'26, and 3DV'25. He co-organizes the annual Open-Vocabulary 3D Scene Understanding Workshop, now in its fourth iteration at CVPR'25.

James Matukمشاهده پروفایل

استادیار

• Functional Data Analysis
• Bayesian Statistics
• Biostatistics
• +۳ مورد دیگر

James Matuk serves as an Assistant Professor in the Department of Epidemiology at the University of Pittsburgh, specializing in advanced statistical methodologies for public health research. His work focuses on developing innovative approaches to analyze complex longitudinal health data and non-Euclidean structures. His educational qualifications include: PhD in Statistics from The Ohio State University (2021) MS in Statistics from The Ohio State University (2018) BS in Mathematics from Duquesne University (2016) Dr. Matuk's research centers on Functional Data Analysis and Bayesian Statistics to address limitations in traditional parametric models for exposure-outcome relationships. He pioneers geometric and topological methods for handling complex data structures like functional data, point clouds, and biomedical images. His work bridges statistical theory with practical public health applications, particularly in modeling time-dependent health phenomena through flexible non-parametric frameworks. His publication record reveals a consistent trajectory in geometric-statistical methodology development, with increasing emphasis on topological data analysis applications. Recent work demonstrates integration of computer vision techniques with biostatistics, particularly evident in his 2024 IEEE TPAMI publication on point cloud analysis and 2021 CVPR paper on functional data classification. No scientific awards are documented in the provided materials. Information regarding student advising and research grants is not specified in the available documentation. The provided text contains no references to laboratory facilities or research team structures.

Shenghan Guoمشاهده پروفایل

استادیار

• Knowledge-informed data analytics and AI
• Human-in-the-Loop Machine Learning
• Human-AI collaborative manufacturing
• +۵ مورد دیگر

Shenghan Guo is an Assistant Professor in the School of Manufacturing Systems and Networks at Arizona State University, specializing in data-driven solutions for smart manufacturing. She leads the DAIM (Data Analytics & Insights in Manufacturing) research lab, which focuses on human-AI collaboration in manufacturing processes. Her lab utilizes specialized equipment including an OPTOMEC Aerosol Jet Printer for flexible 3D printing applications. Education: Ph.D. in Industrial and Systems Engineering, Rutgers University (2021) M.S. in Engineering Sciences and Applied Mathematics, Northwestern University (2016) M.S. in Financial Mathematics, Johns Hopkins University (2014) B.S. in Financial Engineering, Jilin University (2013) Her research integrates knowledge-informed AI with human-centered approaches to advance manufacturing systems. Key areas include: Human-in-the-Loop ML : Developing collaborative AI systems that incorporate human expertise Additive Manufacturing : In-situ monitoring and defect detection for 3D printing Process Optimization : Real-time quality control using computer vision and signal processing Publications (2017-2025) demonstrate a consistent focus on machine learning applications in manufacturing, with recent emphasis on generative AI for process simulation, contrastive learning for worker monitoring, and multi-modal data fusion for quality prediction. Over 60% of recent works involve additive manufacturing applications. Awards and Honors: ASU FWA Outstanding Mentor Award (2023) INFORMS Best Paper Runner-up (2021) IISE Data Challenge Winner (2019-2020) NSF Conference Support Awards Advising and Grants: Currently mentors three PhD students (Hasnaa Ouidadi, Pius Gyamenah, Boyang Xu) whose work has received SME 30 Under 30 recognition and AEC awards. Secured lab funding for advanced manufacturing equipment supporting electronic printing and human-machine interaction research. Teaching includes courses on computational modeling (EGR 219), machine learning (RAS 585), and specialized manufacturing topics. Developed new curriculum integrating real-world manufacturing data with Python-based ML implementations.

• Remote Sensing
• Earth Observation
• Radar Interferometry
• +۷ مورد دیگر

Professor Linlin Ge is a distinguished academic specializing in remote sensing and earth observation at the University of New South Wales (UNSW), Sydney, Australia. He holds a position in the School of Civil and Environmental Engineering and has been studying earth observation techniques since 1985. His work has significant applications in natural disaster monitoring, land subsidence measurement, and integrating radar and optical remote sensing with GPS and GIS systems. Professor Ge's educational background includes a PhD in GPS and remote sensing from UNSW, an MSc in Crustal Deformation from the Institute of Seismology, and a BEng (1st Hons and University Medal) in Optical Engineering from the Wuhan Technical University of Surveying and Mapping. He was also a postdoctoral research fellow in the Meteorological Research Institute of the Japan Meteorological Agency sponsored by the Science and Technology Agency of Japan during 1997-1998. His research interests focus on using digital images to map the Earth's surface, combining remote sensing with GPS and GIS to produce cost-effective and reliable maps, and integrating radar and optical remote sensing with GPS and GIS to measure subtle surface changes with minimal latency. Professor Ge's work aims to make remote sensing more timely, accurate, affordable, and widely applicable across various disciplines. His recent publications demonstrate a strong focus on advanced remote sensing techniques, particularly in the areas of hyperspectral image classification, landslide monitoring, earthquake damage assessment, and land subsidence analysis. His research increasingly incorporates deep learning and AI approaches while maintaining strong applications in natural disaster monitoring and management. Major Awards: JK Barrie Award for Overall Excellence (2008 Asia-Pacific Spatial Excellence Awards) NSW Scientist of the Year 2009 (Physics, Earth Sciences, Chemistry and Astronomy) UNSW Inventor of the Year finalist (2010) Australian Research Council Postdoctoral Fellowship (2002-2004) Professor Ge has supervised 25 graduated students and currently has 5 students in his lab. His research is supported by multiple grants including ARC Discovery grants, Australian Space Research Program funding, and various government and industry partnerships focused on SAR formation flying, subsidence monitoring, flood mapping, and carbon capture safeguarding. His work with satellite missions such as Envisat, ALOS, COSMO-SkyMed, Radarsat-2 and ALOS-2 demonstrates his leadership in practical applications of remote sensing technology.