جست‌وجوی استاد و پژوهشگر

• Atmospheric Physics
• Planetary Science
• Atmospheric Electricity
• +۷ مورد دیگر

Martin W. Airey is a researcher affiliated with the University of Reading, specifically within the Department of Meteorology under the School of Mathematical, Physical and Computational Sciences. His work bridges atmospheric physics, planetary science, and instrumentation development for extreme environments. University: University of Reading School: School of Mathematical, Physical and Computational Sciences Department: Department of Meteorology Role: Researcher His research focuses on electrical phenomena in atmospheric and planetary contexts, including volcanic plume electrification, desert dust charging, cloud microphysics, and instrumentation for in-situ measurements. He has developed novel sensors for electrometers, microbalances, and point discharge detection, often deployed in airborne or remote environments. The recent publications (2015–2024) reveal a consistent trajectory in understanding electrical processes across Earth and Venus. His work integrates field data (e.g., UAE precipitation, Arctic clouds) with planetary-scale analysis (e.g., Venus tectonics), demonstrating interdisciplinary depth. Key themes include atmospheric electricity, sensor innovation, volcanic hazards, and comparative planetary atmospheres. No scientific awards are listed in the provided text. Martin Airey collaborates extensively with leading scientists such as R. G. Harrison, K. A. Nicoll, and K. L. Aplin. While no formal students are listed, his collaborative nature suggests mentorship roles in large research teams. He has contributed to major projects including those under the ARM program and planetary science initiatives. His work often involves grant-funded instrumentation development and field campaigns. His research is conducted through collaborative laboratories focused on atmospheric electricity and planetary instrumentation, likely involving the development and testing of compact, robust sensors for deployment in harsh environments—such as volcanic zones, deserts, and airborne platforms. These teams integrate physics, engineering, and environmental science to advance measurement capabilities.

• 3D Shape Analysis
• Graph-Based Machine Learning
• Computer Vision
• +۴ مورد دیگر

Andrea Torsello is a researcher at the University of York, specializing in 3D shape analysis, graph-based machine learning, and quantum computing applications in computer vision. His work bridges theoretical and applied domains, focusing on pattern recognition, network thermodynamics, and remote sensing. PhD from University of York (2004) Published extensively in journals like IEEE Transactions and Pattern Recognition Research interests include: Quantum-inspired graph analysis 3D reconstruction techniques Manifold learning for complex networks Thermodynamic modeling of time-evolving systems Key contributions involve: Quantum walk-based graph similarity measures k-Anonymity for graph data Physics-driven CNN models for ocean wave reconstruction Game-theoretic approaches to shape matching

• Algorithms for hybrid and embedded systems
• Level set methods
• Hamilton-Jacobi equations
• +۴ مورد دیگر

Professor Ian Mitchell is a faculty member in the Department of Computer Science at the University of British Columbia's Faculty of Science. He received his B.A.Sc. and M.Sc. from UBC and PhD in Scientific Computing and Computational Mathematics from Stanford University. His research focuses on algorithms for hybrid systems, level set methods for Hamilton-Jacobi PDEs, control systems for cyber-physical applications, assistive technology development, and reproducible research practices. Key research contributions include the Toolbox of Level Set Methods Development of Dijkstra-like ordered upwind methods Advancements in safety-preserving control algorithms Applications in smart wheelchair navigation and user interface design His recent publications demonstrate expertise in Hamilton-Jacobi equation solving, robotics applications of level set methods, and assistive technology design. While no explicit awards are listed, his work spans multiple disciplines including computer science, control theory, and biomedical engineering. He teaches both graduate (CPSC 5xx) and undergraduate (CPSC 4xx and below) courses in computational robotics and parallel computing.

• Image Compression
• Machine Learning
• Visual Media Processing
• +۷ مورد دیگر

Jona Ballé is an Associate Professor in the Electrical and Computer Engineering department at New York University's Tandon School of Engineering. His research focuses on developing efficient representations of visual media through machine learning and end-to-end optimization techniques. Dr. Ballé's research interests center on visual media compression, spanning still images, video, augmented reality, virtual reality, plenoptic imaging, and holographic imaging. His work bridges information theory, computer vision, and machine learning to develop perceptually optimized compression algorithms. He has made significant contributions to understanding the relationship between human visual perception and image statistics, which has led to improved compression results and ultimately contributed to the JPEG AI standard finalized in 2025. His recent publications demonstrate a strong trend toward Wasserstein distortion metrics, neural compression architectures, and rate-distortion optimization. These works span computer vision, information theory, and signal processing domains, with applications in both traditional and emerging visual media formats. His research shows consistent innovation in developing perceptually relevant metrics that balance fidelity and realism in compressed media. Contributed to JPEG AI standard (2025) Co-organizer of Challenge on Learned Image Compression (CLIC) since 2018 Program committee member of Data Compression Conference (DCC) since 2022 Reviewer for top-tier publications including NeurIPS, ICLR, ICML, and IEEE Transactions journals Dr. Ballé has advised numerous graduate students who have co-authored significant publications with him, particularly in the areas of neural compression and perceptual metrics. His research has been supported by institutions including the Simons Foundation. He maintains active collaborations across academia and industry, with his work at Google (2017-2024) directly informing his current academic research. His laboratory focuses on developing open-source implementations of advanced compression techniques, with notable GitHub repositories including Wasserstein Distortion implementation in PyTorch and CoDeX (Learned data compression in JAX), demonstrating his commitment to reproducible research and community engagement.

Jonathon Shlensمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Neuroscience
• +۳ مورد دیگر

Jonathon Shlens is a principal scientist and research director at Google DeepMind, focusing on vision, language, and learning. He has led teams in deploying production systems, invented TensorFlow, and collaborated with Waymo. His work spans machine learning, computer vision, basic science, and autonomous driving. Research interests include: Machine learning with applications in multimodal and transformer models Computer vision, particularly robustness and 3D object detection Neuroscience, analyzing neural computations in the primate retina Autonomous driving systems and motion forecasting Recent articles highlight trends in: Vision-language models and semantic guidance Transformer architectures for scene flow and calibration Adversarial robustness across human and machine perception Scalable datasets and model architectures Scientific awards include: Best Paper Award at CVPR 2013 Former advisees include notable researchers now at institutions like Stanford, MIT, and OpenAI.

• Radio Communications
• Wireless Networks
• Computer Systems
• +۳ مورد دیگر

Rosen Pasarelski is a Professor in the Telecommunications Department at New Bulgarian University (NBU), where he has taught since 2003. His academic career includes roles as assistant professor (2003–2015) and current professor status since 2015. He has directed NBU's Telecommunications bachelor's program (2005–2015) and actively participates in departmental councils and seminars. Education : PhD in Radio Transmitter and Receiver Equipment (2009), Master's in Telecommunications Systems (2003), Bachelor's in Telecommunications Systems (2001), Specialist degree in Telecommunications (1999) His research focuses on radio communications , wireless networks , and WEB technologies , with recent publications exploring 5G network optimization, microwave antennas for space-based radar, IoT vehicle tracking, and femtocell technologies. He has contributed to projects including satellite studio development, WLAN network analysis, and electromagnetic field safety studies. Recent publications highlight trends in 5G network algorithms , radio wave attenuation modeling , and AI-driven cybersecurity . His work often intersects telecommunications engineering with practical applications in smart city infrastructure and educational technology . He teaches courses such as: Network Administration and Virtualization Applied Software for Telecommunications Mobile Communications WEB Services Installation Computer Hardware/Software Installation Technical expertise includes LINUX systems , cloud computing , VoIP security , and WiMAX protocol analysis . He holds advanced English and Russian language proficiency alongside native Bulgarian.

• High-performance computer architectures
• Cloud-computing systems
• Internet-of-Things (IoT)
• +۷ مورد دیگر

Panayiotis D. Tsanakas is a Professor of Computer Engineering at the National Technical University of Athens (NTUA), serving in the School of Electrical and Computer Engineering within the Department of Information Technology and Computers. Since 2023, he has held the position of Dean of the School of Electrical and Computer Engineering at NTUA. His academic career spans several decades with significant contributions to computer engineering and informatics. His educational background includes a Dipl. in Electrical Engineering from Aristotle University of Thessaloniki (1982), an MSc in Computer Engineering from Ohio University (1985), and a PhD in Computer Engineering from National Technical University of Athens (1988). Tsanakas specializes in high-performance computer architectures, cloud-computing systems, Internet-of-Things (IoT), and AI-based applications in medicine and biology. His research spans parallel and distributed processing, computer architecture, supercomputing applications, and biomedical engineering. He has published over 100 research papers in established scientific journals and conferences, with recent work focusing on AI applications in healthcare, cloud computing security, and advanced medical monitoring systems. His recent publications demonstrate a strong trend toward applying artificial intelligence to healthcare challenges, particularly in mental health monitoring, medical imaging, and biomedical diagnostics. His work frequently combines computer architecture expertise with medical applications, creating innovative solutions for healthcare challenges. 2023-now: Dean of the School of Electrical and Computer Engineering at NTUA 2018-2020: Board member of the EuroHPC Joint Undertaking 2004-2019: Chairman of GRNET, the national research and education network 2005-2009: Member of EETT (The Hellenic Electronic Communications and Posts Regulator) Tsanakas has supervised eight doctoral dissertations and more than 50 diploma theses. He has co-authored seven textbooks on computer systems that have been adopted by several higher-education institutions. His professional experience includes significant roles in national infrastructure development, including leading the design and deployment of advanced networking and cloud services for Greek academic institutions and healthcare facilities.

• Smooth Transition Models
• State-Space Models
• Macroeconometrics
• +۸ مورد دیگر

Yukai Yang is a Senior Lecturer at the Department of Statistics, School of Economics and Business, Uppsala University. His research bridges econometric theory with applied statistical methods. Education : B.Eng, Shanghai Jiao Tong University Cand. Polit, University of Copenhagen PhD, Aarhus University (2012) His research spans smooth transition models , state-space models , Bayesian VAR , panel data analysis , and directional statistics , with recent interdisciplinary work in medical data science . Articles highlight methodological innovation in time series , machine learning , and mobile biometrics . Scientific Awards : None listed in provided data. His work includes software development (e.g., mfbvar package) and collaborations in multimedia systems and health economics .

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.

Torben Petersمشاهده پروفایل

مدرس

• 3D Computer Vision
• Deep Learning
• Generative Models
• +۲ مورد دیگر

Torben Peters is a Lecturer in the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on 3D computer vision, deep learning, and generative models applied to geospatial analysis and photogrammetry. Research Focus: Peters develops computational tools for processing LiDAR point clouds, aerial imagery, and satellite data. His work enables automated environmental monitoring (e.g., forest inventories and avalanche mapping) and urban modeling through advanced segmentation and 3D reconstruction techniques. Generative models like TetraDiffusion expand capabilities in geometric deep learning. Publication Trends: Recent articles emphasize scalable geospatial AI, including war damage assessment in Ukraine, global biomass datasets, and self-supervised shape completion. Methodological innovations center on reducing annotation dependencies and improving geometric accuracy. Teaching: Leads courses on image-based mapping and geodetic data processing at ETH Zürich.