جستجوی استاد راهنمای دانشگاهی

Shubham Tulsianiمشاهده پروفایل

استادیار

• Computer Vision
• 3D Vision
• Robot Learning
• +۴ مورد دیگر

Shubham Tulsiani is an Assistant Professor at Carnegie Mellon University's Robotics Institute, where he leads the Computer Vision group and the Physical Perception Lab. His research focuses on inferring physically and spatially grounded representations from perceptual inputs, with applications in 3D vision, robot manipulation, and neural scene reconstruction. He directs an active research group with multiple PhD and Master's students. Research interests center on 3D scene understanding , robot learning , and generative modeling , with specific emphasis on: self-supervised perception, neural rendering, multi-view geometry, manipulation from visual inputs, and physics-based reasoning. The lab develops methods that leverage physical world constraints as supervisory signals. Recent publications demonstrate strong focus on diffusion models for 3D tasks , sparse-view reconstruction , and robotic manipulation transfer . Key trends include neural inverse rendering, view synthesis from limited observations, and translating human interactions to robot actions. Awards include: Best Student Paper Award at CVPR 2015 Advising includes supervision of 5 PhD students, 4 MS students, and undergraduates. Lab alumni hold positions at Google, Stanford, Meta, and Princeton. The Physical Perception Lab collaborates with FAIR Pittsburgh and the CMU Computer Vision group.

• Computer Graphics
• Computer Vision
• Light Field Cameras
• +۷ مورد دیگر

Ravi Ramamoorthi is the Ronald L. Graham Professor of Computer Science and Director of the UC San Diego Center for Visual Computing. He holds a faculty position in the Department of Computer Science and Engineering (CSE) and is an affiliate of the Department of Electrical and Computer Engineering (ECE). He joined UC San Diego in 2014, previously at UC Berkeley and Columbia University. He also holds a part-time appointment as a Distinguished Research Scientist at NVIDIA. His research focuses on visual computing, including rendering, computer vision, light field cameras, and physics-based modeling. Notable contributions include foundational work on spherical harmonic lighting, neural radiance fields (NeRF), and Monte Carlo rendering techniques. His work bridges graphics, vision, and signal processing with applications in sparse reconstruction, importance sampling, and real-time rendering. He teaches courses like CSE 167 (Computer Graphics), CSE 168 (Rendering), and advanced topics in computer graphics. Awards include ACM and IEEE Fellowships, the Okawa Foundation Grant, and multiple Frontiers of Science Awards. His research is supported by NSF, ONR, and industry collaborators including Adobe, Sony, and Qualcomm. Key projects include the Center for Visual Computing, Light Field research, and educational initiatives like edX MOOCs on computer graphics and rendering. His work has influenced industry tools (e.g., Pixar, RenderMan) and modern real-time rendering pipelines with denoising techniques.

Jonathan T. Barronمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Generative AI
• +۳ مورد دیگر

Jonathan T. Barron is a Researcher at Google DeepMind in San Francisco, specializing in Computer Vision , Neural Rendering , and 3D Scene Reconstruction . He earned his PhD at UC Berkeley under Jitendra Malik and has pioneered advancements in NeRF (Neural Radiance Fields) and diffusion-based 3D generation. Research Interests : Computer Vision, Deep Learning, Generative AI, Image Processing, and 3D Reconstruction via Radiance Fields. His work includes Bolt3D for rapid 3D scene generation, CAT3D/CAT4D for text-to-3D/4D, and Zip-NeRF for anti-aliased radiance fields. He has also developed real-time rendering frameworks like SMERF and NeRF-Casting for reflections. Scientific awards: PAMI Young Researcher Award He has served as Area Chair for CVPR, ICCV, and NeurIPS, and his research is widely adopted in applications like Google's Lens Blur , Portrait Mode , and Jump VR .

Wenzel Jakobمشاهده پروفایل

دانشیار

• Inverse Graphics
• Differentiable Rendering
• Physically Based Rendering
• +۷ مورد دیگر

Wenzel Jakob is an Associate Professor and leader of the Realistic Graphics Lab at EPFL's School of Computer and Communication Sciences , currently on sabbatical at the University of Tokyo until Fall 2025. His work bridges inverse graphics , physically based rendering , and compiler/systems research , with a focus on developing robust differentiable rendering frameworks. Key research themes include: Backpropagation through rendering algorithms for inverse problems Material appearance modeling and optical measurement systems Compiler design for differentiable rendering pipelines Manifold sampling techniques and light transport derivatives His group created Mitsuba renderer , Dr.Jit , and Instant Meshes (recipient of the SGP Software Award). Recent publications (2021–2024) explore volumetric rendering, SDF-based differentiable systems, and efficient Monte Carlo estimators. Awards include the ACM SIGGRAPH Significant Researcher Award , Eurographics Young Researcher Award , and ERC Starting Grant . Teaching roles (2016–2024) span Advanced Computer Graphics and Numerical Methods for Visual Computing courses at EPFL.

• Remote Sensing
• Bidirectional Reflectance
• Image Processing
• +۳ مورد دیگر

Craig Coburn is a Full Professor in the Department of Geography and Environment at the University of Lethbridge, where he has served since 2002 with promotions to Associate Professor in 2009 and Full Professor in 2019. His research centers on remote sensing physics, specializing in bidirectional reflectance properties and the development of low-cost remote sensing instrumentation for environmental monitoring and satellite calibration. Dr. Coburn's academic foundation includes: B.Sc. (Honours) in Geography from the University of Saskatchewan (1994) M.Sc. in Geography from the University of Alberta (1996) Ph.D. in Geography/Remote Sensing from Simon Fraser University (2002) His work spans instrument design to data processing algorithms, with emphasis on surface bidirectional reflectance. He pioneered world-leading goniometers and low-cost camera systems deployed via aircraft, UAVs, and high-altitude balloons for agricultural monitoring, biological system analysis, and global satellite validation. His research bridges theoretical physics with practical environmental applications. Analysis of his 2017-2021 publications reveals sustained focus on radiometric calibration, BRDF characterization, and sensor development. Key trends include interdisciplinary collaborations in atmospheric science (airborne metals), ecology (riparian systems), and soil science (erosion detection), alongside growing emphasis on UAV platforms and low-cost sensor validation for democratizing remote sensing. No scientific awards were specified in the source material. Dr. Coburn holds Principal Investigator status for the Prairie Farm Rehabilitation Administration-funded cattle wintering sites project ($45,000) and contributes as Co-Investigator to Alberta Ingenuity Centre for Water Research initiatives totaling $568,000. His grant portfolio spans riparian ecology, watershed analysis, and historical projects including National Land and Water Information System (Agriculture Canada) and Mountain Pine Beetle monitoring. His laboratory innovations include robotic goniometers for surface reflectance measurement and thermal imaging systems deployed globally for satellite calibration, supporting both research objectives and hands-on student training in remote sensing physics.

James F. O'Brienمشاهده پروفایل

استاد

• Computer Graphics
• Computer Animation
• Physical Simulation
• +۷ مورد دیگر

Dr. James F. O'Brien is a Professor of Computer Science at the University of California, Berkeley, affiliated with research centers including the Berkeley Artificial Intelligence Research Lab (BAIR) and the Visual Computing Lab (VCL). His research focuses on computer graphics, animation, physical simulation, and image forensics, with applications in film, gaming, and virtual reality. O'Brien pioneered destruction modeling techniques used in over 200 films and games, earning an Academy Award in 2015. He holds leadership roles in tech companies like Juice Labs and Get Klothed, and has advised on patent litigation cases. Education: PhD in Computer Science (Georgia Tech, 2000), MS (Georgia Tech, 1997), BS (Florida International University, 1992). Research Interests: Computer Animation & Simulation Image/Video Forensics Human Perception of Motion VR/AR Privacy & Motion Data Machine Learning Applications Awards: Recipient of the 2015 Academy Award for Technical Achievement, ACM Distinguished Scientist (2009), and MIT TR-35 Innovator (2004). His work spans over 50,000+ VR user studies and groundbreaking contributions to cloth simulation and destruction modeling. Current Projects: Exploring ethical implications of extended reality (XR) motion data, developing privacy-preserving VR systems, and advancing AI-driven animation techniques.

Charles M. Bachmannمشاهده پروفایل

استاد

• Hyperspectral Remote Sensing
• Radiative Transfer Modeling
• Goniometers
• +۷ مورد دیگر

Charles M. Bachmann is a Professor at the Chester F. Carlson Center for Imaging Science , part of the College of Science at Rochester Institute of Technology (RIT) . He also holds the Frederick and Anna B. Wiedman Chair and serves as the CIS Graduate Program Coordinator since 2016. His research focuses on hyperspectral remote sensing of coastal and desert environments, with expertise in BRDF and radiative transfer modeling, goniometer development, and manifold/graph algorithms for multi-sensor imagery analysis. Recent work emphasizes UAS-based soil moisture and carbon mapping for climate studies. Education : AB in Physics (Princeton, 1984), Sc.M. (1986) and Ph.D. (1990) in Physics (Brown University). Scientific Awards : U.S. Patents for hyperspectral remote sensing methods. Teaching : Radiometry, Radiative Transfer, Mathematical Methods of Imaging Science, and graduate thesis/research courses. Students : Mentored research on soil moisture, coastal biomass, and UAS applications.

Hankui Zhangمشاهده پروفایل

دانشیار

• Remote Sensing
• Environmental Monitoring
• Deep Learning
• +۴ مورد دیگر

Hankui Zhang is an Associate Professor in the Department of Geography and Geospatial Sciences at South Dakota State University (SDSU), and a Research Scientist at the Geospatial Sciences Center of Excellence. He holds a Ph.D. from the Chinese University of Hong Kong (2013), specializing in satellite image fusion. His research focuses on developing algorithms for medium-resolution satellite data processing (e.g., Landsat and Sentinel-2), including cloud masking, BRDF correction, and compositing. He also explores AI applications in remote sensing for land cover mapping and environmental monitoring. As a Landsat Science Team member, he contributes to global remote sensing initiatives. Education: B.S. in Geographic Information Systems, Zhejiang University (2007) M.S. in Remote Sensing, Zhejiang University (2010) Ph.D. in Geography and Resource Management, Chinese University of Hong Kong (2013) Research Interests: Deep learning applications in remote sensing, land cover dynamics, analysis-ready data development, and geospatial data harmonization. His work emphasizes operationalizing satellite data for environmental decision-making. Grants & Awards: Over $2.5M in grants as PI/co-PI, including USDA and NASA-funded projects. Notable awards include the SDSU Wadsworth Research Award (2020-2021) and the Global Scholarship for Research Excellence from CUHK (2011-2012). Professional Roles: Editorial board member for Remote Sensing of Environment and Remote Sensing ; guest editor for special issues on deep learning in remote sensing. Top 20 reviewer for Remote Sensing of Environment (2020, 2022-2024). Key Contributions: Published over 70 SCI papers, developed cloud detection algorithms (e.g., LANA), and pioneered analysis-ready data workflows for global monitoring. His work bridges satellite data science with practical environmental applications.

Alexander Dammمشاهده پروفایل

استاد

• Remote Sensing
• Earth Observation
• Imaging Spectroscopy
• +۷ مورد دیگر

Alexander Damm is a Professor and head of the Remote Sensing of Water Systems (RSWS) group, holding a joint appointment between the Department of Geography at the University of Zurich (UZH) and the Swiss Federal Institute of Aquatic Science and Technology (Eawag). His research integrates advanced Earth observation technologies with environmental science to study water systems under changing climatic and anthropogenic pressures. University: University of Zurich Institutional Affiliation: Swiss Federal Institute of Aquatic Science and Technology (Eawag) Department: Department of Geography Alexander Damm obtained his MSc and PhD in remote sensing from Humboldt-University Berlin. Since 2008, he has been contributing to UZH’s leadership in imaging spectroscopy and Earth observation science. MSc, Remote Sensing, Humboldt-University Berlin PhD, Remote Sensing, Humboldt-University Berlin His research focuses on the fundamentals of remote sensing as applied to terrestrial and aquatic ecosystems, particularly in studying water dynamics and environmental change impacts. He specializes in sun-induced chlorophyll fluorescence (SIF), imaging spectroscopy, and the development of methods to monitor ecosystem productivity, drought responses, and biogeochemical cycles. His work bridges physics, ecology, and environmental engineering to improve understanding of plant-water relations and ecosystem resilience. The recent publications highlight a strong trend in using airborne and satellite-based spectroscopy to assess vegetation health, water stress, forest dynamics, and atmospheric constituents. Key themes include SIF retrieval, drought monitoring, canopy structure modeling, and air quality estimation. These works span applications from croplands and forests to tundra and inland waters, reflecting a broad interdisciplinary approach grounded in quantitative remote sensing. Alexander Damm is involved in several high-profile research initiatives, including: ESA’s FLuorescence EXplorer (FLEX) mission SNSF projects: FLUO4ECO, Spatial-sustainable-finance, DeltAs MeteoSwiss: UrbanNature EU Horizon: NextGenCarbon He leads the RSWS group, which develops and applies cutting-edge remote sensing methodologies for water system monitoring. The team collaborates across disciplines and institutions, focusing on integrating field measurements, airborne campaigns, and satellite data for environmental assessment. Damm’s leadership in projects like FLEX and HyPlant underscores his role in advancing spectroscopic remote sensing for global ecosystem monitoring.

Xin Zhangمشاهده پروفایل

استادیار

• Smart/Digital Agriculture
• Artificial Intelligence (AI) in Agriculture
• Computer Vision
• +۵ مورد دیگر

Dr. Xin Zhang is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University. His research focuses on smart agriculture, AI applications, agricultural robotics, and unmanned aerial systems. He leads the Sensing & Automation in Agrisystems (SAAS) Lab, which develops technologies for crop monitoring, robotic harvesting, and machine vision systems. Dr. Zhang holds a Ph.D. from Washington State University and has held postdoctoral positions at UC Davis. He is a FAA-certified UAS pilot and actively collaborates on projects involving machine learning, computer vision, and precision agriculture. Research interests include digital agriculture frameworks, crop prediction models, and the integration of AI into farming systems. Key achievements include pioneering work in robotic cotton-picking simulations and UAV-based yield estimation. His lab emphasizes interdisciplinary approaches combining robotics, data science, and environmental engineering. Awards include the 2020 Giuseppe Pellizzi Prize for agricultural engineering research and multiple ASABE accolades. Current projects involve scalable field extraction frameworks using satellite imagery and deep learning models for plant phenotyping. He has published over 50 peer-reviewed articles, with a focus on advancing automation in crop handling and environmental sensing technologies. Education: Ph.D., Biological & Agricultural Engineering (WSU), M.S., Agriculture (Northwest A&F University), B.S., Agronomy (Gansu Agriculture University).

Jeppe Revall Frisvadمشاهده پروفایل

دانشیار

• Computer Graphics
• Digital Image Synthesis
• Realistic Rendering
• +۷ مورد دیگر

Jeppe Revall Frisvad is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), within the Visual Computing group. His work bridges computer graphics, material science, and applied optics, focusing on realistic rendering and material appearance modeling. Ph.D. in Computer Graphics, DTU Informatics (2008) M.Sc. in Engineering (Applied Mathematics), DTU (2004) Education in University Teaching, LearningLab DTU (2008–2009) His research centers on computing material appearance from physical and chemical properties, developing faster and more accurate physically based rendering methods. Key areas include light scattering, bidirectional reflectance distribution functions (BRDF), anisotropy, and translucency modeling. Applications span computer games, movies, digital prototyping, architectural visualization, and training simulators. His recent publications (2024–2025) reflect a strong trend in digitizing material appearance, especially for 3D printing and food science, using spectrophotometry and optical validation. The work combines computer graphics with interdisciplinary applications in food structure and biological imaging, emphasizing measurement, modeling, and simulation. Scientific Awards: Research travel prize from AEG Elektronfonden (2006) IGDA scholarship for GDCE 2005 DTU Ph.D. scholarship (2004) He actively supervises Ph.D. students and leads multiple research projects, including those on organ-on-chip imaging, cheese quality analysis, and optical modeling of teeth. He collaborates across disciplines and institutions, with external research stays at UC San Diego and the University of Otago. He is involved in projects integrating AI with 3D imaging and material digitization. Jeppe is a key member of the Visual Computing research environment at DTU, contributing to both fundamental rendering algorithms and practical applications in industry and science.

• Interactive Global Illumination
• Perceptual Graphics
• Non-physical Graphics
• +۱ مورد دیگر

Tobias Ritschel is a Professor of Computer Graphics at University College London . His research spans advanced rendering techniques, perceptual modeling, and data-driven graphics, with a focus on bridging physical accuracy and artistic flexibility in visual computing. Key research themes include: Interactive Global Illumination : Real-time simulation of complex lighting effects on GPUs Perceptual Graphics : Human vision-driven rendering and display optimization Non-physical Graphics : Beyond-photorealistic techniques for artistic expression Data-driven Graphics : Leveraging large datasets for novel rendering and modeling approaches His recent work emphasizes neural rendering , differentiable graphics , and X-ray tomography , with applications in 3D reconstruction , NeRF manipulation , and holographic imaging . Notable scientific achievements include the Eurographics Young Researcher Award 2014 and Eurographics Thesis Award 2011 . He has advised multiple PhD students including Philipp Henzler (EG PhD Award 2024) and Thomas Leimkühler (Otto Hahn Medal 2019), while actively contributing to conference leadership as co-chair for EGSR 2024 and Pacific Graphics 2024 . His team collaborates on X-ray reconstruction with Pablo Villanueva-Perez and works on 3D perception with Anthony Steed.

Mathias Kneubühlerمشاهده پروفایل

مدرس ارشد

• Remote Sensing
• Imaging Spectroscopy
• Earth Observation
• +۷ مورد دیگر

Mathias Kneubühler is a Senior Lecturer and Scientific Section Head of Remote Sensing Spectroscopy at the Department of Geography, Faculty of Science, University of Zurich. He leads the Spectroscopy Lab and has been a key figure in advancing imaging spectroscopy and Earth observation research. He is actively involved in international remote sensing initiatives, including serving as the Swiss ESA-DOSTAG Delegate and Co-Chair of the EARSeL Special Interest Group on Imaging Spectroscopy. PhD in Remote Sensing, University of Zurich (1997–2002) MSc in Geography, University of Zurich (1992–1996) His research focuses on imaging spectroscopy , spectro-directional measurements , and ecosystem monitoring , with applications in plant phenology , biodiversity assessment , and land surface dynamics . He investigates how multi-angular and hyperspectral data can improve the understanding of vegetation structure, function, and environmental responses. His work integrates field spectroscopy, airborne and satellite remote sensing, and advanced data processing techniques. The recent publications highlight a strong trend in imaging spectroscopy for ecological applications, including plant trait mapping , camouflage detection , tree species identification , and phenology modeling . These studies often involve UAV and satellite data fusion, BRDF correction, and machine learning, demonstrating interdisciplinary innovation in remote sensing science. Co-Chair, EARSeL Special Interest Group on Imaging Spectroscopy (2016–present) President, Swiss Commission for Remote Sensing (2015–2020) Swiss ESA-DOSTAG Delegate (2004–2024) He has supervised several PhD and Master’s students, including Marius Vögtli, Christian Rossi, and Mojdeh Miraki, contributing to the next generation of remote sensing scientists. His leadership in organizing scientific workshops and campaigns underscores his role in fostering collaborative research. He is associated with the Spectroscopy Lab at the University of Zurich, which conducts cutting-edge research in spectro-directional measurements and environmental monitoring.

• computer vision
• computer graphics
• visual perception

Giuseppe Claudio Guarnera serves as a Senior Researcher at the Norwegian University of Science and Technology (NTNU) in the Department of Computer Science, leading the Research Council of Norway-funded "Spectraskin" project on spectral skin modeling. He concurrently holds a Lecturer position in Computer Vision and Graphics at the University of York (UK) and has been a Guest Researcher at Justus-Liebig University Giessen's Department of Psychology since 2017. His academic background includes: Ph.D. in Computer Science from the University of Catania, Italy, with a dissertation in computer vision and pattern recognition Foundational research experience at the USC Institute for Creative Technologies (US) Guarnera's research centers on computer vision, computer graphics, and visual perception applications, with specialized expertise in spectral skin reflectance modeling, BRDF representation, and material acquisition for virtual environments. His work integrates perceptual validation to enhance realism in digital face rendering and fabric modeling, bridging theoretical computer graphics with practical VR applications. Analysis of his 2017-2020 publications reveals a consistent focus on material and skin reflectance measurement techniques, perceptual validation frameworks, and cross-renderer parameter remapping. Key trends include the development of spectral skin modeling for realistic digital faces (notably in the Spectraskin project), practical BRDF acquisition methods, and single-image fabric reconstruction—demonstrating strong interdisciplinary connections between computer vision, graphics, and human perception studies. Guarnera secures competitive research funding as Principal Investigator of the "Spectraskin" young research talents project awarded by the Research Council of Norway, which investigates spectral skin modeling and rendering for realistic digital faces. He directs the Spectraskin project team at NTNU, which specializes in advancing spectral skin modeling techniques through interdisciplinary collaboration between computer vision, graphics, and perceptual psychology researchers.

• Computer Vision
• Computer Graphics
• Machine Learning
• +۴ مورد دیگر

Dr. Marc Habermann is a Tenured Senior Researcher and Scientific Manager of the Real Virtual Lab at the Max Planck Institute for Informatics (Department 6: Visual Computing and Artificial Intelligence). He leads the Graphics and Vision for Digital Humans research group, focusing on cutting-edge technologies in Computer Vision, Computer Graphics, and Machine Learning. His work emphasizes real-time human performance capture, photorealistic animation synthesis, and generative 3D human models derived from video data. Research Interests: Computer Vision, Computer Graphics, Machine Learning, Human Performance Capture, Non-Rigid Deformation Reconstruction, Neural Rendering, and Motion Capture. His contributions span topics like Gaussian splats optimization, sparse-view avatar synthesis, and physics-based cloth simulation. Awards: Saarland University Associate Fellow (2025) Key Publications (2025): Second-order Optimization of Gaussian Splats with Importance Sampling (arXiv) GIGA: Generalizable Sparse Image-driven Gaussian Avatars (arXiv) EVA: Expressive Virtual Avatars from Multi-view Videos (Siggraph 2025) Labs & Projects: Manages the Real Virtual Lab and heads the Graphics and Vision for Digital Humans group, advancing technologies for digital human avatars and immersive telepresence systems.