جست‌وجوی استاد و پژوهشگر

Kyros Kutulakosمشاهده پروفایل

استاد

• Computational Imaging
• Computer Vision
• 3D Sensing
• +۹ مورد دیگر

Kyros Kutulakos is a Professor in the Department of Computer Science at the University of Toronto, where he leads research in computational imaging and 3D sensing. His affiliations include the Toronto Computational Imaging Group, Computer Vision Group, Dynamic Graphics Project (DGP), and Vector Institute Group. He teaches graduate and undergraduate courses such as CSC320 (Introduction to Visual Computing) and CSC2530 (Computational Imaging & 3D Sensing). His research interests span computational imaging, non-line-of-sight imaging, single-photon detectors, 3D sensing, and neural rendering. Notable contributions include advancements in structured-light imaging, time-of-flight systems, and super-oscillatory microscopy. He has advised numerous PhD and MSc students, fostering cutting-edge research in imaging technologies. Kutulakos has received prestigious awards, including the Dean’s Research Excellence Award (2023) and multiple best paper prizes (e.g., Marr Prize at ICCV 2023). He has served as program chair for ICCV 2013, ICCP 2010, and CVPR 2003, contributing to academic leadership in computer vision. His work bridges optics, photonics, and computation, with applications in autonomous systems, medical imaging, and astronomy. Current research focuses on extreme imaging scenarios, such as imaging in pitch-black environments and around corners, leveraging novel sensor designs and computational techniques.

Nima Fazeliمشاهده پروفایل

استادیار

• Robotic Manipulation
• Embodied Intelligence
• Multimodal Representation Learning
• +۴ مورد دیگر

Nima Fazeli is an Assistant Professor of Robotics at the University of Michigan (2020–Present), holding courtesy appointments in Computer Science & Engineering (CSE) and Mechanical Engineering. He directs the Manipulation and Machine Intelligence (MMint) Lab, focusing on enabling dexterous robotic manipulation through multimodal representation learning, tactile sensing, and model-based reasoning. His work integrates mechanics, perception, controls, and planning to achieve autonomous interaction with uncertain environments. Education: PhD, MIT (2019); MSc, University of Maryland (2014); BSc, Amirkabir University of Technology (2011) Research interests emphasize embodied intelligence , including visuo-tactile fusion, contact dynamics modeling, and cross-modal learning. Recent work explores tactile shadows, deformable object manipulation, and language-guided robot control. His research is supported by the NSF CAREER grant and National Robotics Initiative, with applications in manufacturing, assistive robotics, and space systems. Publications span topics like tactile sensing hardware (e.g., GelSlim 4.0), visuo-tactile implicit representations (ViTaSCOPE), and failure recovery policies (Racer). His team’s work has been featured in outlets like The New York Times and BBC. Key Awards: NSF CAREER Grant (2024) Teaching includes Introduction to Robotic Manipulation . Collaborations involve cross-disciplinary projects with mechanical, electrical, and biomedical engineering groups.

• Computer Vision
• Computer Graphics
• Machine Learning
• +۱۱ مورد دیگر

Andrea Tagliasacchi is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), where he holds the Visual Computing Research Chair. He is also a part-time (20%) Staff Research Scientist at Google DeepMind in Toronto and holds an associate professor (status only) appointment in the Department of Computer Science at the University of Toronto. Education: PhD in Computing Science – Simon Fraser University (NSERC Alexander Graham Bell Fellow) Postdoctoral Research – École Polytechnique Fédérale de Lausanne (EPFL) MSc in Computer Science – Politecnico di Milano (Gold Medalist) His research lies at the intersection of computer vision, computer graphics, and machine learning, with a focus on 3D visual perception. Key areas include neural radiance fields (NeRF), 3D Gaussian splatting, inverse rendering, and geometric deep learning, with applications in robotics, augmented reality, and autonomous systems. His work emphasizes robust and efficient scene understanding and reconstruction from visual data. His recent publications, appearing in top venues like CVPR, SIGGRAPH, NeurIPS, and ECCV, demonstrate a strong emphasis on neural fields, 3D reconstruction, and generative modeling. Trends include improving rendering efficiency, enhancing robustness to noise and distractors, and enabling controllable and 3D-aware generation. His group has made significant contributions to Gaussian splatting, NeRF optimization, and diffusion-based 3D/4D synthesis. Scientific Awards: 2024 CVPR Best Paper Award (Honorable Mention) 2020 CVPR Best Student Paper Award 2015 SGP Best Paper Award NSERC Alexander Graham Bell Canada Graduate Scholarship MITACS Best Paper Award (SIGGRAPH Asia 2009) NSF Best Poster Award (SGP 2012) He has advised numerous PhD and MSc students, many of whom are now researchers at leading institutions and companies. His research has been supported through collaborations with Google, Intel, and academic partners. He serves the community as a Senior Area Chair for CVPR 2025, Associate Editor for IEEE TPAMI (2024–2026), Guest Editor for IEEE TPAMI on 3D GenAI, and Program Chair for 3DV 2024. He leads a vibrant research lab at SFU focused on pushing the boundaries of 3D scene understanding with machine learning.

Wojciech Matusikمشاهده پروفایل

استاد

• Computer Graphics
• Computational Design and Fabrication
• Computer Vision
• +۷ مورد دیگر

Wojciech Matusik is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Computational Design and Fabrication Group and is a member of the Computer Graphics Group. His research spans computer graphics, robotics, and AI-driven manufacturing, with a focus on computational design, tactile sensing, and material science. Matusik holds a PhD in Computer Science from MIT (2003), an MS from MIT (2001), and a BS from UC Berkeley (1997). His work includes groundbreaking projects like differentiable cloth simulation (DiffCloth), AI-enhanced molecular design, and tactile sensing gloves. He has received prestigious awards such as the MIT TR35 (2004), DARPA Young Faculty Award (2012), and Ruth and Joel Spira Teaching Award (2014). Matusik teaches courses on computer graphics, machine learning, and computational fabrication at MIT. Key research themes include: Robotics: Robotic assembly, tactile interaction, and soft robotics Graphics: 3D holography, procedural material generation Manufacturing: Additive fabrication, topology optimization His recent articles explore AI-driven molecular synthesis, holographic displays, and tactile-enabled VR systems. Matusik collaborates on open-source tools like the WiReSens tactile platform and Simit language for sparse systems.

Olga Sorkine Hornungمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• Geometry Processing
• +۳ مورد دیگر

Prof. Olga Sorkine Hornung is a Full Professor of Computer Science at ETH Zürich, leading the Interactive Geometry Lab. She holds a BSc and PhD from Tel Aviv University (2000 and 2006) and conducted postdoctoral research at Technical University Berlin. Her research focuses on computer graphics, geometric modeling, and geometry processing, with applications in shape editing, digital fabrication, and animation. She has received numerous accolades, including the ACM Fellowship (2020), ERC Consolidator Grant (2020), and the Golden Owl Teaching Award (2021). Her work bridges theoretical foundations and practical algorithms, addressing challenges in parameterization, surface compression, and interactive design tools. Her research interests span: Computer Graphics & Visualization Geometric Modeling & Processing 3D Content Creation & Digital Fabrication Garment Design & Simulation Human Motion Analysis & Animation Awards and grants include: 2024: Best Paper Honorable Mention (EUROGRAPHICS) 2023: Member of Swiss Academy of Engineering Sciences (SATW) 2020: ERC Consolidator Grant 2017: Rössler Prize (ETH Zurich) Her lab focuses on developing novel methods for interactive geometry processing, with recent advancements in garment modeling (e.g., AIpparel, Rags2Riches) and motion retargeting systems like WalkTheDog. She actively collaborates on interdisciplinary projects, including biomedical applications and sustainable fashion technology.

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.

Tzu-Mao Liمشاهده پروفایل

استادیار

• Computer Graphics
• Rendering Algorithms
• Differentiable Programming
• +۵ مورد دیگر

Tzu-Mao Li is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of California, San Diego (UCSD), affiliated with the Center for Visual Computing. His research focuses on differentiable graphics algorithms, combining classical visual computing with modern machine learning techniques. He holds a Ph.D. from MIT CSAIL under Frédo Durand and a postdoc at MIT and UC Berkeley with Jonathan Ragan-Kelley. His work spans rendering, programming languages for graphics, Monte Carlo methods, and inverse problems. Education: B.S. and M.S. from National Taiwan University (2011-2013), advised by Yung-Yu Chuang. Ph.D. from MIT CSAIL (Computer Graphics Group), advised by Frédo Durand. Postdoctoral research at MIT and UC Berkeley with Jonathan Ragan-Kelley. Research Interests: Differentiable rendering, Monte Carlo integration, programming language design for visual computing, physical simulation, adversarial machine learning, and applications in computer vision and robotics. Key areas include rendering algorithms (path tracing, bidirectional methods), optimization techniques (MCMC, gradient-based), and neural representations (SDFs, neural fields). Publications focus on advancing rendering algorithms, differentiable systems, and applications in inverse problems. Notable contributions include edge sampling for differentiable rendering, warped-area sampling, and diffusion models for BSDF sampling. Awards: ACM SIGGRAPH 2020 Outstanding Doctoral Dissertation Award, multiple Best Paper Awards at SIGGRAPH, and oral presentations at ICCV. Teaching: Courses include CSE 167 (Computer Graphics), CSE 168 (Rendering), and CSE 272 (Advanced Image Synthesis), emphasizing physically-based methods and programming.

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

Prof. Matthias Nießner is a Professor at the Technical University of Munich , where he leads the Visual Computing Lab . Prior to this, he held a Visiting Assistant Professor position at Stanford University . His work bridges computer vision , graphics , and machine learning , focusing on 3D reconstruction , semantic scene understanding , and AI-driven video synthesis . Prof. Nießner has published over 150 works in top venues like SIGGRAPH , CVPR , and ECCV , with several receiving best paper awards (SIGCHI’14, HPG’15, SPG’18, SIGGRAPH’16 Emerging Tech). His research has garnered international media attention, including features in the New York Times , Wall Street Journal , and MIT Technological Review , as well as TV demonstrations (e.g., Jimmy Kimmel Live for Face2Face technology). Awards : TUM-IAS Rudolph Moessbauer Fellowship (2017–ongoing) Google Faculty Award (2017) Nvidia Professor Partnership Award (2018) ERC Starting Grant (2018, €1.5M) Eurographics Young Researcher Award (2019) Research Trends : 3D Gaussian Splatting for real-time rendering Neural Radiance Fields (NeRF) with mesh supervision Audio-driven facial animation via diffusion models Latent space diffusion for 3D scenes Self-supervised and zero-shot methods for 3D and image analysis As a co-founder and director of Synthesia Inc. , he drives democratization of synthetic media. His YouTube channel has over 5 million views, reflecting his impact beyond academia.

David B. Lindellمشاهده پروفایل

استادیار

• Computational Imaging
• Computer Vision
• Non-Line-of-Sight Imaging
• +۵ مورد دیگر

David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.

• 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 .

Bernt Schieleمشاهده پروفایل

استاد

• Computer Vision
• Multimodal Sensor Processing
• Machine Learning
• +۳ مورد دیگر

Prof. Bernt Schiele is a Max Planck Director at the Max Planck Institute for Informatics and holds a Professorship at Saarland University. His research focuses on understanding multimodal sensor data, with key areas in computer vision, 3D object recognition, and machine learning. He leads the Computer Vision and Machine Learning group, addressing challenges in sensor fusion, scene understanding, and human activity recognition. Schiele has held academic roles at TU Darmstadt, ETH Zurich, and MIT, and contributes to top journals like IEEE Transactions on PAMI and conferences like ECCV. His work emphasizes robust models, interpretability, and domain adaptation for real-world applications. Education: PhD (1997, Grenoble), MSc (1994 Karlsruhe/1993 Grenoble) Key Positions: MIT (1997-2000), ETH Zurich (1999-2004), TU Darmstadt (2004-2010) Research interests span 3D scene understanding, multimodal sensor processing, and machine learning techniques for large-scale data. His recent work advances robust object detection, explainable AI, and domain-invariant training methods. He also chairs major conferences like ECCV 2018 and co-chairs ICCV 2011. Publications highlight innovations in interpretable vision transformers, certified explanations, and test-time adaptation. Despite no listed awards, his contributions shape foundational areas of computer vision and multimodal AI.

Justus Thiesمشاهده پروفایل

استاد

• Computer Graphics
• Computer Vision
• Neural Rendering
• +۳ مورد دیگر

Prof. Justus Thies is Full Professor for 3D Graphics & Vision at the Technical University of Darmstadt and leads the Neural Capture & Synthesis research group at the Max Planck Institute for Intelligent Systems. His research develops AI methods to capture and synthesize the real world using commodity hardware, focusing on markerless motion capture of faces and bodies, and photorealistic neural rendering. His work has been recognized with the German Pattern Recognition Award, Eurographics Young Researcher Award, and an ERC Starting Grant (all 2024). Recent publications focus on Gaussian-based avatars, neural human reconstruction, and diffusion models for scene synthesis.

Marc Stammingerمشاهده پروفایل

استاد

• Computer Graphics
• 3D Reconstruction
• Neural Rendering
• +۷ مورد دیگر

Prof. Marc Stamminger is a Professor of Visual Computing at FAU since 2002, leading the Chair of Computer Science 9 (Computer Graphics). His work focuses on algorithms for synthesizing and analyzing images through 3D modeling, LiDAR/Radar capture, and light simulation. He co-leads FAU Solar, applying 3D modeling for environmental lighting analysis under varying conditions. Stamminger has published over 250 papers, winning prestigious awards like the Siggraph Test-of-Time Award. He holds executive roles in Eurographics and is Vice Dean of FAU's Technical Faculty. Research interests span neural rendering , 3D reconstruction , radar imaging , and medical visualization . Recent work emphasizes radiance field rendering (e.g., VR-Splatting, INPC) and radar-based human motion tracking. His lab's FAU Solar project integrates large-scale 3D models with environmental lighting simulations. Publications trends highlight neural rendering optimizations , radar-MIMO systems , and agricultural digital twins . Key collaborations involve medical imaging (e.g., vocal fold reconstruction) and autonomous driving data generation. Awards: Siggraph Test-of-Time (2023?), 2× Siggraph Best-Of-Show Grants/Teams: FAU Solar Lab, Eurographics leadership, FAU Vice Dean Labs: Chair of Computer Science 9, FAU Solar Initiative

Shang-Tse Chenمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Cybersecurity
• +۵ مورد دیگر

Shang-Tse Chen is an Associate Professor at the Department of Computer Science and Information Engineering and Graduate Institute of Networking and Multimedia , National Taiwan University . He leads the NTU AI Security Lab , focusing on applied and theoretical machine learning with emphasis on cybersecurity, adversarial ML, and ML privacy/fairness. Education: PhD in Computer Science (Georgia Tech, 2019), BSc in CSIE (NTU, 2010) Awards: K. T. Li Young Researcher Award (2025), IBM PhD Fellowship (2018), KDD Best Student Paper Runner-Up (2016), NSF SaTC Grant (2017-2021) His research spans adversarial ML, certified defenses, model inversion attacks, and intersection with differential privacy/fairness. Recent work includes physical adversarial attacks on object detectors and practical defenses using JPEG compression. He teaches courses like Security and Privacy of Machine Learning and Introduction to Medical Informatics . Key publication trends show focus on adversarial robustness (ICML/NeurIPS/ICLR), cybersecurity applications (ACSAC), and ML fairness (ACL/EMNLP). Collaborations include industry partnerships with Intel Labs and Symantec. Scientific Awards: K. T. Li Young Researcher Award (2025) ACM TiiS Best Paper Honorable Mention (2020) IBM PhD Fellowship (2018) KDD Audience Appreciation Award Runner-Up (2018) Symantec Fellowship Runner-Up (2016) KDD Best Student Paper Runner-Up (2016) NSF Grant (2017) He advises 13 current students (PhD/MS/Undergrad) and has mentored alumni now at CMU/UC Berkeley. The lab actively recruits postdocs and students across levels.