جستجوی استاد راهنمای دانشگاهی

Youngjoong Kwonمشاهده پروفایل

استادیار

• Computer Vision
• Computer Graphics
• Human Performance Capture
• +۳ مورد دیگر

Youngjoong Kwon is an incoming Assistant Professor in the Department of Computer Science at Emory University (Fall 2025), with postdoctoral research experience at Stanford University's Translational AI Lab and Vision and Learning Lab, and Carnegie Mellon University's Robotics Institute. He holds a PhD in Computer Science from the University of North Carolina at Chapel Hill and a BS from Yonsei University, Korea. Education: PhD, Computer Science, University of North Carolina at Chapel Hill (2023) BS, Computer Science, Yonsei University (2017) Research: His work bridges Computer Vision and Computer Graphics, focusing on human performance capture, avatar modeling, neural rendering, and telepresence systems. Recent publications demonstrate expertise in sparse view synthesis, deformable light fields, and real-time 3D avatar generation. Awards: Bronze Prize, 27th HumanTech Paper Award, Samsung Electronics Co., Ltd. (2022) Academic Engagement: Active as Area Chair (3DV 2026) and reviewer for top conferences (CVPR, ECCV, NeurIPS, etc.) and journals. Collaborations: Hosted by institutions including University of British Columbia, Max Planck Institute for Intelligent Systems, Stanford University, Adobe Research, NVIDIA Research, Texas A&M University, and Brown University for invited talks.

Feng Xuمشاهده پروفایل

دانشیار

• 3D Vision
• Computer Graphics
• Digital Health
• +۴ مورد دیگر

Feng Xu is an Associate Professor at the School of Software, Tsinghua University, Beijing, China. His research bridges computer science , medicine , and neuroscience , focusing on interdisciplinary applications in 3D vision, digital health, and human motion capture. Education : Ph.D. in Automation (2007-2012) and B.S. in Physics (2003-2007), both from Tsinghua University. Research Interests : 3D Vision, Computer Graphics, Digital Health, Physics-based Modeling, and Real-time Human Motion Tracking. His work explores 3D vision and graphics through applications like IMU-based motion capture , NeRF editing , and facial geometry reconstruction . Recent publications emphasize integrating physics-aware models with deep learning for real-time performance, particularly in healthcare contexts such as mediastinal neoplasm diagnosis and radiology report generation . Notable contributions include the EditableNeRF framework for topology editing and Transformer IMU Calibrator for dynamic sensor calibration. His work has received the Best Student Paper Award at IEEE ICME 2019 . Contact : Email: xufeng2003@gmail.com Email: feng-xu@tsinghua.edu.cn

Congyi Zhangمشاهده پروفایل

پژوهشگر ارشد

• Computer Science
• Neural Shape Representation
• 3D Reconstruction
• +۳ مورد دیگر

Congyi Zhang is a postdoctoral research and teaching fellow in the Department of Computer Science at the University of British Columbia. He earned his Ph.D. in Computer Science from Peking University and a B.Sc. in Mathematics from Fudan University. Prior to his current role, he served as a research associate at the Department of Computer Science, Hong Kong University, and as a visiting researcher at the Max Planck Institute for Informatics. His research focuses on neural shape representation, AI-driven 3D content generation, human-computer interaction, and 3D reconstruction techniques. He has contributed to methods that improve geometric accuracy, optimize sampling strategies, and enable efficient local editing of 3D models. Recent publications highlight advancements in neural implicit fields, multiview diffusion models, and efficient 3D mesh creation from 2D inputs. His work balances theoretical innovation with practical applications in digital media, orthodontics, and archaeology. He is currently transitioning to an Assistant Professor position at the Computer Science Department, University of Texas at Dallas, where he seeks motivated Ph.D. students for Spring/Fall 2026 cohorts.

Jiatao Guمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a planned start in Fall 2025. He also works as a part-time Staff Research Scientist at Apple (MLR) and previously served as a full-time Research Scientist at Meta AI (FAIR Labs). His academic journey includes a Ph.D. in Electrical and Electronic Engineering (2018) from the University of Hong Kong under Prof. Victor O.K. Li, and a B.Eng. in Electronic Engineering (2014) from Tsinghua University.

Xudong Xuمشاهده پروفایل

پژوهشگر

• Embodied AI
• 3D Scene Generation
• Generative Modeling
• +۳ مورد دیگر

Xudong Xu is a researcher at Shanghai Artificial Intelligence Laboratory, specializing in Embodied AI with a focus on interactive scene generation and 3D object synthesis. He earned his Ph.D. in Information Engineering from The Chinese University of Hong Kong (2018-2023), advised by Prof. Dahua Lin, and holds a Bachelor's degree in Automation from Nanjing University (2014-2018), where he ranked first in his cohort. His research explores compositional scene generation, procedural modeling of articulated objects, and material-aware 3D synthesis. He has published in top venues like SIGGRAPH Asia, ECCV, NeurIPS, and contributed to projects such as MATLABER and RoomTex. His work leverages Gaussian splatting, PBR materials, and occupancy fields for high-fidelity 3D environments. Xudong has received prestigious awards including the Shanghai Leading Talent Program, CUHK Postgraduate Scholarship, and National Scholarship at Nanjing University. He serves as a reviewer for major AI/ML conferences like CVPR, ICCV, and NeurIPS, and mentors interns from institutions such as University of Virginia and UC San Diego.

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.

Srinath Sridharمشاهده پروفایل

استادیار

• 3D Computer Vision
• Artificial Intelligence
• 4D Spatiotemporal Understanding
• +۶ مورد دیگر

Srinath Sridhar is the John E. Savage Assistant Professor in the Department of Computer Science at Brown University, where he leads the Interactive 3D Vision & Learning Lab (IVL). He is also an Amazon Scholar and a Visiting Faculty at the Indian Institute of Science (IISc). His research lies at the intersection of 3D computer vision and artificial intelligence, with a focus on 4D spatiotemporal visual understanding of dynamic scenes involving objects, humans in motion, and their interactions. He develops foundational methods for perceiving and reasoning about 3D environments over time. The recent publications reflect a strong trend in learning-based 3D reconstruction, spatiotemporal modeling, and human-centric vision tasks. His work combines geometric principles with deep learning to solve challenging problems in visual understanding. NSF CAREER award Google Research Scholar award Best Student Paper award at WACV 2025 Best Paper Honorable Mention award at Eurographics 2019 Srinath advises research at Brown through the IVL lab and has received competitive external funding, including the NSF CAREER and Google Research Scholar awards. His interdisciplinary work bridges computer vision, AI, and robotics. He collaborates with institutions such as Stanford University, Max Planck Institute, and Indian Institute of Science. He leads the Interactive 3D Vision & Learning Lab (IVL) at Brown University, which focuses on advancing 3D and 4D visual intelligence through deep learning and geometric reasoning. The lab emphasizes open science, with several public repositories on GitHub.

Bastian Wandtمشاهده پروفایل

پژوهشگر

• Human Motion Capture
• 3D Reconstruction
• Machine Learning
• +۳ مورد دیگر

Bastian Wandt is a Researcher at the University of British Columbia in the group of Helge Rhodin, and will join Linköping University as an Assistant Professor in September 2022. He holds a PhD from Leibniz University Hannover (2015–2020) and a Mechatronics degree (B.Sc. 2012, M.Sc. 2015) from the same institution. His research focuses on human motion capture, 3D reconstruction, and machine learning, particularly deep learning techniques for weakly supervised training and probabilistic modeling. He founded the AI consulting company HEUWA in 2018 and has taught courses such as DATA 311 at UBC. Research interests include applying neural networks and dimensionality reduction to human pose estimation, with special emphasis on monocular video analysis and physics-based models. Key contributions involve unsupervised learning methods like ElePose and CanonPose , as well as anomaly detection frameworks like Same Same But DifferNet . Awards include the KI Talent Award (2020, €4,000) for his dissertation. He has supervised numerous theses on topics like pose space modeling, recurrent neural networks for motion capture, and texture synthesis. His work bridges computer vision, robotics, and medical imaging applications.

• Video coding
• Video analysis
• Video adaptation
• +۳ مورد دیگر

Fernando Manuel Bernardo Pereira is a Full Professor at the Telecommunications Institute of Instituto Superior Técnico (IST), Lisbon. His research focuses on video coding, multimedia representation standards, and advanced multimedia systems. He actively contributes to academic and industry collaborations on standards like JPEG Pleno and security frameworks for media annotation. Key Affiliations : Instituto Superior Técnico, Telecommunications Institute Roles : Full Professor, Researcher in Multimedia Communication His scientific interests span video analysis, adaptation techniques, and multimedia services. Recent work emphasizes deep learning applications in point cloud coding, quality metrics for 3D data, and explainable AI in facial recognition systems. He collaborates internationally on projects involving light field technology and trustworthy media systems. Publications reflect a focus on cutting-edge areas like neural network-based image coding, compression artifact analysis, and capsule networks for expression recognition. While no awards are explicitly listed, his prolific publication record indicates significant contributions to the field. No advising students or grant details are provided in the text. His research teams likely engage in collaborative efforts given his work on standards and multi-institutional responses to industry calls for evidence.

• Computer Graphics
• Computer Vision
• Machine Learning
• +۹ مورد دیگر

Corey Toler-Franklin is an Assistant Professor in the Computer Science Department at Barnard College, Columbia University, and Director of the Graphics, Imaging & Light Measurement Lab (GILMLab). Her research focuses on computer graphics, vision, and AI applications in biomedical research, archaeology, and cultural heritage preservation. She holds a Ph.D. in Computer Science from Princeton University and has held academic roles at the University of Florida and industry positions at Autodesk, Adobe, and Google. Her educational background includes a B.Arch. from Cornell University and an M.S. in Computer Graphics from Cornell’s Program of Computer Graphics. Notable awards include the 2009 Google Anita Borg Award and recognition as the first African American to earn a Ph.D. in CS from Princeton. Her work bridges computational methods with real-world applications in healthcare and cultural preservation, including AI for cancer diagnosis and 3D digitization of fossils. Publications highlight innovations in multispectral rendering, material style transfer, and medical imaging analysis. Collaborations span oncology, neuroscience, and archaeological institutions like the Akrotiri Laboratory. The GILM Lab emphasizes interdisciplinary projects such as the iDigFossils initiative, engaging K-12 students in STEM through 3D printing and exploration of fossils.

Yaoyao Liuمشاهده پروفایل

استادیار

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

Yaoyao Liu is an Assistant Professor at the University of Illinois Urbana-Champaign, holding joint appointments in the School of Information Sciences and Coordinated Science Laboratory. He is affiliated with the Siebel School of Computing and Data Science, National Center for Supercomputing Applications (NCSA), and Illinois Informatics. His research focuses on computer vision and machine learning, particularly in areas like continual learning, few-shot learning, and 3D geometry modeling. He completed his PhD at the Max Planck Institute for Informatics and a BS at Tianjin University. Research Interests: Continual learning and class-incremental learning Generative models and diffusion models 3D geometry and medical imaging applications Awards: ECVA PhD Award (2024). Professional Activities: Area Chair for CVPR, NeurIPS, ICLR, and other top conferences. Served as Senior Program Committee member for AAAI and IJCAI. Labs/Teams: Active in interdisciplinary projects at NCSA and Illinois Informatics, collaborating on medical imaging and 3D vision challenges.

Huy Voمشاهده پروفایل

دانشیار

• Big Data Systems
• Data Science
• High-Performance Computing
• +۳ مورد دیگر

Huy Vo is an Associate Professor in the Department of Computer Science at the City College of New York (CCNY), part of the City University of New York (CUNY) system. His research focuses on Big Data Systems, Data Science, High-Performance Computing, Large-Scale Visualization, and Scalable Display Building. He is affiliated with CCNY's School of Engineering and maintains an active website at https://www-cs.ccny.cuny.edu/~hvo/ . His work spans urban analytics, visualization technologies, and machine learning applications. Notable contributions include analyzing global urban road networks using GNNs, developing semiconductor-specific large language models (SemiKong), and optimizing fleet management for ride-pooling in NYC. He also explores public health technologies, such as asthma self-management apps and pandemic policy impact visualizations. Vo's research often integrates interdisciplinary approaches, combining geospatial data, computational modeling, and real-time systems. His recent work highlights trends in scalable computing, AR visualization, and data-driven decision-making in urban environments. No scientific awards are explicitly mentioned in the provided text. His advising and grants sections remain unspecified, but his extensive publication record indicates active research collaborations and grant-funded projects. The SAT-Hub project exemplifies his focus on smart urban infrastructure, while his work on visualization frameworks like Unitypic underscores innovations in interactive 3D systems.

• Computer Graphics
• 3D Modeling
• Computer Animation
• +۷ مورد دیگر

Marie-Paule Cani is a Professor of Computer Science at École Polytechnique (Institut Polytechnique de Paris), where she serves as Dean of the Master of Science and Technology program and leads the STREAM research team at LIX Laboratory (CNRS/IP Paris). Her research bridges computer graphics, HCI, and AI, focusing on expressive 3D modeling, animation, and virtual environments. Education: 1987: M.Sc. in Computer Science, École Normale Supérieure & University Paris XI 1990: Ph.D. in Computer Graphics, University Paris XI 1995: Habilitation in Computer Science, Institut National Polytechnique de Grenoble Research Interests: She pioneers user-centered tools for 3D content creation, combining gesture-based interfaces (sketching, sculpting) with knowledge-enhanced procedural models. Key areas include implicit surfaces, physical simulation, AI-driven animation, and accessible tools for non-experts. Her work enables intuitive design of complex shapes, terrains, and dynamic scenes. Publication Trends: Recent work (2023–2025) emphasizes AI-integrated graphics, including controllable natural phenomena (volcanoes, erosion), sketch-based bio-visualization, reinforcement learning for animation, and real-time character control. Cross-disciplinary applications span geology, biology, and cinematography. Awards & Honors: ACM Steven A. Coons Award (2023) French Academy of Sciences (2020) ACM SIGGRAPH Academy (2019) ERC Advanced Grant EXPRESSIVE (2012–2017) CNRS Silver Medal (2012) Leadership & Grants: She founded research teams (STREAM, Imagine, Evasion) and secured major funding: ERC Advanced Grant, Google-École Polytechnique AI Chair (2018–2021), Horizon 2020 project CLIPE (2020–2024), and Hi!Paris Creative AI Fellowship (2021–2024). She mentors researchers in geometric modeling, animation, and AI. Labs & Teams: Directs the Modeling, Simulation & Learning pole at LIX (2019–2023). Founded STREAM (Structure Representation for Animation/Modeling) and Imagine (Inria/Grenoble teams), advancing real-time graphics and procedural content.

• Computer Graphics
• Virtual Reality
• 3D Terrain Modeling
• +۲ مورد دیگر

Oscar Argudo Medrano is a researcher at the Universitat Politècnica de Catalunya's Facultat d'Informàtica de Barcelona (FIB) , affiliated with the ViRVIG research group (Visualization, Virtual Reality, and Graphic Interaction). His work focuses on computer graphics and 3D terrain modeling , with recent publications in Computer Graphics Forum , ACM Transactions on Graphics , and Cartography and Geographic Information Science . He collaborates extensively with Carlos Andújar, Antonio Chica, and Nuria Pelechano. Email: oargudo@cs.upc.edu Research Areas : 3D terrain analysis and synthesis Environmental simulation Vegetation and ecosystem modeling Procedural content generation Cultural heritage digitization Recent Article Trends : His recent publications emphasize terrain descriptors , glacier modeling , tree variations , and mechanical weathering simulations , with applications in virtual reality , digital heritage , and ecosystem visualization . Collaborative projects like European Cloud for Heritage OpEn Science highlight his work in cultural preservation and environmental impact analysis .

Russell Poldrackمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Neuroinformatics
• Decision-Making
• +۵ مورد دیگر

Russell Poldrack is the Albert Ray Lang Professor of Psychology at Stanford University , with an additional courtesy appointment in Psychiatry and Behavioral Sciences. He serves as Department Chair in Psychology, Associate Director of the Stanford Data Science Initiative, and Director of the Center for Open and Reproducible Science. A British Academy Fellow (2023), Poldrack leads research at the intersection of cognitive neuroscience and neuroinformatics , focusing on decision-making, executive control, and open science frameworks like NeuroSynth and OpenNeuro. Developed fMRIPrep pipeline for standardized neuroimaging preprocessing Championed reproducibility in fMRI analysis through multiverse analysis frameworks Created hyve visualization engine for composite neuroimaging representations His recent publications address decision-making mechanisms (eLife 2025), fMRI reliability (Nature Human Behaviour 2024), and machine learning reproducibility (Science Advances 2024). Poldrack's lab has produced over 150 published reuses of shared neuroimaging data through the OpenNeuro platform, while his Poldrack Lab continues to explore self-regulation ontology in clinical populations. He currently oversees 13 independent studies through Stanford's Neuroimaging Data Science program. Major awards: OHBM Open Science Award , Wiley Young Investigator (2005) Professional roles: Board of Scientific Counselors, NIH NIMH; Former Human Connectome Project EAB Chair Methodological contributions: BIDS standard adoption, MRIQC quality control protocols