جست‌وجوی استاد و پژوهشگر

Richard Palmerمشاهده پروفایل

پژوهشگر

• 3D Facial Analysis
• Rare Disease Diagnosis
• Medical Imaging
• +۳ مورد دیگر

Dr. Richard Palmer is a Researcher at Curtin University, affiliated with the School of Earth and Planetary Sciences (EPS) within the Faculty of Science and Engineering. He holds a portfolio role in the Office of the Provost, demonstrating leadership in academic governance. His research focuses on innovative applications of 3D facial analysis for rare disease diagnosis, treatment monitoring, and public health initiatives. Key collaborations include the CLINIFACE project, which integrates non-rigid registration techniques for phenotypic visualization. Palmer’s work spans clinical research, medical imaging, and interdisciplinary projects such as the Western Australian Undiagnosed Diseases Program. His expertise bridges computer science, genetics, and public health, addressing challenges like autism spectrum disorder analysis and hereditary conditions like Silver Russel syndrome and hereditary angioedema. He has pioneered open-source tools for 3D facial growth curves and mobile-based 3D facial reconstruction. His publications emphasize methodological advancements in 3D modeling, algorithmic comparisons for spatial analysis, and the impact of BMI on facial clustering studies. Collaborators include institutions like the Human Phenotype Ontology consortium and global medical research networks. Palmer actively contributes to precision medicine through both technological innovations and policy-driven healthcare solutions.

• Geometric Shape Analysis
• Deep Learning
• Computer Vision
• +۵ مورد دیگر

Maks Ovsjanikov is a Professor in the Department of Computer Science at École Polytechnique , France, and a Visiting Research Scientist at Google DeepMind. His research focuses on geometric 3D shape analysis , deep learning for non-rigid shape comparison , and applications of AI to scientific discovery , supported by the ERC Consolidator Grant VEGA . Research Interests : 3D shape processing, functional maps, deep learning, cryo-EM analysis, and robust non-rigid matching. Awards : ERC Consolidator Grant, Best Paper Awards at 3DV 2022 and 2019, Best Student Paper Award at 3DV 2020, Replicability Stamp (2017). Teams : Member of the GeomeriX team at LIX, École Polytechnique. Recent work trends highlight his leadership in zero-shot 3D keypoint detection , cross-domain video model evaluation , and diffusion-based shape matching priors . His students, including Souhaib Attaiki , have received top recognition like the IP Paris Best PhD Thesis award .

• Geometric Analysis
• Differential Geometry
• Relativity Theory
• +۴ مورد دیگر

Martin Kell is a Senior Lecturer at the Department of Mathematics within the Faculty of Mathematics and Natural Sciences at the University of Tübingen. His work bridges geometric analysis and optimal transport theory , with a focus on metric spaces, curvature conditions, and non-linear PDEs. Education : Diplomarbeit (Master's Thesis): Conley Index of Isolated Equilibria , University of Rostock (2010) Dissertation: On Curvature Conditions Using Wasserstein Spaces , University of Leipzig (2014) Research Interests Kell explores the interplay of geometry and analysis , particularly in generalized curvature , geometric flows , and curvature-isoperimetry relations . His work includes studying metric spaces where Sobolev spaces are non-linear, aiming to define analogues of Alexandrov curvature for non-Riemannian settings. Publications Trends Kell’s research spans geometric analysis , differential geometry , and optimal transport . Key themes include non-linear gradient flows , Ricci curvature bounds , and metric space convergence . Teaching He has taught advanced courses such as: Optimal Transport (2016/2017): Covering transport mappings, entropy functionals, and geometric inequalities. Linear Partial Differential Equations (2017): Focusing on elliptic/parabolic equations, Sobolev spaces, and regularity theory. Limits of Spaces (2019): Introducing Gromov-Hausdorff convergence, Alexandrov geometry, and stability theorems. Contact: room_c6p34@uni-tuebingen.de (reconstructed from departmental contact structure).

Michael Wandمشاهده پروفایل

استاد

• Computer Graphics
• Machine Learning
• Geometry Processing
• +۵ مورد دیگر

Michael Wand is a Professor at the Institute of Computer Science , Johannes Gutenberg University Mainz , leading the Visual Computing Group . His research bridges Computer Graphics and Computer Vision , focusing on data-driven methods and structure-aware processing of geometric data. Research Themes Machine learning for geometric data analysis Symmetry and regularity detection in 3D shapes Generative models of visual data Big data in geometry processing Publication Trends : His recent work (2015-2016) explores integrating Markov Random Fields and Convolutional Neural Networks for image synthesis, generative adversarial networks for real-time texture synthesis, and algebraic models for parameterized shape editing. Earlier contributions (2007-2013) focus on point cloud processing , non-rigid shape reconstruction , and interactive editing of complex geometries. Academic Background : Doctoral degree (2004) in Computer Science from University of Tübingen Diploma (2000) in Computer Science from Universität-GH Paderborn

Sami Brandtمشاهده پروفایل

استاد

• Mathematical imaging
• Computer vision
• Machine learning
• +۴ مورد دیگر

Sami Brandt is a Professor in Data Science and Machine Learning at the IT University of Copenhagen. Specializing in Audio-Visual Computing, they lead a research group focused on cutting-edge applications in computer vision, neural networks, and extended reality (XR). Their work bridges mathematical imaging with practical technological solutions. Research Interests: Latent Space Analysis in Diffusion Models Non-rigid Structure-from-Motion Techniques Human Motion Prediction and Smoothing Algorithms Tensor-based Methods for Emotion Recognition Metaverse Applications for Parkinson's Disease Therapies Scientific Awards: Best Student Paper Award (2022), 11th International Conference on Pattern Recognition Applications and Methods Projects: MotiVerseP (2024-2025): Motivation for movement therapies in a metaverse for Parkinson's disease XTREME (2024-2026): Extended Reality Environment for Immersive Experience of Art and Music

Giovanni Trappoliniمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Multimodal Deep Learning
• +۵ مورد دیگر

Giovanni Trappolini serves as Assistant Professor at Sapienza University of Rome within the Department of Computer, Control, and Management Engineering, conducting research at the RSTLess Lab under Prof. Fabrizio Silvestri. Previously, he completed his Ph.D. in Machine Learning at Sapienza under Prof. Emanuele Rodolà, following an MSc in Data Science where he graduated cum laude as a Sapienza honor graduate. His educational background includes: MSc in Data Science, Sapienza University of Rome (cum laude, Sapienza honor graduate) Ph.D. in Machine Learning, Sapienza University of Rome (2022) BSc from Luiss Guido Carli Trappolini's research bridges Machine Learning and Deep Learning with emphases on multimodal systems and information retrieval . He pioneers applications in Graph Neural Networks security, Federated Learning architectures, and Italian-language Large Language Models —notably creating Fauno , Italy's leading LLM. His work extends to operating system innovation through generative AI, 3D shape analysis using transformers, and creative applications like AI-driven music generation. Analysis of his 2023 publications reveals dominant trends toward integrating retrieval systems with generative models (RAG), developing robust neural databases, and enhancing cross-modal understanding. Key themes include adversarial defense for graph networks, privacy-preserving federated retrieval, and transformer-based geometric learning—demonstrating consistent focus on foundational AI infrastructure. Scientific recognition includes: Sapienza honor graduate Cum laude graduate distinction Trappolini actively contributes to academic instruction through courses including Advanced Data Mining and Language Technologies (Sapienza, 2023) and multiple iterations of Python Programming for Data Science (2019-2023). He maintains significant research collaborations with Stanford, Technion, Meta, Amazon, TII, and UniPi while preparing new PhD-level coursework in Geometric Deep Learning for 2024. His research operates within the RSTLess Lab ecosystem, focusing on scalable AI systems and multimodal integration.

Dan B Goldmanمشاهده پروفایل

دانشیار مدعو

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

Dan B Goldman is an affiliate associate professor at the University of Washington's Computer Science and Engineering department, while serving as a Principal Engineer and AI/ML architect for Lucasfilm & ILM. His career bridges academia and industry, with prior roles at Adobe's Creative Technologies Lab (2007-2015) and Google (2015-2024), where he led computer vision R&D for Project Starline. He is a member of the Visual Effects Society and holds a PhD in Computer Science (2007) from the University of Washington. Research Focus: Computer graphics, computer vision, artificial intelligence, and human-computer interaction Notable Projects: Content-Aware Fill (Photoshop), Project Starline (Google), HyperNeRF (neural radiance fields), and Nerfies (deformable 3D modeling) Academic Legacy: Mentored by David Salesin, with an Erdős-Bacon number of 6 (via Rose Byrne and David Salesin's academic lineage). His work appears in top venues like SIGGRAPH, CVPR, and ECCV, covering topics from neural rendering to 3D reconstruction. Industry Impact: Pioneered technologies used in major films (Star Wars, Jurassic Park) and Adobe Photoshop features. His recent work at Google Labs (2022-2024) focused on generative media, reflecting his dual expertise in creative tools and computational methods.

• Computer Graphics
• Physics-Based Simulation
• Fluid Dynamics
• +۳ مورد دیگر

Prof. Dr. Jan Bender holds a professorship in Computer Animation at RWTH Aachen University's College of Engineering. As a leading researcher in physics-based simulation methods, his work focuses on developing advanced numerical techniques for fluid dynamics, deformable solids, and multi-physics interactions through Smoothed Particle Hydrodynamics (SPH) and Finite Element Methods (FEM). Key Contributions: Invented PF-FLIP for two-phase flows, developed SymX symbolic framework for energy-based simulations, created STARK unified solver for robotics applications, and introduced implicit boundary handling for SPH Methodologies: Specializes in hybrid Eulerian/Lagrangian approaches, differentiable physics, adaptive discretization, and machine learning integration for simulation acceleration Research Impact: 2023 & 2024 Best Paper Awards in VMV and SCA conferences. His work enables billion-particle fluid simulations and realistic multi-body interactions for robotics, with applications in welding, thermal spraying, and soft robotics. Collaborations: Works extensively with robotics institutes (Gazebo Fluids extension) and materials science departments (TIG welding, thermal spray modeling). Maintains open-source code repositories for simulation frameworks.

Matej Kristanمشاهده پروفایل

استاد

• Computer Vision
• Visual Object Tracking
• Detection and Segmentation
• +۳ مورد دیگر

Matej Kristan is a Full Professor at the Faculty of Computer and Information Science , University of Ljubljana, where he serves as Vice Chair of the Department of Artificial Intelligence . He leads the Visual Object Tracking (VOT) Initiative , presides over the IAPR Slovenian Pattern Recognition Society , and acts as Associate Editor for IJCV . PhD from Faculty of Electrical Engineering, UL (2008) Co-organized 13+ workshops/conferences Research Interests focus on: Visual Object Tracking : Long-term re-detection, transparent object tracking, segmentation-based methods Autonomous Boats : Obstacle detection, sensor fusion, maritime navigation Physics-Informed Deep Learning : Sea level forecasting (HIDRA models), climate prediction Surface Defect Detection : Real-time deep architectures for cracks/dents Scientific Excellence : 26 Research Excellence Awards (Slovenian Research Agency) 10 Teaching Excellence Awards (UL/FRI) Best Paper Awards: ISPA2015, BMVC2022, Pattern Recognition Journal 2024 Student Mentorship : Supervised 8 PhD students including Alan Lukežič (summa cum laude 2021), Domen Tabernik , and Peter Uršič . His students have won 23 awards including multiple Prešeren Awards and Uroš Seljak distinctions. Laboratory Involvement : Key member of the Visual Cognitive Systems Laboratory , contributing to multimodal perception systems for maritime robotics, educational robotics platforms, and advanced deep learning architectures.

Slobodan Ilicمشاهده پروفایل

استاد مدعو

• 3D Computer Vision
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

PD Dr. Slobodan Ilic is a Senior Key Expert Research Scientist at Siemens AG (since 2014) and an Adjunct Professor at the Chair of Computer Science Applications in Medicine , Technical University of Munich (TUM). His work bridges 3D computer vision and medical imaging , with a focus on real-time object detection, deformable surface modeling, and depth data analysis. Current Roles: Adjunct Professor at TUM, Senior Key Expert at Siemens Research Themes: 6D pose estimation, non-rigid 3D reconstruction, RGB-D data processing Labs: CAMP Chair (TUM), Siemens AG Research Division His recent work explores LLM-driven control systems , semantic-aware 3D generation , and cross-modal medical imaging . Articles highlight advancements in point cloud registration , rotation-invariant descriptors , and hyperspectral calibration . While no specific awards are documented in the provided text, his team at Siemens/TUM advises PhD candidates in 3D vision for robotics and medical applications .

David Heldمشاهده پروفایل

دانشیار

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

David Held is an Associate Professor at Carnegie Mellon University's Robotics Institute, leading the Robots Perceiving And Doing (RPAD) lab. His work focuses on perceptual robot learning, integrating robotics, machine learning, and computer vision to enable robots to interact with complex environments. He holds a Ph.D. in Computer Science from Stanford University, an M.S. and B.S. in Mechanical Engineering from MIT, and conducted postdoctoral research at UC Berkeley. His research spans object manipulation, autonomous driving, and reinforcement learning, with a focus on robust perception and control in dynamic settings. Research Interests: Developing methods for robots to manipulate novel objects, handle deformable materials, and operate in unstructured environments through deep learning and simulation-to-real transfer. He explores autonomous driving via self-supervised learning and semi-supervised techniques. Notable Articles (2024–2025): Focus on articulated object manipulation, sim2real transfer, safety-aware policies, and perception in robotics. Recent work includes ArticuBot for universal manipulation policies and SplatSim for zero-shot transfer using Gaussian splatting. Awards: Google Faculty Research Award (2017), NSF CAREER Award (2021). Labs: RPAD Lab, CMU Center for Autonomous Vehicle Research. Teaching: Courses include Statistical Techniques in Robotics and Advanced Computer Vision.

Minhyuk Sungمشاهده پروفایل

دانشیار

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

Minhyuk Sung is an Associate Professor in the School of Computing at KAIST, where he leads the KAIST Visual AI Group. He is also affiliated with the Graduate School of AI and the Metaverse Program. Previously, he worked as a research scientist at Adobe Research. He received his Ph.D. from Stanford University under the supervision of Leonidas Guibas and completed his M.S. and B.S. degrees at KAIST. Dr. Sung's research focuses on generating, manipulating, and analyzing various visual data, including images, videos, and 3D data. His work primarily centers around diffusion models, flow models, and other generative AI techniques applied to visual content. He has made significant contributions to the fields of 3D vision, text-to-image generation, and multi-modal AI systems. His recent publications demonstrate a strong trend toward improving the quality, efficiency, and controllability of generative models, with particular emphasis on 3D-aware generation, synchronization techniques, and grounding visual content with linguistic descriptions. His work spans multiple top-tier conferences including NeurIPS, CVPR, ICCV, and SIGGRAPH. Asiapgraphics Researcher Award (2024) Dr. Sung actively mentors students and recruits new members for his research group. He serves as an area chair for conferences like 3DV 2026 and frequently gives talks at major institutions including NVIDIA, Google, Stanford, and various universities across Asia. His research has attracted significant attention in both academic and industrial circles. He teaches advanced courses including Diffusion and Flow Models (CS492(C)), Machine Learning for 3D Data (CS479), and Diffusion Models and Their Applications (CS492(D)), demonstrating his commitment to training the next generation of AI researchers.

Michael Posaمشاهده پروفایل

استادیار

• Robotics
• Control Theory
• Machine Learning
• +۳ مورد دیگر

Michael Posa is an Assistant Professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania's School of Engineering and Applied Science. He also holds affiliations with the Departments of Computer and Information Science, and Electrical and Systems Engineering. As the head of the Dynamic Autonomy and Intelligent Robotics (DAIR) Lab, part of the GRASP Lab, his research focuses on control, learning, and planning for robots interacting dynamically and safely with complex environments. Key interests include non-smooth dynamics of contact, machine learning, and numerical optimization, with applications in legged robots and robotic manipulation. His work emphasizes computationally efficient algorithms for real-time control and decision-making. Recent achievements include the 2024 Best Paper Award for contributions to multi-contact model predictive control. He actively mentors students like Brian Acosta and William Yang, whose theses address bipedal walking and dynamic manipulation. The DAIR Lab collaborates on interdisciplinary projects and participates in robotics conferences like ICRA. Michael’s lab emphasizes diversity and innovation, recruiting students across MEAM, ESE, and CIS departments. His research bridges theory and practice, with publications spanning model reduction for legged systems, vision-based contact localization, and impact-aware control strategies. Ongoing efforts explore contact-implicit MPC frameworks and integrating physics-driven perception (e.g., Vysics) for robust autonomy.

• Computer Graphics
• Geometry Processing
• Differentiable Rendering
• +۳ مورد دیگر

Marc Alexa is a Professor at the Technische Universität Berlin , leading the Department of Computer Graphics within the Institute of Computer Engineering and Microelectronics . His research spans computational geometry, rendering, and human-computer interaction, with a focus on differentiable rendering, mesh processing, and geometric modeling. PhD (2002) and MSc (1997) from Technische Universität Darmstadt under advisors Prof. Encarnacao and Prof. Gross (ETH Zurich) Research interests include: Differentiable rendering frameworks Geometric stylization and manifold reconstruction Optimization of polygonal and tetrahedral meshes Eye-tracking applications in mental imagery and visual perception Scientific awards include: ACM Fellow (2024) SIGGRAPH Academy inductee (2022) ERC Advanced Grant for geometric computation (2022) Eurographics Fellow (2018) Engineering Science Prize (2012) His recent publications (2023-2025) focus on differentiable acoustic path tracing, Poisson manifold reconstruction, and shadow mapping optimization. He has served as Editor-in-Chief of ACM Transactions on Graphics (2018-2021) and chaired key conferences like SIGGRAPH 2013 Technical Papers .

Lyle Noakesمشاهده پروفایل

استاد

• Computer Vision
• Optimal Control
• Variational Problems
• +۳ مورد دیگر

Lyle Noakes is a Professor in the Department of Mathematics and Statistics at The University of Western Australia (UWA), within the School of Physics, Maths and Computing. His research focuses on geometric optimization, differential geometry, optimal control, and variational problems. He has contributed to advancements in computer vision, spline theory, and Riemannian manifolds. Noakes has held grants related to nonlinear approximation theory, geometry in computer vision, and inverse scattering. His work spans theoretical developments and applications in fields such as robotics path planning and quantum algorithms. Research Interests: Geometric optimization of curves and surfaces Optimal control in non-Euclidean spaces Applications of differential geometry to computer vision Scattering theory and inverse problems Recent articles highlight innovations in minimizing curvature/spirality in curves, convergence analysis of numerical methods, and geometric rigidity. His work on Napoleonic triangles in hyperbolic and spherical geometries demonstrates cross-disciplinary geometric exploration. Noakes has advised 10 students and collaborated on projects funded by ARC Small Grants and UWA initiatives.