جستجوی استاد راهنمای دانشگاهی

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +۵ 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.

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

Bernhard Thomaszewski is a Lecturer at the Department of Computer Science at ETH Zürich. His research focuses on computational mechanics, robotics, and computer graphics, with an emphasis on simulation-based design and material modeling. He explores topics such as deformable contact, flexible materials, and robotic mechanisms. His work bridges theoretical foundations and practical applications, including medical imaging, garment simulation, and biomechanical systems. Notable research interests include the development of novel algorithms for real-time simulation, optimization-driven design of mechanical systems, and integration of machine learning with physical models. He has contributed to advancements in finite element modeling, differentiable simulation, and topology optimization for robotic and biomedical applications. His recent projects highlight interdisciplinary collaboration, addressing challenges in areas like orthodontic treatment prediction, automated pipeline design, and neural network-driven material characterization. While no specific grants or awards are explicitly listed, his prolific publication record underscores his impactful contributions to computational engineering and computer science.

• Computer Graphics
• Robotics
• Computational Mechanics
• +۳ مورد دیگر

Dr. Stefano Buoso is a Lecturer at ETH Zürich's Department of Information Technology and Electrical Engineering, specializing in biomedical imaging and computational mechanics. His research focuses on cardiovascular modeling, fluid-structure interaction, and reduced-order modeling. He holds a position in the Professur für Biomedizinische Bildgebung (Biomedical Imaging Professorship). Key research areas include computational fluid dynamics applied to cardiovascular systems, development of machine learning tools for medical imaging (e.g., MRI and echocardiography), and creation of patient-specific numerical phantoms. His work bridges engineering and medicine, with applications in clinical diagnostics and personalized medicine. Recent articles emphasize innovations like automated 3D heart modeling from 2D echocardiography, synthetic MRI data generation, and turbulence analysis in aortic flow. He has developed open-source tools like CMRsim for cardiovascular simulations. Buoso collaborates on digital twin technologies for carotid artery disease assessment and fluid dynamics modeling of biological systems. His technical contributions include methods for rapid mesh generation, stabilized reduced-order models, and MRI-guided cardiac shape modeling. Research spans from microvascular neurovascular unit studies to bio-inspired membrane wing aerodynamics, reflecting interdisciplinary expertise.

• Biomedical Engineering
• Cardiovascular Modeling
• Computational Fluid Dynamics
• +۴ مورد دیگر

Professor Pascal Fua is a distinguished faculty member at EPFL (Swiss Federal Institute of Technology) in the School of Computer and Communication Science. He joined EPFL in 1996 and currently serves as Head of the Computer Vision Laboratory (CVLAB). His extensive research spans multiple cutting-edge areas in computer vision and geometric deep learning, with applications ranging from 3D reconstruction to medical imaging and aerodynamic optimization. Dr. Fua's research interests encompass Computer Vision, 3D Reconstruction, Shape Modeling, Geometric Deep Learning, Medical Image Analysis, Augmented Reality, Motion Recovery, Surface Mesh Processing, and Aerodynamic Shape Optimization. His work demonstrates a remarkable ability to bridge theoretical computer vision with practical applications across diverse domains. His research has evolved from traditional geometric computer vision techniques to incorporating deep learning approaches for 3D modeling, with recent focus on differentiable rendering, implicit surface representations, and applications in medical imaging and engineering design. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on differentiable iso-surface extraction, geometric deep learning for aerodynamic shape optimization, and novel approaches to 3D reconstruction. His work spans both theoretical advances in computer vision algorithms and practical applications in medical imaging, autonomous driving, and computational fluid dynamics. IEEE Fellow Multiple ERC Grants recipient Associate Editor of IEEE Transactions for Pattern Analysis and Machine Intelligence Throughout his career, Professor Fua has mentored numerous PhD students who have gone on to make significant contributions in computer vision and related fields. His laboratory has established collaborations across multiple disciplines, including medical imaging, aerospace engineering, and neuroscience, demonstrating the broad applicability of his research. His current work continues to push the boundaries of geometric deep learning and 3D vision, with particular emphasis on making these techniques more practical and applicable to real-world engineering and medical problems.

• Computer Vision
• 3D Reconstruction
• Shape Modeling
• +۶ مورد دیگر

Xu Chen is a doctoral researcher at ETH Zurich specializing in 3D generative models and neural implicit shape animation . His work focuses on creating photo-realistic simulations of human activity for applications in human-centric perception tasks .

• 3D Generative Models
• Computer Vision
• Human Activity Simulation
• +۵ مورد دیگر

Barbara Solenthaler is a Lecturer at the Department of Computer Science, ETH Zurich. Her research focuses on physics-based simulations, facial animation, and machine learning applications in computer graphics.

• Computer Graphics
• Physics-Based Simulations
• Facial Animation
• +۳ مورد دیگر

Federico Stella is a Researcher and Doctoral Assistant at the Computer Vision Laboratory (CVLAB) within the School of Computer and Communication Sciences (IC) at EPFL in Lausanne, Switzerland. His work focuses on advancing computer vision and 3D geometry processing through machine learning and neural network approaches. Research interests include implicit neural representations, 3D reconstruction, and applications of spherical CNNs for geometric problems. He has contributed to methodologies in mesh generation, surface detection, and self-supervised learning techniques. Recent publications highlight advancements in distance field networks, gradient-based surface detection, and differentiable meshing algorithms. His research bridges computer graphics and machine learning, with potential applications in robotics and virtual environments.

• Computer Vision
• Machine Learning
• 3D Geometry Processing
• +۴ مورد دیگر

Romain Guiet is a Scientist at the BioImaging and Optics Core Facility (BIOP) and a Lecturer in the EDMS program at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the School of Life Sciences (SV) and contributes to EPFL’s imaging community through technical support, teaching, and research. His roles span scientific support, education, and institutional service, including membership in the School Council SV. His research interests center on BioImage Analysis , with expertise in image acquisition (widefield, confocal, super-resolution), image processing , and quantitative data extraction . He develops open-source workflows using tools like ImageJ/Fiji, QuPath, Python, and R, promoting FAIR data principles. His background includes a PhD in Biology with a focus on immunology, cancer, 3D cell migration, and organoid systems. He supports experimental design and staining strategies for in vitro models including cell cultures and organoids. His recent publications highlight interdisciplinary work in organoid modeling, cardiogenesis, neural circuit imaging, and bioimage informatics. He co-developed DEVILS, a data visualization tool, and contributes to NEUBIAS training initiatives. His work emphasizes open science and community engagement through platforms like forum.image.sc. Metabolic reprogramming in osteoclasts Osteoclasts: Other functions RANKL-responsive epigenetic mechanism reprograms macrophages into bone-resorbing osteoclasts Nuclear receptors in osteoclasts WNT-modulating gene silencers as a gene therapy for osteoporosis, bone fracture, and critical-sized bone defects Romain Guiet advises on microscopy-based projects and mentors students through EPFL’s teaching programs. He is actively involved in grant-supported research and collaborative projects across EPFL. He is a key contributor to the BioImaging and Optics Core Facility, supporting a wide range of life science research. He also participates in the School Council SV, contributing to academic governance.

• BioImage Analysis
• Microscopy
• Image Processing
• +۱۵ مورد دیگر

Björn Ommer is a full Professor at Ludwig Maximilian University of Munich (LMU) where he heads the Computer Vision & Learning Group. Previously, he was a full professor at Heidelberg University and served as a director of the Interdisciplinary Center for Scientific Computing (IWR) and the Heidelberg Collaboratory for Image Processing (HCI). He is affiliated with multiple prestigious institutions including the Bavarian AI Council, ELLIS unit Munich, the Helmholtz Foundation, and the Munich Center for Machine Learning (MCML). Dr. Ommer received his PhD from ETH Zurich where he was awarded the ETH Medal for his dissertation 'Learning the Compositional Nature of Objects for Visual Recognition.' After completing his doctoral studies, he held a post-doctoral position in the Computer Vision Group of Jitendra Malik at UC Berkeley. His primary research interests span all aspects of semantic image and video understanding based on deep machine learning, with particular emphasis on generative approaches for visual synthesis (including Stable Diffusion), invertible deep models for explainable AI, deep metric and representation learning, and self-supervised learning paradigms. His work has significant interdisciplinary applications in digital humanities and neurosciences. His extensive publication record demonstrates a clear progression toward increasingly sophisticated generative models, culminating in the development of Stable Diffusion. His recent work focuses on improving diffusion models, exploring flow matching techniques, and developing more controllable generative systems with applications across multiple domains. German AI-Prize 2024 Technology-Prize of Eduard-Rhein-Foundation 2024 Nominated for German Future Prize of the President of Germany ELLIS Fellow ETH Medal for Dissertation Best Paper Award at CVPR'21 AI for Content Creation Workshop Professor Ommer serves as an associate editor for IEEE T-PAMI and has held significant leadership roles in major computer vision conferences including program chair for GCPR and Senior/Area Chair for CVPR, ICCV, ECCV, and NeurIPS. He delivered the opening keynote at NeurIPS'23 and has supervised numerous PhD students who have gone on to positions at leading technology companies including Amazon, Facebook, and Apple. His research group is located in downtown Munich and actively recruits talented students and researchers for cutting-edge work in computer vision and machine learning.

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. Hao Liu is a Researcher in the Department of Tissue Technology and Biofabrication at ETH Zurich's Institute for Biomechanics. His work intersects tissue engineering, biofabrication, and advanced computational techniques such as computer vision and 3D reconstruction. He focuses on interdisciplinary applications, including robotics and neural radiance fields (NeRF) for biomedical and robotic systems. Institution: ETH Zurich Role: Researcher (Staff of Professorship for Tissue Engineering and Biofabrication) Research interests span tissue engineering, biofabrication, and computational methods like 3D reconstruction, visual localization, and neural networks. His recent work emphasizes robust 3D scene understanding, camera pose estimation, and deep learning for multi-view stereo. Publications highlight advancements in relative pose estimation, neural radiance fields, and line-based geometric analysis, contributing to both robotics and biomedical engineering fields. He has no listed scientific awards, but his research demonstrates significant innovation in computational imaging and biofabrication. No advising or grant details are provided in the available texts. His affiliation with the Institute for Biomechanics suggests involvement in labs focused on biomechanical systems and biofabrication technologies.

• Tissue Engineering
• Biofabrication
• Computer Vision
• +۳ مورد دیگر

Marcel Padilla is Lecturer at ETH Zurich's Department of Computer Science, researching geometric algorithms for physical simulation and visualization. As Feodor-Lynen Fellow, he develops computational methods for fluid dynamics and astrophysical phenomena. Research integrates discrete exterior calculus with computer graphics to model complex systems like solar coronae and vortex dynamics. Recent publications present novel frameworks for rigid body motion estimation and magnetic filament simulation. Teaches geometry processing and mathematical visualization.

• Geometry processing
• Discrete differential geometry
• Physical simulation
• +۲ مورد دیگر