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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.

• Computer Graphics
• Geometric Modeling
• Geometry Processing
• +3 more

Dr. Xiao Zhou is a Research Fellow affiliated with the Professorship for Consumer Behavior at ETH Zurich. His current position began in 2021. Prior to this, he conducted postdoctoral research at the University of Reading (2020-2021) and completed his PhD in Consumer Behavior at the University of Copenhagen (2015-2019). His research focuses on advancing consumer behavior studies through interdisciplinary approaches, combining insights from marketing, behavioral economics, and human motion analysis. Zhou’s work emphasizes understanding decision-making processes and their applications in real-world contexts. Education history includes a PhD in Consumer Behavior from the University of Copenhagen (2015-2019), followed by postdoctoral training at the University of Reading (2020-2021). His academic trajectory reflects a strong foundation in both theoretical and applied research. His research interests span consumer behavior, marketing research methodologies, and behavioral economics. Notably, his recent publications explore cutting-edge technologies in motion capture and computer vision, including real-time human motion tracking and generative models for 3D reconstruction. This reflects a unique intersection of consumer behavior studies with technical advancements in computational methods. Zhou’s research outputs demonstrate expertise in areas such as inertial sensor-based motion analysis, physics-aware tracking systems, and hierarchical modeling techniques. His contributions highlight innovative solutions for challenges in human pose estimation and 3D reconstruction. He is currently based at ETH Zurich’s Department of Consumer Behavior, contributing to both academic research and potential industrial applications of his work.

• Consumer Behavior
• Marketing Research
• Behavioral Economics
• +3 more

Siyu Tang is an Assistant Professor in the Department of Computer Science at ETH Zürich, where she leads the Computer Vision and Learning Group (VLG) at the Institute of Visual Computing. Her research focuses on computational models for human perception and digitalization through computer vision and machine learning. Her educational background includes: PhD in Computer Science, Max Planck Institute for Informatics (2017), supervised by Prof. Bernt Schiele Master of Science in Media Informatics, RWTH Aachen University Bachelor of Science in Computer Science, Zhejiang University, China Dr. Tang specializes in human-centric computer vision, developing statistical models for motion analysis, pose estimation, and digital human creation. Her work integrates machine learning with optimization techniques to enable machines to interpret human activities from visual data, with applications spanning virtual reality, healthcare, and human-computer interaction. Key research thrusts include generative models for content creation, egocentric vision, and human motion synthesis. Her recent publications (2024-2025) demonstrate intense focus on 3D human modeling and neural rendering, with Gaussian splatting emerging as a dominant technique for efficient avatar creation and scene reconstruction. Significant themes include text-driven motion synthesis using diffusion models, relightable avatars, surgical training applications, and egocentric multimodal pretraining. This work bridges computer vision, graphics, and machine learning to advance human digitalization. No scientific awards were mentioned in the provided text. Dr. Tang leads the VLG research group at ETH Zürich, mentoring PhD and Master's students in human-centric AI. She previously secured an early career research grant from the Max Planck Institute for Intelligent Systems to establish her independent research program. Her group actively pursues funding for projects in human motion analysis, 3D reconstruction, and generative modeling, with strong industry and clinical collaborations. The Computer Vision and Learning Group (VLG) operates within ETH's Institute of Visual Computing, maintaining dedicated facilities for motion capture, 3D scanning, and high-performance computing. The team collaborates internationally with institutions like the Max Planck Society and focuses on scalable solutions for real-world human digitalization challenges, including surgical training systems and immersive virtual environments.

• Computer Vision
• Machine Learning
• Digital Humans
• +5 more

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
• +7 more

Sabine Süsstrunk is a Full Professor and Director of the Images and Visual Representation Laboratory (IVRL) at EPFL's School of Computer and Communication Sciences. She holds a BS in Scientific Photography from ETH Zürich, MS from Rochester Institute of Technology, and PhD from University of East Anglia. Her career includes positions at Hewlett-Packard Labs and Corbis Corporation. Her research explores computational imaging, computational photography, color processing, computer vision, and image quality. Key interests include near-infrared applications, multispectral imaging, and computational aesthetics. Her work bridges hardware and software solutions for imaging challenges. Publications demonstrate consistent focus on advancing generative models (diffusion models, neural cellular automata), 3D reconstruction (NeRF variants), and media integrity (DeepFake detection). Recent trends show increased emphasis on 3D vision, robustness in generative AI, and video analysis. Awards & Honors: IS&T/SPIE Electronic Imaging Scientist of the Year (2013) Raymond C. Bowman Teaching Award (2018) EPFL AGEPoly IC Polysphere Award (2020) 8 Best Paper/Demo Awards Fellowships: IEEE, IS&T, ELLIS, AIIA She leads the IVRL lab and advises PhD candidates while serving as President of the Swiss Science Council. Research is supported through competitive grants and industry collaborations.

• Computational Imaging
• Computational Photography
• Color Image Processing
• +4 more

Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.

• Signal Processing
• Audio Processing
• Image Processing
• +5 more

Ben Jann is a Professor and current Department Head at the University of Bern in the Department of Social Sciences . His methodological expertise spans statistical software development, decomposition analysis, and data visualization. Role: Department Head Institution: University of Bern Department: Department of Social Sciences Research Interests : Jann specializes in creating Stata modules for advanced statistical analysis, including geospatial mapping (geoplot), robust regression (robreg10), and inequality decomposition (oaxaca-blinder methods). His substantive work examines social inequality through economic and sociological lenses, analyzing how living costs and taxation systems impact income distribution in Switzerland. He also investigates educational transitions and gender disparities in STEM occupational choices through longitudinal studies like TREE2. Recent Publications Trends : Jann’s 15 most recent articles (2025-2023) demonstrate his focus on statistical software development for social science applications. Key contributions include methods for marginal odds ratios (2023), two-level model variance decomposition (2025), and list experiment analysis (2025). His empirical work connects statistical methodology to pressing social issues like gender wage gaps (2021) and teacher content knowledge in developing countries (2021).

• Statistical Methods
• Data Visualization
• Social Inequality
• +2 more

Sagie Benaim is an Assistant Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem. Previously, he was a postdoctoral researcher at DIKU (Department of Computer Science, University of Copenhagen) working with Professor Serge Belongie and was a member of the Pioneer Center for AI. He completed his PhD at Tel Aviv University in the Deep Learning Lab under the supervision of Professor Lior Wolf. Dr. Benaim's research spans computer vision, machine learning, and computer graphics, with a particular emphasis on generative models, neural signal representations, and inverse graphics. His work explores how disentangled representations can be leveraged to better understand and manipulate visual content. He has made significant contributions to 3D scene manipulation, image-to-video generation, and neural rendering techniques. His recent publications reveal a strong trajectory toward advancing generative AI capabilities, particularly in 3D understanding, multimodal generation, and video synthesis. His research consistently bridges theoretical foundations with practical applications, demonstrating innovation in neural representation manipulation for both creative and analytical purposes. Dr. Benaim is actively seeking excellent students and postdocs to join his research group, indicating his commitment to mentoring the next generation of researchers in computer vision and machine learning. His position at the Hebrew University of Jerusalem places him within a vibrant academic community focused on advancing the frontiers of computer science.

• computer vision
• machine learning
• computer graphics
• +4 more

Bernhard Egger is a junior professor (adidas Stiftungsprofessur) at the Chair of Visual Computing, Cognitive Computer Vision Lab at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) . His research bridges human/machine perception of faces and shapes with synthetic data generation. Formerly, he was a postdoc at MIT's Computational Cognitive Science Lab and Computer Science & AI Lab, following a PhD in facial image annotation at the University of Basel. Research Focus: 3D Morphable Models, Statistical Shape Modeling, Inverse Rendering, AI in Mental Health, Medical Imaging. Education: PhD (University of Basel, 2017), MSc/BSc in Computer Science (University of Basel), Teaching Diploma (University of Applied Sciences Northwestern Switzerland). Notable Awards: Best Poster Award (2024, Cognitive Computational Neuroscience) Best Paper Award (CVPR Workshop 2019) Honk Award (SIGBOVIK 2020) Publications Trends: Recent work spans implicit surface modeling (ICLR 2025), 3D scene decomposition (3DV 2025), medical shape models (BVM 2025), and multimodal AI (npj Mental Health 2024). Labs: FAU's Cognitive Computer Vision Lab (advisor to 8+ students/researchers).

• 3D Morphable Models
• Statistical Shape Modeling
• Computer Graphics
• +7 more

Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.

• Computer Vision
• Computational Photography
• Machine Learning
• +4 more

Dr. Sergey Prokudin is a Lecturer at the Department of Computer Science, ETH Zürich, affiliated with the Professur für Computer Vision. His research focuses on computer vision, machine learning, medical imaging, and robotics. Key areas include 3D reconstruction, neural fields, and uncertainty quantification in biomedical applications. His work bridges theoretical advancements in neural networks with practical applications in healthcare and robotics. Research interests span dynamic scene understanding, spatiotemporal modeling, and medical imaging processing. Notable projects include digital twin creation for spinal surgery, robust MRI parameter estimation, and avatar generation from single images. He has published extensively on topics like Gaussian splatting, residual neural fields, and point cloud alignment. No scientific awards have been explicitly listed. His advising and grants focus on interdisciplinary projects combining computer vision with medical engineering. He is part of ETH's Computer Vision group and contributes to labs exploring 3D modeling and medical visualization technologies.

• Computer Vision
• Machine Learning
• Medical Imaging
• +3 more

Ronan Boulic is a Senior Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) within the School of Computer and Communication Sciences. He leads the Immersive Interaction (IIG) research group and serves as a Senior Scientist in multiple departments including IC-SIN, IC-SSC, and GR-SCI-IC. His work focuses on motion synthesis for virtual humans and robots, human movement modeling, inverse kinematics, and immersive 3D interactions in virtual environments, with applications in virtual prototyping, gaming, ergonomics, and biomedical simulations. PhD in Computer Science (1986) from University of Rennes, France Habilitation (1995) from University of Grenoble, France Research interests include: Embodiment in Virtual Reality Human Movement Modeling and Optimization Motion Capture and Real-Time 3D Interaction Avatar Emotion Expression and Synchronization Cybersickness Detection and Mitigation Collision Detection and Inverse Kinematics His recent publications (2023-2025) emphasize cybersickness frameworks, avatar embodiment, and real-time motion control. He has supervised over 15 PhD candidates, including Nana Tian (2023) and Thibault Porssut (2021), and collaborates with the EPFL Cognitive Neuroscience Laboratory (LNCO). Teaching includes foundational Computer Science (ICC), Programming, and Software Engineering to first-year students. He is also a member of the PhD program committee for Robotics, Control, and Intelligent Systems at EPFL.

• Virtual Reality
• Human-Computer Interaction
• Computer Graphics
• +10 more

Raoul de Charette is a Research Director in computer vision at Inria Paris, leading the Astra-Vision group within the ASTRA team. His academic journey includes a PhD from Mines Paris (2012) and Habilitation (HDR) in 2022, with research stints at Carnegie Mellon University (2011), Mines Paris (2013), and the University of Makedonia (2014). His educational background comprises: PhD from Mines Paris (2012) Habilitation (HDR) (2022) De Charette's research centers on robust and interpretable visual scene understanding , spanning 3D scene reconstruction, domain adaptation, material recognition, and physics-grounded vision foundation models. His work integrates physical principles and synthetic data to enhance model robustness in real-world scenarios like autonomous driving and urban environments. Key contributions include uncertainty-aware 3D scene completion (PaSCo), material extraction from single images (Material Palette), and prompt-driven domain adaptation (PODA). Recent publications reveal a strategic shift toward vision-language integration, material-centric scene understanding, and foundation models that minimize labeled data dependency. His group pioneers physics-informed approaches to improve interpretability and resilience against environmental challenges like adverse weather conditions. Key scientific recognition includes: Best Paper Honorable Mention at EGSR 2025 for MatSwap ELLIS Membership PR[AI]RIE-PSAI Fellowship De Charette actively mentors four PhD students—Fatima Balde, Mohammad Fahes, Ivan Lopes, and Tetiana Martyniuk—often in industry collaborations with Valeo.ai and Kyutai. He secures funding through fellowships and industry partnerships, regularly opening PhD positions (including a 2025 opening for Physics-Grounded Vision Foundation Models). As an area chair for CVPR, ECCV, WACV, and IROS, he shapes the field through conference leadership and co-organizing initiatives like the African Computer Vision Summer School. He directs the Astra-Vision group within Inria Paris' ASTRA team, driving interdisciplinary research at the intersection of computer vision, machine learning, and physics-based modeling for real-world deployment in robotics and intelligent transportation systems.

• Visual Scene Understanding
• Computer Vision
• 3D Reconstruction
• +3 more

Robert W. Sumner is an Adjunct Professor at ETH Zurich and Associate Director at Disney Research Zurich. He specializes in computer graphics, focusing on mesh deformation, inverse kinematics, and game programming. His work bridges academic research and industry applications, particularly in 3D modeling and animation. Affiliations: Disney Research Zurich, ETH Zurich Education: PhD in Computer Science from MIT (2005), MSc from MIT (2001) Research Interests: Developing algorithms for shape manipulation, deformation transfer, and game development. Notable contributions include the ETH Game Programming Laboratory course and foundational work on mesh-based inverse kinematics. Key Projects: Curvature-domain shape processing, embedded deformation techniques, and deformation transfer between 3D models.

• Computer Graphics
• Mesh Processing
• Inverse Kinematics
• +3 more

Felix Wechsler is a Doctoral Assistant and PhD Student in Photonics at the École Polytechnique Fédérale de Lausanne (EPFL), working in the Laboratory of Applied Photonics Devices (LAPD) under Prof. Christophe Moser. He is also a PhD Student Representative in the EDPO-GE group within the Doctoral Program in Photonics. His primary affiliation is with the School of Engineering (STI) and the Department of Microengineering (IEM) at EPFL. Education: Felix holds a Bachelor of Science in Informatics and Physics from the Technische Universität München (TUM), and a Master of Science in Photonics from Friedrich Schiller University Jena (FSU Jena). Research Interests: His research focuses on computational tools for tomographic 3D printing, optical wave propagation, and microscopy. He develops innovative methods using Julia programming, including packages like SwissVAMyKnife.jl and Dr.TVAM. His work aims to improve volumetric additive manufacturing through advanced optical techniques and algorithmic optimization. Publications: Recent work includes scalable optical propagation methods, MEMS-based light modulators for holographic manufacturing, and inverse rendering frameworks for tomographic additive processes. These contributions highlight advancements in both theoretical and applied aspects of optical engineering and manufacturing. Awards: No scientific awards have been explicitly mentioned in the provided information. Advising & Collaborations: Felix has not yet advised students, but his research is supported through collaborations and institutional funding at EPFL. His involvement in developing open-source tools reflects his commitment to reproducibility and community-driven science. Labs & Teams: He is part of the LAPD laboratory and collaborates with the Realistic Graphics Lab (RGL) at EPFL, as evidenced by his reposts on Bluesky.

• Computational Optics
• 3D Printing
• Optical Propagation
• +3 more