جست‌وجوی استاد و پژوهشگر

Olga Sorkine-Hornungمشاهده پروفایل

استاد

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

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.

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.

• Design Science Research
• Enterprise Architecture Management
• Governance of Digital Platforms
• +۵ مورد دیگر

Robert Winter is a Full Professor of Business IT at the University of St. Gallen (HSG), Switzerland, and Director of the Institute of Information Management (IWI-HSG). He serves as Founding Director of HSG’s Executive MBA in Business Engineering and has led the School of Management’s Doctoral Program. His roles include Vice Editor-in-Chief of Business & Information Systems Engineering and current editorial board membership at MIS Quarterly Executive. Education M.Sc. Business Administration (1984), Goethe University, Frankfurt M.Sc. Business Education (1986), Goethe University, Frankfurt Ph.D. in Social Sciences (1989), Goethe University, Frankfurt Venia legendi (1995), Goethe University, Frankfurt Research Interests Focuses on Design Science Research, Enterprise Architecture Management, and governance of digital platforms. His work explores methodologies for collaborative innovation, data-driven transformation, and complexity management in large-scale systems. Key areas include enterprise transformation steering, data mesh adoption, and agile methodologies in public-sector contexts. Key Contributions Developed frameworks for enterprise architecture governance and digital platform ecosystems Authored influential papers on design science methodology and simulation-based research Recipient of AIS Senior Scholars’ Global Best Paper Award (2017) and Herbert A. Simon Award (2022) Professional Roles President of the School of Management Doctoral Program Member of editorial boards for leading journals (e.g., European Journal of Information Systems) Leadership in professional associations: VHB Board (2011–2014), AIS Swiss Chapter President (2012–2016)

• Explainable AI
• Computational Argumentation
• Quantitative Bipolar Argumentation Frameworks
• +۲ مورد دیگر

Xiang Yin is a Research Associate at the Department of Computing in Imperial College London , affiliated with the Computational Logic and Argumentation group (CLArg) . His work bridges Explainable AI (XAI) and Computational Argumentation (CA) , focusing on the explainability of Quantitative Bipolar Argumentation Frameworks (QBAFs) through attribution and counterfactual explanations. Research Interests: Explainable AI (XAI) Computational Argumentation Quantitative Bipolar Argumentation Frameworks Model Interpretability Human-AI Interaction Logical Reasoning for AI Publication Trends reveal a focus on argumentation-based explainability, with 2025-2024 works addressing large language models for claim verification, truth-discovery frameworks, and counterfactual explanations. Earlier works (2023-2022) explore random forest explanations, faithfulness criteria, and QBAF analysis. His 2018 publications on aircraft prediction systems demonstrate applied machine learning expertise. Education PhD in Artificial Intelligence under Prof. Francesca Toni and Dr. Nico Potyka Pre-PhD: Machine Learning R&D Engineer at Baidu Labs & Teams Xiang is part of the CLArg group at Imperial College London, focusing on integrating computational argumentation with AI explainability and contestability.

Juerg Leutholdمشاهده پروفایل

استاد

• Photonics
• Plasmonics
• Terahertz Technology
• +۴ مورد دیگر

Juerg Leuthold is a Full Professor and Head of the Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zürich, Switzerland. He also leads the Institute of Electromagnetic Fields (IEF) at the same institution. His academic career spans leadership roles at ETH Zürich, Karlsruhe Institute of Technology (KIT), and Bell Labs, with expertise in photonics, plasmonics, and THz technology for communication and sensing applications. Research Interests Photonics for high-speed communication Plasmonics and nonlinear optical signal processing Terahertz technology Applications in sensing and network systems Scientific Awards Optica Fellow IEEE Fellow ERC Advanced Grant (2015) Landesforschungspreis (Baden-Württemberg, 2009) Leadership & Committees Head of D-ITET (2023–present) General Chair of ECOC 2022 Board of Directors, OSA/Optica

Julian Adamekمشاهده پروفایل

پژوهشگر

• Computational Cosmology
• Theoretical Cosmology
• Large-scale structure of the Universe
• +۳ مورد دیگر

Julian Adamek is a computational cosmologist and lead developer of gevolution , a general-relativistic N-body code for cosmological simulations. His work focuses on modeling relativistic effects in cosmic structure formation to better understand gravity’s role on large scales and dark energy. Research Interests: Computational Cosmology, Theoretical Cosmology, Large-scale structure of the Universe, Relativistic N-body simulations. Technical Leadership: Lead developer of gevolution , a public cosmological simulation code available via GitHub. Recent publications span diverse applications of deep learning in geospatial analytics, environmental monitoring, and computer vision, including phenology modeling, biomass mapping, conflict assessment, and 3D reconstruction from point clouds. Key Trends: Integration of AI/ML for environmental tasks, cross-domain applications (cosmology, ecology, forestry), and satellite data processing. Technical Focus: Transformer networks, diffusion models, super-resolution imaging, and ensemble learning for uncertainty quantification. Julian collaborates with researchers in cosmology and geospatial science, though specific students or awards are not mentioned in the provided texts.

Bernhard Thomaszewskiمشاهده پروفایل

مدرس

• Computer Graphics
• Robotics
• Computational Mechanics
• +۳ 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.

Pascal Fuaمشاهده پروفایل

استاد

• Computer Vision
• 3D Reconstruction
• Shape Modeling
• +۶ 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.

Michael Bronsteinمشاهده پروفایل

استاد

• Geometric Deep Learning
• Graph Representation Learning
• Computer Vision
• +۴ مورد دیگر

Affiliations and Roles Michael Bronstein is a Professor at the Università della Svizzera italiana (USI) in the Faculty of Informatics and the Institute of Computational Science . He holds the Chair in Machine Learning and Pattern Recognition at Imperial College London and serves as Head of Graph Learning Research at Twitter . Previously, he was affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) as an Adjunct Professor. Education Ph.D. in Computer Science, Technion–Israel Institute of Technology (2007) Visiting appointments at Stanford University, MIT, Harvard University, and Tel Aviv University Research Interests Bronstein's work focuses on geometric deep learning , graph representation learning , and non-rigid shape analysis . He pioneered methods for extending machine learning to non-Euclidean domains like graphs and manifolds. His research combines theoretical advancements in spectral geometry with practical applications in computer vision, robotics, and medical imaging. Publications Trends His articles emphasize geometric deep learning frameworks, functional maps for shape correspondence, and spectral methods for manifold analysis. Key themes include invariant representations, partial shape matching, and applications in 3D reconstruction and graph neural networks. Awards and Honors Five ERC Grants Royal Society Wolfson Research Merit Award IEEE and IAPR Fellowships World Economic Forum Young Scientist Advising and Entrepreneurship Bronstein is a serial entrepreneur, founding companies like Novafora , Invision (acquired by Intel), and Fabula AI (acquired by Twitter). His academic advising spans PhD and Master’s students in machine learning and geometry processing. Labs and Teams Active in the Institute of Computational Science at USI and leads Twitter’s Graph Learning Research team, focusing on real-world applications of geometric deep learning.

• IoT (Internet of Things)
• Platform Business Models
• Data Analytics
• +۵ مورد دیگر

Felix Wortmann is a Professor and Senior Lecturer of Information and Operations Management at the University of St. Gallen (HSG), affiliated with the ITEM-HSG Institute. His research focuses on IoT platforms, platform business models, data ecosystems, and the integration of health informatics with automotive systems. He explores topics such as energy efficiency in smart buildings, machine learning applications for driver safety, and blockchain-based decentralized systems. Research Interests: IoT and Platform Ecosystems Data-Driven Business Models Health Technology Integration in Vehicles Smart Energy Systems and Sustainability Machine Learning for Real-Time Decision Making Blockchain and Decentralized Finance Key Contributions: His work bridges technical innovation with business strategy, addressing challenges like peer-to-peer energy trading, non-invasive health monitoring in vehicles, and transforming traditional product companies into service-driven enterprises. Grants & Collaborations: Collaborations include automotive manufacturers, energy companies, and healthcare institutions. Current research focuses on software-defined vehicles, circular economy catalysts, and AI-driven health interventions.

Ilya Karlinمشاهده پروفایل

مدرس

• Fluid Dynamics
• Computational Fluid Dynamics
• Thermodynamics
• +۳ مورد دیگر

Prof. Ilya Karlin is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. His research focuses on advanced computational fluid dynamics, particularly leveraging lattice Boltzmann methods for simulating complex fluid phenomena. Key areas include non-ideal fluid behavior, multiphase flows, combustion processes, and hydrodynamic closures. He has contributed extensively to improving numerical methods for compressible flows, turbulent systems, and reactive mixtures in porous media. His work bridges kinetic theory with continuum mechanics, addressing challenges in hydrodynamic manifolds and non-local effects. Dr. Karlin's publications emphasize rigorous mathematical analysis alongside computational innovation, such as spectral closure techniques and entropy-based models. His research often explores the interplay between microscopic kinetic descriptions and macroscopic hydrodynamic equations. He has developed novel algorithms like the 'particles on demand' method for handling strong discontinuities in flows. Recent work includes studies on rarefaction effects, capillarity-viscosity balance, and exact hydrodynamic manifolds for BGK equations. His studies span diverse applications from microfluidics and phase transitions to detonation modeling and environmental fluid mechanics. While no specific awards are listed in the provided text, his prolific publication record indicates significant contributions to the field of computational fluid dynamics.

Renato Pajarolaمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

• Computational Fluid Dynamics (CFD)
• Hydraulic Turbines
• Cavitation Modeling
• +۴ مورد دیگر

Jean Decaix is a Researcher at the University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis - Haute Ecole d'Ingénierie ) within the Department of Energy and Environmental Techniques . His work focuses on hydropower systems , computational fluid dynamics (CFD) , and cavitation modeling for hydraulic turbines. Decaix contributes to projects like SCCER-SoE (Supply of Electricity center) and XFlex Hydro , aiming to enhance grid stability through advanced turbine operation. Decaix's research spans Francis and Pelton turbines , with a focus on flow topology , unsteady cavitating flows , and hydraulic short-circuit modes . He develops freely distributable CFD tools for building airflow and turbine efficiency, validated through experimental measurements and numerical simulations . Notable projects : SCCER-SoE (2017-2020): Innovation roadmaps for geothermal and hydropower Solution de transfert d'énergie par pompage-turbinage à petite échelle (2015-2017): Economic model development for small-scale hydropower Decaix's 15 most recent publications (2015-2024) cover topics like cavitation suppression , vortex rope dynamics , Pelton turbine efficiency , and CFD validation for building energy systems. His work emphasizes renewable energy integration and mechanical stress reduction in hydropower plants.

• Civil Engineering
• Computational Fluid Dynamics
• Porous Media Flow
• +۳ مورد دیگر

Dr. Luka Malenica is a dedicated researcher at ETH Zurich's Professorship for Durability of Engineering Materials, focusing on computational modeling of material degradation processes in civil infrastructure. His work bridges theoretical numerical methods with practical engineering applications through direct involvement in the department's research operations. His research program centers on multiphase flow in porous media , corrosion mechanisms in reinforced concrete , and advanced numerical techniques . He employs pore-scale direct numerical simulations to investigate steel-concrete interface phenomena, developing predictive models for infrastructure durability. His methodological innovations include control volume isogeometric analysis, adaptive multiresolution modeling, and deep learning applications for adaptive meshing—addressing challenges in heterogeneous media flow and transport phenomena. Analysis of his 15 most recent publications reveals a consistent research trajectory since 2015, with accelerating output in corrosion science (5 papers in 2024-2025) and computational methods (7 papers 2019-2022). Key interdisciplinary connections span civil engineering (corrosion in reinforced concrete), chemical engineering (bubble column reactors), and hydrology (karst aquifer modeling), unified by his expertise in numerical simulation of transport phenomena. Recent work demonstrates increasing sophistication in modeling macrovoid formation at steel-concrete interfaces and capillary-driven multiphase systems.

Christian Vogtمشاهده پروفایل

مدرس

• IoT and Embedded Systems
• Wireless Sensor Networks
• Medical Electronics
• +۳ مورد دیگر

Dr. Christian Vogt Dr. Christian Vogt is a Lecturer at the Department of Information Technology and Electrical Engineering within the Center for Project-Based Learning at ETH Zürich. His research focuses on IoT systems, embedded electronics, and wearable technology with applications in agriculture, healthcare, and urban computing. He specializes in energy-efficient sensor networks, flexible electronics, and real-time data transmission systems. Research Interests Vogt's work bridges hardware innovation and software optimization, emphasizing practical implementations through project-based learning. Key areas include: IoT and Wireless Sensor Networks Low-power embedded machine learning Wearable biomedical devices Flexible/stretchable electronics Ultra-wideband and radar-based sensing Publications Recent work highlights energy-efficient livestock monitoring systems using LoRa, battery-free sensor nodes powered by human body energy, and real-time gesture recognition with novel sensors. His research often involves interdisciplinary collaborations between computer science, electrical engineering, and biomedical applications. Affiliations ETH Zürich - Center for Project-Based Learning Department of Information Technology and Electrical Engineering Contact: christian.vogt@pbl.ee.ethz.ch