جست‌وجوی استاد و پژوهشگر

Regine Eibl-Schindlerمشاهده پروفایل

استاد

• Bioreactor Engineering
• Tissue Engineering
• Biopharmaceutical Production
• +۴ مورد دیگر

Regine Eibl-Schindler is a Professor at the Zurich University of Applied Sciences (ZHAW) , School of Life Sciences and Facility Management. She leads the Center for Cell Cultivation Techniques, Tissue Engineering and Medical Biology, focusing on advanced bioreactor design and biopharmaceutical production processes. Project leader in bioreactor optimization for therapeutic cell production Deputy leader in vaccine and antibody manufacturing innovations Co-developer of single-use systems for stem cell and microbial cultures Her research spans bioreactor engineering , computational fluid dynamics (CFD) modeling , and process intensification for scalable cell culture systems. She has pioneered microcarrier-based cultivation of induced pluripotent stem cells and mesenchymal stem cells , emphasizing xeno-free media and disposable technologies. Recent work includes automated bioreactor qualification and shear stress optimization for cell growth. Her expertise in single-use bioreactors has been applied to diverse fields including cell therapy manufacturing , vaccine development , and cellular agriculture for clean meat production. She developed standardized qualification protocols for novel bioreactor designs and advanced process analytical technology (PAT) for real-time cell health monitoring. Key collaborations include optimizing CHO cell antibody production , HEK293 cell expansion , and plant cell bioreactors for pharmaceutical and food applications. Her work bridges bioprocess engineering with regenerative medicine , ensuring compliance with regulatory standards through rigorous CFD simulations and scale-up methodologies .

David Bommesمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Hexahedral Meshing
• +۴ مورد دیگر

Prof. David Bommes is a Professor and Head of the Computer Graphics Group (CGG) at the University of Bern's Institute of Computer Science. He specializes in geometry processing, with a focus on hexahedral and quad mesh generation, volumetric mapping, and parametrization quantization. His work bridges theoretical computer science and practical applications in computational geometry, geology, and engineering. Research Interests: His key areas include hex meshing, frame field synthesis, topology optimization, and robust mesh construction. He explores methods to generate high-quality meshes for simulations and modeling, often addressing challenges in non-manifold structures and boundary layer representations. Recent advancements include progressive embedding for tetrahedral maps and quantization techniques for hexahedral meshes. Publications: Over 20 publications since 2004 cover topics like parametrization quantization, subdivision surfaces for geological modeling, and automatic differentiation tools. His 2023 work on expansion cones and 2022 survey on hex-mesh processing highlight his leadership in this domain. Labs/Teams: Directs the CGG, a research group advancing computational geometry and graphics. Collaborates on projects involving mesh generation, geometric algorithms, and interdisciplinary applications in geology and engineering.

• Data-Driven Computing
• Fracture Mechanics
• Multiscale Modeling
• +۴ مورد دیگر

Marine Casalis is a Researcher at the Department of Political Analysis at ETH Zurich, Switzerland. Their work focuses on interdisciplinary research at the intersection of computational mechanics, materials science, and biomedical engineering. Key research interests include data-driven computing, fracture mechanics, and multiscale modeling of materials. Casalis has presented extensively at international conferences, such as ECCOMAS-CMCS, STAMS, and IUTAM Symposia, highlighting contributions to topics like ductile fracture, hydrogen storage in nanoparticles, and optimal transport methods in mechanics. Their research bridges theoretical and applied perspectives, with a strong emphasis on predictive modeling and algorithm development. Recent work includes advancements in model-free data-driven approaches and their application to complex material systems. Research activities span computational mechanics, with a focus on understanding and simulating deformation, fracture, and microstructural evolution in metals and other materials. Notable contributions include studies on strain-gradient microplasticity, optimal scaling laws for fracture, and the mechanics of fused silica glass. Casalis has also explored biomedical applications such as oncotripsy and ultrasound neuromodulation, demonstrating a commitment to interdisciplinary innovation. Their work often integrates advanced numerical methods, such as meshfree approximations and atomistic simulations, to address challenges in energy and materials science. Publications and presentations reflect a sustained engagement with cutting-edge topics in mechanics and materials science, including collaborations with institutions like Oxford University, Politecnico di Milano, and the Technical University of Munich. While no specific awards are mentioned, their extensive conference participation underscores a dynamic research profile. Advising and grant details are not provided in the available text, but their academic contributions are evident through their active role in international academic networks and computational initiatives.

Andreas Wieserمشاهده پروفایل

استاد

• Geospatial Engineering
• Geomatics
• Geodetic Monitoring
• +۳ مورد دیگر

Prof. Andreas Wieser is a Full Professor of Geosensorics and Engineering Geodesy at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, leading the Institute of Geodesy and Photogrammetry. His work focuses on advanced measurement systems for high-precision positioning, navigation, and geodetic monitoring of infrastructure and natural environments. Notable contributions include parameter estimation methods, quality control frameworks, and sensor optimization for applications like avalanche monitoring, railway deformation analysis, and CERN's Futur Circular Collider project. Education: PhD in Surveying from TU Graz (2001), Thesis: 'Robust and fuzzy techniques for parameter estimation and quality assessment in GPS' Habilitation at TU Graz (2007), Thesis: 'GPS based velocity estimation and its application to an odometer' Postdoc at University of Calgary (2003-2004) Research Interests: Development of multi-spectral LiDAR systems for material probing and distance measurement Integration of sensor technologies for environmental monitoring (e.g., snow depth analysis, hydroelectric dam deformation) High-precision geodetic infrastructure for large-scale projects like CERN's FCC Algorithmic advancements in point cloud registration, radiometric calibration, and uncertainty quantification Honors: 2006 Karl-Rinner Award (Austrian Geodetic Commission) 2003 Erwin-Schrödinger Fellowship (Austrian Science Fund) 2002 Josef Krainer Sponsorship (State of Styria) Grants & Labs: Alpine Metrology Lab: Long-range TLS and permanent GNSS for geomonitoring Leadership on CERN FCC Geodesy project Development of Comb-based multispectral LiDAR systems His work bridges geomatics with engineering challenges, emphasizing practical implementations of advanced sensor technologies across civil infrastructure, environmental science, and heritage preservation.

Wanyun Xieمشاهده پروفایل

پژوهشگر

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

Wanyun Xie is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the Institute of Electrical Engineering. They work within the Laboratory for Information and Inference Systems (LIONS), focusing on research areas such as Machine Learning , Deep Learning , Optimization , and Robust Learning . Their email address is wanyun.xie@epfl.ch . Recent research trends in their publications include advancements in gradient norm clipping , Sharpness-aware Minimization (SAM) , data-mixing frameworks , and game-theoretic approaches for Byzantine-robust federated learning . These works emphasize non-Euclidean smoothness , parallelized optimization , and stable nonconvex-nonconcave training .

Philippe Müllhauptمشاهده پروفایل

مدرس

• Nonlinear Control
• Control Theory
• General Mechanics
• +۴ مورد دیگر

Philippe Müllhaupt is a Lecturer at EPFL, affiliated with multiple departments including the School of Engineering (STI), Department of Mechanical Engineering (SGM), and the Physics section (SB-SPH). He holds roles in teaching and research across disciplines such as nonlinear control, biomechanics, and robotics. His research focuses on control systems, musculoskeletal modeling, and dynamic systems analysis. Education: Ingénieur en Electricité, EPFL (1993) DEA en Automatique et Traitement du Signal, Paris-Sud (1994) Doctorat en Automatique, EPFL (1999) Research Interests: Nonlinear control theory and applications Mechanical systems modeling (e.g., shoulder biomechanics) Robotics and dynamic systems control Mathematical methods in control systems Publications: Over 70 peer-reviewed articles in journals and conferences, focusing on control systems, biomechanical modeling, and robotic applications. Recent work includes musculoskeletal shoulder simulations and nonlinear control algorithms. Teaching: Linear system theory Nonlinear control Mechanical engineering fundamentals Collaborations: Active in interdisciplinary projects involving biomechanics, robotics, and engineering education. Supervised 6 PhD/MSc students.

Patrick Schniderمشاهده پروفایل

مدرس

• Discrete Geometry
• Computational Geometry
• Topological Data Analysis
• +۴ مورد دیگر

Patrick Schnider is a lecturer in the Department of Mathematics and Computer Science at the University of Basel and the Department of Computer Science at ETH Zürich. His academic journey includes postdoctoral positions at ETH Zürich under Prof. Bernd Gärtner and Prof. Emo Welzl, and at the University of Copenhagen with Prof. Karim Adiprasito. He holds a PhD in Theoretical Computer Science from ETH Zürich between 2015 and 2020. Education: Bachelor's and Master's degrees in Mathematics from ETH Zürich. PhD in Theoretical Computer Science under Prof. Emo Welzl. Research focuses on combinatorial and topological methods in discrete geometry and high-dimensional data analysis, including topological methods for mass partitions, combinatorial depth measures, geometric transversals, computational geometry, and topological properties of solution spaces. Recent work explores geometric algorithms, topological data analysis, and combinatorial optimization, with notable contributions to clustering algorithms, persistent homology applications, and fair division problems. No scientific awards explicitly mentioned. Collaborations include the Theory of Combinatorial Algorithms Group at ETH Zurich and research groups led by Prof. Gärtner and Adiprasito. Other interests include music, playing clarinet in a wind orchestra, and conducting in various orchestral projects.

• Machine Learning
• Deep Learning
• Time Series Analysis
• +۳ مورد دیگر

Fabian Gröger is a Research Associate, Doctoral Student, and Lecturer at Lucerne University of Applied Sciences and Arts (HSLU), affiliated with the Lucerne School of Computer Science and Information Technology. He is a Scientific Collaborator at the Algorithmic Business (ABIZ) Research Lab and teaches in the CAS Machine Learning program. His roles include Data Scientist, Research Assistant, and Teaching Assistant in machine learning and artificial intelligence programs. Education: Ongoing PhD in Biomedical Engineering, University of Basel Master of Science in Data Science/ML, HSLU Bachelor of Science in Computer Science, HSLU His research focuses on Machine Learning , Deep Learning , Audio Data , and Self-Supervised Learning , with applications in digital dermatology, bioaerosol monitoring, and medical anomaly detection. His work emphasizes scalable foundation models, data quality audits, and reducing annotation needs in healthcare AI. Recent publications highlight contributions to hyperbolic space applications in medical AI, audio representation learning for hearables, and benchmarking strategies like CleanPatrick. His work spans conferences like MICCAI, NeurIPS, and ECCV. Awards: Outstanding Reviewer (JEADV, 2024) Best Lightning Talk (AI Medicine Symposium, 2024) Best Paper (MICCAI 2023) Best Master Thesis (HSLU, 2023) Gröger leads projects such as SelfClean for Audio and AI for All , and collaborates with industries like Prepress Media AG. He advises on algorithms for small-hotel revenue management and exhibits at events like the Museum Exposition on Food & AI. His lab affiliations include the ABIZ Research Lab and the Digital Dermatology group, where he explores ethical AI, clinical decision support systems, and data-centric healthcare solutions.

• Computer Graphics
• Geometry Processing
• 3D Reconstruction
• +۳ مورد دیگر

Dr. Jing Ren is affiliated with the Department of Computer Science at ETH Zürich, holding a role within the Professorship for Computer Science. Their research focuses on computational geometry, 3D reconstruction, and computer graphics, with notable contributions to shape analysis, non-rigid matching, and fabric modeling. Dr. Ren’s work bridges theoretical advancements with practical applications in textile design, architectural modeling, and medical imaging. They collaborate extensively on projects involving functional maps, optimization algorithms, and geometric morphometrics. Key research interests include: Non-rigid shape correspondence and matching Computational modeling of woven fabrics and textiles 3D face and building reconstruction techniques Efficient spectral and discrete optimization methods Recent publications (2022–2024) emphasize innovations in fabric parameterization, Gaussian noise distribution, and rethinking 3D face reconstruction benchmarks. Their work often employs machine learning and functional map frameworks to solve geometric problems across disciplines. Laboratory and team affiliations are not explicitly detailed in the provided materials, but their research aligns with ETH Zürich’s broader initiatives in computer science and engineering. No grants or advising activities are specified in the current data.

• Metric Entropy
• Functional Analysis
• Machine Learning
• +۳ مورد دیگر

Dr. Thomas Allard is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on metric entropy, functional analysis, and their applications to machine learning, signal processing, and operator theory. His recent work bridges mathematical foundations with practical domains such as information theory and computational methods. Key research topics include entropy of compact operators, metric entropy in Banach spaces, and causal linear systems analysis. His contributions span theoretical advancements and algorithmic techniques, with applications ranging from Sobolev spaces to machine learning theory. No scientific awards or grants are explicitly mentioned in the provided data. He has not listed advisees, though his research areas suggest potential supervision in mathematical information science and related fields. His office is located at ETF E 118, ETH Zürich, with contact details available via email.

Ya-Ping Hsiehمشاهده پروفایل

مدرس

• Machine Learning
• Optimization
• Stochastic Processes
• +۳ مورد دیگر

Dr. Ya-Ping Hsieh is a Lecturer at the Department of Computer Science at ETH Zürich, affiliated with the Institute for Machine Learning. His research focuses on advanced optimization techniques, stochastic processes, and their applications in machine learning. Key areas include non-convex optimization, diffusion models, and optimal transport theory. His work often addresses convergence properties of algorithms and their theoretical guarantees in complex systems. Dr. Hsieh's recent publications explore topics such as entropy-maximizing exploration via diffusion models, Riemannian stochastic optimization frameworks, and the dynamics of min-max algorithms. His contributions bridge theoretical foundations and practical implementations, emphasizing rigorous mathematical analysis. No scientific awards or grants are explicitly listed in the provided materials. He is part of the Institute for Machine Learning at ETH Zürich, contributing to both academic and applied research in computational methods.

Yijiang Huangمشاهده پروفایل

پژوهشگر

• Robotics
• Structural Engineering
• Automation
• +۳ مورد دیگر

Yijiang Huang is a Researcher affiliated with the Department of Computer-Aided Robotics at ETH Zürich. His work focuses on advancing computational robotics, with a particular emphasis on structural design optimization, robotic assembly processes, and algorithmic fabrication methods. He holds a position within the Professorship for Computational Robotics and contributes to cutting-edge research in multi-robot systems, topology optimization, and sustainable construction techniques. His research integrates robotics with structural engineering to enhance automation in manufacturing and construction. Key areas include non-repetitive robotic assembly, cooperative multi-arm systems, and the development of frameworks for high-performance design optimization. Huang has published extensively on topics such as task and motion planning (TAMP), deflation methods for non-convex optimization, and bespoke interlocking connections for timber structures. His articles highlight innovations like the CantiBox project, which explores robotic assembly of interweaving timber elements, and protocols linking algorithmic design with construction intent. These contributions aim to bridge gaps between computational methods and real-world applications, emphasizing efficiency, sustainability, and precision in robotic fabrication systems.

• Aerothermodynamics
• Mechanical Engineering
• Process Engineering
• +۳ مورد دیگر

Dr. Ndaona Chokani is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. Their research focuses on aerothermodynamics, mechanical engineering, and process engineering, with a recent emphasis on interdisciplinary applications in computer vision and image processing. Affiliated with the Professur f. Aerothermodynamik, their work spans theoretical and applied domains. While explicit educational background details are not provided, their academic career includes contributions to fields such as fluid dynamics and process systems engineering. Research interests are inferred from departmental affiliation and recent publications, emphasizing computational methods in mechanical systems and image quality assessment. Recent publications highlight work in perceptual quality metrics for images and videos, immersive systems (VR/360 content), and machine learning applications in 3D pose estimation and generative models. Key areas include diffusion models for low-light imaging, spatial-temporal geometric networks, and benchmarking frameworks for image harmonization. No awards or grants are explicitly listed in the provided text. Their research often involves collaborations on datasets like Salient360! and tools for gaze data analysis in 3D environments. Future work appears to focus on improving perceptual metrics and expanding applications in healthcare and immersive technologies.

• Geometry processing
• Discrete differential geometry
• Physical simulation
• +۲ 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.

• Graph Algorithms
• Theoretical Computer Science
• Approximation Algorithms
• +۲ مورد دیگر

Tianyi Zhang is a Researcher at the Professorship for Theoretical Computer Science, ETH Zurich, located at OAT Z 29, Andreasstrasse 5. His research focuses on advancing fundamental algorithms in graph theory, with particular expertise in dynamic graph problems, efficient spanner constructions, edge coloring optimizations, and shortest-path computations. Dr. Zhang develops both theoretical frameworks and practical implementations for complex computational challenges. His core research areas include the design of near-linear and subquadratic time algorithms for graph optimization problems, fault-tolerant network structures, streaming-optimized graph coloring, and geometric graph embeddings. Recent work emphasizes breakthroughs in Vizing's theorem implementations, dynamic set cover deamortization, and space-efficient distance oracles. Dr. Zhang's publications demonstrate consistent innovation in algorithm efficiency for planar graphs, Euclidean spaces, and dynamic network settings. His 2023-2025 articles reveal concentrated efforts on: 1) Optimizing edge coloring through multi-step Vizing chains and streaming adaptations, 2) Enhancing spanner constructions for doubling metrics and planar environments, and 3) Developing failure-resistant path algorithms with improved time/space complexity. These contributions address scalability challenges in large-scale network processing. He collaborates within the Theoretical Computer Science research group at ETH Zurich, contributing to the institution's leadership in algorithmic innovation. No information about awarded grants, supervised students, or educational background is available in the source materials.