جست‌وجوی استاد و پژوهشگر

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.

• Synthesis and Catalysis
• Process Chemistry and Scale-up
• Flow Chemistry (Microreactors)
• +۵ مورد دیگر

Christophe Allemann is a Full Professor at the Fribourg School of Engineering and Architecture (HEIA-FR), part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). He serves as Head of Institute and specializes in synthesis and catalysis, process chemistry, flow chemistry, and chemical production. His research focuses on topics such as sol-gel processes, sensor development, and catalyst optimization. Education: PhD in Chemistry, University of Fribourg, Switzerland (2002) BSc and BA programs in Chemistry and Architecture at HEIA-FR Research Interests: His work spans synthesis methodologies, flow chemistry applications, process scale-up, and risk analysis. He emphasizes interdisciplinary education and the development of robust industrial processes. Publications: His recent work includes advancements in phosgene sensors, lanthanide complexation studies, and mixing efficiency in micromixers. Key themes include catalytic mechanisms, solvent effects, and microreactor technologies. Labs/Teams: As Head of Institute, he leads research teams focusing on chemical production, catalysis, and continuous processing innovations.

Federica Lanzaمشاهده پروفایل

مدرس

• Seismology
• Geophysics
• Volcanology
• +۳ مورد دیگر

Federica Lanza is a Lecturer at the Department of Earth and Planetary Sciences at ETH Zurich, affiliated with the Schweiz. Erdbebendienst (SED), the Swiss Seismological Service. Her work focuses on seismology, geophysics, and geothermal systems, with expertise in induced seismicity, fault dynamics, and advanced monitoring technologies like Distributed Acoustic Sensing (DAS). She teaches courses such as Seismic Waves II in the Autumn Semester 2025. Her research integrates field experiments, computational modeling, and machine learning to address challenges in seismic hazard assessment, geothermal energy development, and tectonic processes. Key areas include forecasting induced earthquakes at geothermal sites, analyzing fault interactions in fold-and-thrust belts, and developing innovative sensor systems for subsurface monitoring. Dr. Lanza collaborates on large-scale projects like the Utah FORGE initiative, advancing techniques for real-time seismic monitoring and fracture network characterization. Her contributions bridge fundamental geophysical research with practical applications in energy systems and risk mitigation.

Josep Mas-Rosellóمشاهده پروفایل

استاد

• Organic Chemistry
• Asymmetric Catalysis
• Green Chemistry
• +۳ مورد دیگر

Dr. Josep Mas-Roselló is a **Professor of Organic Chemistry** at ETH Zurich, Switzerland, leading the Wennemers Group. His research focuses on designing eco-friendly organic catalysts to replace toxic metal-based catalysts in chemical transformations. He investigates catalytic activation of inert bonds (e.g., hydrogenations, C─H functionalization) using experimental and computational methods. Education: B.Sc. in Chemistry (2013), University of València, Spain Ph.D. in Chemistry (2017), University of Bristol, UK (advisor: Prof. Jonathan Clayden) Research Interests: His group develops sustainable organic catalysts for asymmetric synthesis, aiming to reduce environmental impact. Key themes include oxime hydrogenation, iridium-catalyzed reactions, and ligand design. Recent work addresses challenges in CO₂ fixation and metal-free catalytic systems. Scientific Awards: SNF Ambizione Fellowship (2022) Lab & Collaborations: As group leader, he mentors Master’s, PhD students, and postdocs. The lab collaborates on projects involving computational modeling and green chemistry innovations. Research outcomes include patents (e.g., WO2020094528A1 for enantioselective processes).

• Geophysics
• Planetary Science
• Fluid Dynamics
• +۵ مورد دیگر

Jérôme André Roland Noir is a Privatdozent (Professor) in the Department of Earth and Planetary Sciences at ETH Zürich, affiliated with the Institut für Geophysik. His research focuses on geophysical fluid dynamics, particularly in rotating and stratified flows influenced by topography, planetary core dynamics, and subsurface ocean processes. He contributes to experimental and theoretical studies of planetary fluid layers, combining laboratory experiments with numerical simulations. Key research areas include precession-driven flows, core-mantle coupling, and rotational instabilities in non-axisymmetric geometries. Recent work explores the interplay between topography and fluid dynamics in planetary cores, as well as the impact of libration on resonant fluid modes. His articles highlight experimental and computational advancements in understanding geophysical flows, including studies on Length of Day variations and fractal energy structures in librating spheres. He co-edited a special issue on precession and libration-driven flows. Current projects are funded by grants such as 'Roughness and large scale topography enhanced coupling in planetary cores and subsurface oceans' and 'Unravelling Earth’s magnetic history and processes.' Dr. Noir’s work bridges fluid mechanics with geophysics, addressing fundamental questions in planetary dynamics and Earth’s rotational behavior. His lab facilities at ETH Zürich support experimental investigations of complex fluid systems.

Kai Hormannمشاهده پروفایل

استاد

• Computational Sciences
• Geometric and Visual Computing
• Informatics
• +۳ مورد دیگر

Kai Hormann is a full professor in the Faculty of Informatics at Università della Svizzera italiana (USI Lugano). He earned his Ph.D. in computer science from the University of Erlangen-Nürnberg in 2002 and has held positions at Clausthal University of Technology, Caltech, and the CNR in Pisa. His research focuses on mathematical foundations of geometry processing, including barycentric coordinates, subdivision algorithms, and rational interpolation. Hormann has authored over 100 publications and serves as an associate editor for journals like Computer Aided Geometric Design and Dolomites Research Notes on Approximation . In 2024, he received the prestigious John A. Gregory Award for his contributions to geometric modeling. Education : Ph.D. in Computer Science, University of Erlangen-Nürnberg (2002) Diploma in Mathematics, University of Erlangen (1997) Abitur, Leibniz-Gymnasium Bad Schwartau (1992) Research Interests : Hormann’s work bridges computer science, applied mathematics, and engineering. His current projects include interactive shape deformations, time-dependent object processing, 3D visualization, and GPU-accelerated algorithms. His research areas encompass geometry processing, computational sciences, and numerical analysis. Publications : His recent articles focus on barycentric coordinates, subdivision schemes, and surface reconstruction. Themes include transfinite coordinates, curvature continuity, and efficient interpolation methods. Awards : John A. Gregory Award (2024) Chair of SIAM Activity Group on Geometric Design (2017–2018) Advising & Grants : He has advised numerous PhD students, including work on barycentric rational curves and subdivision schemes. He has organized conferences such as the SIAM GD and GMP series, reflecting his leadership in computational geometry. Labs & Teams : Hormann is affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), collaborating on interdisciplinary projects in AI and geometry processing.

Ola Svenssonمشاهده پروفایل

دانشیار

• Approximation Algorithms
• Combinatorial Optimization
• Computational Complexity
• +۳ مورد دیگر

Ola Svensson is an Associate Professor at the School of Computer and Communication Sciences, EPFL. His research focuses on approximation algorithms, combinatorial optimization, computational complexity, and scheduling. He has been supported by grants including the ERC Starting Grant "OptApprox" (2014-2019), SNF grants, and the ERC Consolidator Grant "POTCO" (2023-). He teaches courses such as Advanced Algorithms and Approximation Algorithms and Hardness of Approximation. Education: PhD from IDSIA - Universita della Svizzera italiana (2009) and Master's from Uppsala University (2005). Research Interests: Design and analysis of approximation algorithms for NP-hard problems, scheduling, and computational complexity. He explores limitations of approximation techniques through hardness results and contributes to theoretical computer science. Publications span clustering, scheduling, and graph problems like the Traveling Salesman Problem. Recent work includes learning-augmented algorithms and robust optimization. Awards: I&C teaching award and best paper awards at FOCS (2017) and STOC (2018). Over a dozen PhD students advised, many entering postdocs or industry roles. Labs/Teams: Part of the theory group at EPFL, collaborating on academic projects and course development.