جستجوی استاد راهنمای دانشگاهی

Donat Fähمشاهده پروفایل

استاد

• Seismology
• Geophysics
• Earthquake Engineering
• +۷ مورد دیگر

Prof. Donat Fäh is a faculty member at ETH Zurich's Institute of Geophysics, part of the Swiss Seismological Service (SED). His work focuses on advancing seismic hazard assessment and site response modeling in Switzerland and beyond. He leads interdisciplinary projects integrating geophysical surveys, machine learning, and empirical data to refine risk models for urban areas like Basel and Lucerne. Key contributions include developing the ERM-CH23 national earthquake risk framework and improving methodologies for nonlinear soil behavior analysis using KiK-net data from Japan. His research emphasizes high-resolution amplification mapping, subsurface characterization via ambient vibrations, and understanding glacial and subaqueous slope dynamics. Research interests span seismic site effects, soil mechanics, landslide stability monitoring, and the application of advanced geophysical inversion techniques. He collaborates internationally to enhance earthquake risk communication and building code compliance, particularly in low seismicity regions. Current efforts include refining 3D geophysical models for urban settings and exploring Bayesian methods for subsurface structure identification. His work bridges fundamental geophysical research with practical engineering solutions for infrastructure resilience. Advising and grants: No formal advisees or grant details are explicitly listed in the provided texts. His collaborative projects, however, suggest involvement in large-scale initiatives such as URBASIS and the Swiss strong-motion network modernization. Labs and teams: Prof. Fäh is affiliated with the Swiss Seismological Service (SED) and actively contributes to ETH Zurich’s seismic monitoring infrastructure. His team collaborates with institutions in Japan (KiK-net network) and applies cutting-edge geophysical techniques to study subglacial environments and lakebed geotechnics.

Federica Sandroneمشاهده پروفایل

مدرس

• Rock Mechanics
• Tunnel Engineering
• Underground Construction
• +۴ مورد دیگر

Federica Sandrone is a Lecturer at the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as a Scientist at the Laboratory of Experimental Rock Mechanics (LEMR) within the Institute of Civil Engineering. Her academic career spans over 15 years with continuous contributions to tunnel engineering and rock mechanics research. Her research focuses on the intersection of rock mechanics and tunnel engineering, with particular expertise in tunnel pathology analysis, TBM performance in challenging geological conditions, and long-term tunnel behavior. Sandrone's work bridges theoretical analysis with practical engineering applications, addressing real-world problems in tunnel infrastructure management and maintenance. Her research methodology combines field investigations, laboratory testing, and numerical modeling to understand complex geomechanical behaviors. Analysis of her recent publications reveals a consistent focus on tunnel inspection methodologies, TBM performance prediction in difficult ground conditions, and the long-term behavior of tunnel structures. Her work has evolved from fundamental tunnel pathology studies to more advanced applications involving GIS integration, probabilistic modeling, and modern inspection techniques including laser scanning and image analysis. Engineer at SBB-Infrastructure (2008-present) responsible for Tunnels Management and Maintenance Assistant for Tunnel Engineering courses (2007-present) PhD supervision including Erika Paltrinieri's 2015 thesis on TBM performance Development of tunnel inspection methodologies and condition assessment procedures Her teaching activities include courses in Rock Mechanics and Underground Construction, where students learn about the mechanical behavior of rock materials, tunnel excavation and support design, planning and management of underground works, and risk assessment in tunnel construction.

Adrian Perrigمشاهده پروفایل

استاد

• Network Security
• Cybersecurity
• Distributed Systems
• +۳ مورد دیگر

Adrian Perrig is a Full Professor at the Department of Computer Science at ETH Zürich. He leads research in network security, distributed systems, and internet architecture, focusing on projects like the SCION secure internet architecture and its commercialization through Anapaya Systems. His work emphasizes secure communication, denial-of-service defense, and public key infrastructure (PKI) innovations. Affiliations: ETH Zürich, Institute for Information Security Key Contributions: SCION, SAGE, RHINE, F-PKI Research interests include path-aware networks, cryptographic protocols, and resilient systems. His publications span over 295 results since 2005, with notable awards including the Best Paper Award (CoNEXT 2021) and ANRP 2023. He has contributed to foundational work in secure routing, DNS security, and GPU attestation. Scientific awards include Best Paper Awards at CoNEXT and ACM SIGCOMM, as well as recognition for applied networking research. His work bridges academia and industry, addressing challenges in global network security and scalability.

• Geotechnics
• Numerical Simulations
• Bayesian Inference
• +۴ مورد دیگر

Stéphane Commend is an Associate HES Professor at the Fribourg School of Engineering and Architecture (HEIA-FR) under HES-SO Valais-Wallis. He also holds a lecturer role at the School of Engineering and Management of the Canton of Vaud. His primary research focuses on geotechnics, numerical simulations, and probabilistic modeling applied to infrastructure projects like tunneling and deep excavations. Education and affiliations include roles across multiple HES-SO institutions, with a strong emphasis on integrating advanced computational methods into geotechnical engineering. Notable projects include the Grand Paris Express tunnel project, Bayesian inference for wood constitutive modeling, and probabilistic risk analysis for urban construction. Research interests span soil-structure interaction, finite element modeling, and uncertainty quantification. Recent work emphasizes Bayesian methods for parameter calibration, machine learning in excavation design, and natural hazard vulnerability assessment. Key contributions include frameworks linking ZSOIL and UQLab for reliability analysis, and prototypes like SLIDE-PM for mudflow impact modeling. Current projects (e.g., iBAG and OptiSoil) focus on optimizing construction methods using AI and data-driven approaches. He leads collaborative teams across HES-SO institutes and academic partners like EPFL and CETU. Key Projects: iBAG Project (2022–2025): Bayesian methods in geotechnics OptiSoil (2019–2025): Machine learning for excavation design TULIP Project: TBM-pile interaction probabilistic analysis

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.

• Reinforcement Learning
• Computational Decision-Making
• Artificial Intelligence
• +۵ مورد دیگر

Carlo D'Eramo is Professor and Head of the Reinforcement Learning and Computational Decision-Making professorship at the Center for Artificial Intelligence and Data Science (CAIDAS) of University of Würzburg. He additionally serves as an independent group leader of hessian.AI, focusing on developing lightweight methods to obtain adaptive autonomous agents that can handle real-world complexity. His academic journey includes: B.Sc. in Computer Engineering from Politecnico di Milano (2011) M.Sc. in Computer Engineering from Politecnico di Milano (2015) Double degree in Computer Science from University of Illinois at Chicago (2015) Ph.D. in Information Technology from Politecnico di Milano (2019) Postdoctoral research at TU Darmstadt's Intelligent Autonomous Systems group (2019-2022) D'Eramo leads the LiteRL research group investigating how agents can efficiently acquire expert skills accounting for real-world complexity. His research spans multiple reinforcement learning domains including multi-task, curriculum, adversarial, options, and multi-agent RL. His work bridges theoretical advances with practical applications, particularly in robotics and decision-making systems. His recent publications (2023-2025) demonstrate significant contributions across exploration strategies, neural network architectures, multi-agent coordination, and physics-informed machine learning. His work frequently appears in top-tier venues including TMLR, RLJ, IEEE PAMI, ICML, and ICLR, with multiple papers receiving spotlight or oral presentation designations. Professional activities include: Senior area chair for RLC Area chair for AAAI, ACML, AISTATS, NeurIPS, and ICLR Reviewer for DFG and ERC proposals Creator of MushroomRL reinforcement learning framework D'Eramo actively contributes to academic community service while mentoring researchers in his group. His work on lightweight methods aims to make reinforcement learning more practical for real-world applications across various domains.

Tova Miloمشاهده پروفایل

استاد

• Databases
• Big Data Management
• Machine Learning for Data Management
• +۷ مورد دیگر

Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.

Fabio Nobileمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Tim Ruben Davidsonمشاهده پروفایل

پژوهشگر

• deep generative models
• agentic systems
• synthetic data
• +۱ مورد دیگر

Tim Ruben Davidson is a Researcher at the Digital Life Lab (DLAB) within the School of Computer and Communication Sciences at EPFL. His current role is Doctoral Assistant, pursuing a doctoral program in Computer and Communication Sciences. His research focuses on advanced machine learning topics including deep generative models, agentic systems, synthetic data applications, and representation learning. His work bridges theoretical advancements in AI with practical implications, particularly in understanding AI agency, synthetic data generation, and latent space structures. Key research trends in his articles include exploring AI self-awareness, evaluating AI-driven peer review systems, and optimizing generative models through geometric and topological approaches. He has contributed to foundational studies on hyperspherical VAEs and reparameterization techniques on Lie groups, advancing mathematical foundations of neural networks. His work is published in top-tier venues and reflects interdisciplinary engagement between computer science, mathematics, and ethical AI considerations.

• Dynamical systems and gradient flows
• Analysis of partial differential equations
• Stochastic analysis
• +۲ مورد دیگر

Arnulf Jentzen is a distinguished mathematician holding dual positions as Presidential Chair Professor at the School of Data Science and Shenzhen Research Institute of Big Data at The Chinese University of Hong Kong, Shenzhen, and as Full Professor at the Faculty of Mathematics and Computer Science at the University of Münster, Germany. His research spans multiple institutions with significant contributions across mathematical disciplines. His primary research interests include dynamical systems and gradient flows (particularly geometric properties, domains of attractions, blow-up phenomena), analysis of partial differential equations, stochastic analysis (including stochastic calculus and well-posedness analysis), machine learning (with focus on mathematics for deep learning and stochastic gradient descent methods), and numerical analysis (particularly computational stochastics and computational finance). His work demonstrates a strong interdisciplinary approach bridging pure mathematics with practical computational applications. Jentzen's publication record shows a clear trend toward machine learning applications in solving complex mathematical problems, particularly in overcoming the curse of dimensionality in high-dimensional PDEs through deep neural networks. His research group actively publishes on optimization methods like Adam, convergence analysis, and applications of deep learning to partial differential equations and optimal control problems. ICBS Frontier of Science Award in Mathematics (2024) Fellow, Lamarr Institute (2023) ERC Consolidator Grant (2022) Joseph F. Traub Prize for Achievement in Information-Based Complexity (2022) Felix Klein Prize, European Mathematical Society (EMS) (2020) Professor Jentzen advises numerous PhD students across both institutions and serves on multiple editorial boards including SIAM Journal on Numerical Analysis, Journal of Complexity, and Communications in Computational Physics. His research group at Münster and CUHK-Shenzhen focuses on developing mathematical foundations for machine learning with applications to scientific computing problems. He has received significant research funding including an ERC Consolidator Grant, supporting his interdisciplinary work at the intersection of mathematics and artificial intelligence.

Olivia Romppainen-Martiusمشاهده پروفایل

استاد

• Climate Science
• Meteorology
• Natural Hazards
• +۳ مورد دیگر

Prof. Olivia Romppainen-Martius is a Professor and Group Leader of the Climate Impacts research group at the Institute of Geography, University of Bern. She co-directs the Mobiliar Lab for Natural Risks and focuses on short-term climate dynamics, high-impact weather systems, and midlatitude weather patterns. Her work integrates advanced modeling techniques with real-world applications in disaster risk reduction. Her research spans climate variability analysis, probabilistic forecasting of extreme events, and the development of early warning systems. She leads collaborative projects combining machine learning with Earth system modeling to improve understanding of weather extremes under climate change. Key interests include flood forecasting, hailstorm dynamics, and Mediterranean cyclone behavior. Prof. Romppainen-Martius has pioneered web-based visualization tools for impact-based flood warnings and has contributed to kilometer-scale Earth system modeling frameworks like nextGEMS. Her methodologies emphasize statistical reconstruction of historical weather events and the application of AI to convective weather prediction. Her research outputs reflect a strong focus on bridging climate science with practical risk management strategies, particularly in alpine and pre-alpine environments. Current efforts include long-term analysis of Swiss hailstorms and development of crowd-sourced data integration for meteorological observations.

Andreas Krauseمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۵ مورد دیگر

Andreas Krause is a Professor of Computer Science at ETH Zurich, leading the Learning & Adaptive Systems Group and serving as Chair of the ETH AI Center and Academic Co-Director of the Swiss Data Science Center. He previously held an Assistant Professorship at Caltech. His research focuses on machine learning, Bayesian optimization, reinforcement learning, and AI safety. Key contributions include advancements in active sensing, learning-based control, and probabilistic AI. Affiliations: ETH AI Center, Swiss Data Science Center, Max Planck Institute for Intelligent Systems (Fellow) Education: PhD in Computer Science (Carnegie Mellon University, 2008); Diplom in Computer Science and Mathematics (Technical University of Munich, 2004) His work bridges theory and practice, with applications in robotics, healthcare, and climate science. Notable awards include the IEEE Fellow distinction, Rössler Prize, and multiple ERC grants. Krause actively chairs ICML conferences and contributes to global AI policy through the UN’s High-level Advisory Body on AI. Research Interests: Machine learning fundamentals, adaptive systems, AI safety, and optimization. Recent work explores safe exploration in reinforcement learning, causal modeling, and large-scale generative AI.

Fabio Crestaniمشاهده پروفایل

استاد

• Information Retrieval
• Text Mining
• Digital Libraries
• +۳ مورد دیگر

Fabio Crestani is a Full Professor of Informatics at the Università della Svizzera italiana (USI) since 2007, serving as Pro-rector for Internationalisation since March 2024. He previously held roles at the University of Strathclyde (UK) and conducted sabbaticals at institutions like UC Berkeley and Xerox PARC. His expertise spans Information Retrieval, Text Mining, and Digital Libraries, with over 250 publications and editorial leadership roles, including Editor-in-Chief of Information Processing and Management (2008–2015). Education: PhD and MSc in Computing Science, University of Glasgow (UK) Degree in Statistics, University of Padova (Italy) Research Interests: Advanced information access systems Conversational search and user interaction models Machine learning for text analysis Early risk prediction (e.g., mental health via social media) Grants & Collaborations: Funded by Swiss National Science Foundation, Hasler Stiftung, and EU projects. Collaborations with institutions in UK, Italy, Spain, USA, and Malaysia. Labs & Teams: Lead the Information Retrieval Group at USI, which focuses on distributed IR, personalization, and mobile information access. The group includes 10+ researchers and has produced influential work in top-tier venues like SIGIR and ACL.

Thomas Sheldrakeمشاهده پروفایل

استادیار

• Volcanology
• Geochemistry
• Geochronology
• +۳ مورد دیگر

Thomas Sheldrake is an SNSF Eccellenza Professorial Fellow (Assistant Professor) at the Department of Earth Sciences, University of Geneva. His research focuses on magmatic processes, volcanic systems, and geochronological methods with applications to understanding eruption dynamics and tectonic interactions. He specializes in integrating geochemical, petrological, and computational approaches to study magma evolution and volcanic hazards. Research interests include: Magmatic system evolution and eruption triggers Zircon-based geochronology and petrochronology Volcanic crystal records of magma storage and degassing Remote sensing of volcanic structures using micro-CT Statistical modeling of volcanic eruption data His work spans field-based studies in the Caribbean, Iceland, and Italy, alongside computational methods for mineral analysis. Notable contributions include linking sea-level changes to magmatic pulses during the Messinian crisis and developing zircon age spectra techniques for magma evolution quantification. His recent 2025 study on Caribbean coral porosity demonstrates interdisciplinary approaches bridging geology and environmental science. Scientific Awards: SNSF Eccellenza Professorial Fellowship Research collaborations involve institutions globally, with frequent co-authorship on topics ranging from Holuhraun eruption geochemistry to Saint Kitts volcanic stratigraphy. His work emphasizes bridging fundamental geoscience with applied volcanic hazard assessment.

Simon Weberمشاهده پروفایل

پژوهشگر

• Unique Sink Orientations
• Computational Complexity
• Combinatorial Algorithms
• +۷ مورد دیگر

Simon Weber is a researcher affiliated with the ETH Zurich (Department of Computer Science). His work focuses on Unique Sink Orientations (USOs) , a combinatorial abstraction of optimization problems like Linear and Quadratic Programming. Simon's research spans three areas: (1) Structure of USOs and their links to Oriented Matroids; (2) Constructions of high-dimensional USOs to analyze algorithm complexity; and (3) Algorithmic improvements for sink-finding. He also explores topics in graph compression, neural networks, and ∃R-complete problems. Key Publications: PhD thesis on USO reductions, ∃R-completeness in neural training, and USO phase analysis. Scientific Contributions: Advances in USO complexity, FPT algorithms for MaxCut, and recognition of geometric hypergraphs and nerves of convex sets. He has supervised multiple theses at ETH Zurich, including topics on USO visualization, MaxCut algorithms, and necklace splitting. His teaching experience includes being a Head Assistant for courses like Geometry: Combinatorics & Algorithms and Topological Data Analysis . Simon's work has been recognized with Best Paper and Best Student Paper Finalist awards at SC19.