جست‌وجوی استاد و پژوهشگر

Jan S Hesthavenمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Analysis
• Discontinuous Galerkin Methods
• +۷ مورد دیگر

Jan S Hesthaven is a Professor of Mathematics and Dean of the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds adjunct professorships at Brown University and the Technical University of Denmark. His research focuses on computational methods for partial differential equations, model order reduction, and high-order numerical methods like discontinuous Galerkin schemes. He leads the Chair of Computational Mathematics and Simulation Science (MCSS) and founded SCITAS, EPFL's Scientific IT and Application Support unit. Education: M.Sc. (1991) and Ph.D. (1995) in Numerical Analysis from Technical University of Denmark, with postdoctoral training at Brown University and NASA Langley. He earned a Dr.Techn. (2009) for contributions to nodal discontinuous Galerkin methods. Research interests include computational fluid dynamics, seismic monitoring of porous media, and machine learning integration with PDE solvers. Notable contributions include physics-informed neural networks, reduced basis methods for acoustics, and surrogate models for gravitational waves. He has received prestigious awards like the SIAM Fellowship (2014) and the Philip J. Bray Teaching Award (2004). Leadership roles: Dean of SB (2017–present), Director of CCV at Brown (2006–2013), and Deputy Director of ICERM (2010–2013). His work bridges fundamental mathematics with applications in engineering, physics, and environmental science. Grants and collaborations include NSF funding for model reduction and NASA partnerships. He supervises interdisciplinary projects on seismic data analysis and acoustic simulations. SCITAS, under his leadership, provides high-performance computing support across EPFL.

• Quantum Physics
• Computational Physics
• Quantum Chemistry
• +۳ مورد دیگر

Giuseppe Carleo is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB) and the Institute of Physics (IPHYS). He leads the Numerical Quantum Sciences Lab (CQSL) and holds roles such as Co-directeur in STI-SSIQ and member of doctoral committees. His research focuses on computational quantum physics, quantum many-body systems, and machine learning applications in physics. He teaches courses like Advanced computational physics and Computational quantum physics . He supervises multiple PhD students and has contributed to over 50 publications, including studies on neural network quantum states and quantum dynamics. His work bridges theoretical physics with computational methods, addressing challenges in quantum chemistry and condensed matter systems. Education: PhD in Theory and Numerical Simulation of Condensed Matter from SISSA, Trieste (2007-2011). His research interests emphasize leveraging machine learning and numerical techniques to solve complex quantum problems. He actively contributes to the development of open-source tools like NetKet for quantum system simulations.

Maciej Bestaمشاهده پروفایل

پژوهشگر

• Sparse Graph Computations
• Large Language Models
• Graph Neural Networks
• +۷ مورد دیگر

Maciej Besta is a leading researcher at ETH Zurich's Institute for Computing Platforms, where he heads research initiatives at the Scalable Parallel Computing Lab (SPCL) and contributes to the ETH Future Computing Laboratory (EFCL). Working under the mentorship of Professor Torsten Hoefler, he has established himself as a prominent figure in high-performance computing, graph processing, and large language models. Position: Researcher at Institute for Computing Platforms, ETH Zurich Research Leadership: Head of Sparse Graph Computations and Large Language Models Research at SPCL Collaboration: Leads project management for SPCL's contributions to ETH Future Computing Laboratory Besta's research spans multiple abstraction levels, from hardware and network topologies to middleware, algorithms, and programming models. His primary focus areas include graph-enhanced language models, graph neural networks, graph databases, and sparse models, with applications across various computational settings. He approaches these problems through rigorous performance modeling and formal reasoning, emphasizing both scalability and practical implementation. His recent publications reveal a clear trend toward integrating graph structures with language models and AI systems. Besta has pioneered work on graph databases, knowledge graphs of thoughts, and higher-order graph neural networks, while maintaining his strong foundation in high-performance computing and network topology design. His research bridges traditional HPC with cutting-edge AI, creating novel approaches for efficient large-scale computation. IEEE TCSC Award for Excellence in Scalable Computing (Early Career, 2023) Multiple Best Paper Awards at Supercomputing conferences (2022, 2023) ACM SIGHPC Doctoral Dissertation Award (2022) ETH Medal for outstanding doctoral thesis (2021) Fellow of The Explorers Club (2022) Besta actively mentors ETH Zurich students through semester projects, Bachelor's, and Master's theses, focusing on graph processing and related computer science challenges. His mentorship extends beyond technical guidance, incorporating lessons from his extensive polar and mountaineering expeditions that emphasize mental resilience, efficient risk management, and leadership. He has supervised numerous student projects that have resulted in high-impact publications at top-tier conferences. As a core member of the Scalable Parallel Computing Lab, Besta collaborates with researchers across ETH Zurich and international institutions. His unique approach integrates insights from extreme environment expeditions into research methodology, creating a distinctive framework for tackling complex computational problems. The lab's work under his leadership spans theoretical modeling, practical implementation, and real-world deployment of high-performance systems.

Jan-Hendrik Bastekمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Mechanical Metamaterials
• Physics-Informed Machine Learning
• +۳ مورد دیگر

Jan-Hendrik Bastek is a Researcher affiliated with the Department of Mechanics and Materials Research at ETH Zürich. His work focuses on computational mechanics, mechanical metamaterials, and the application of physics-informed machine learning in engineering challenges. Based in Zürich, Switzerland, he contributes to advancing material design through interdisciplinary approaches that integrate deep learning, inverse problem solving, and advanced modeling techniques. His research interests span topics such as nonlinear mechanics, material property prediction, and the development of novel metamaterials with tailored properties. He has published extensively on physics-informed neural networks, data-driven simulation methods, and the optimization of truss-based metamaterials. His work bridges computational science and engineering applications, emphasizing practical solutions for complex structural and material systems. Recent publications highlight trends in applying video denoising diffusion models for metamaterial design, GNN-assisted phase space integration, and viscoelastic behavior analysis of time-dependent materials. His contributions reflect a commitment to merging traditional mechanics with cutting-edge AI techniques to address forward and inverse problems in computational mechanics. No scientific awards or grants are explicitly listed in the provided information. Bastek collaborates within the Mechanics and Materials Research group at ETH Zürich, contributing to both fundamental and applied research projects in the field.

Sebastian Kozerkeمشاهده پروفایل

استاد

• Magnetic Resonance Imaging
• Cardiac Mechanics
• Perfusion Imaging
• +۳ مورد دیگر

Prof. Dr. Sebastian Kozerke is Full Professor at ETH Zürich's Department of Information Technology and Electrical Engineering, leading the Professorship for Biomedical Imaging. His research program develops advanced magnetic resonance imaging methods for cardiac applications, focusing on ultra-fast dynamic imaging of perfusion, cardiac mechanics, and microstructure analysis. Key innovations include k-t undersampling techniques and parallel imaging methods that significantly advance spatiotemporal resolution in medical imaging. Professor Kozerke's research spans multiple domains including perfusion imaging, diffusion tensor imaging for myocardial microstructure, and real-time metabolic imaging using dynamic nuclear polarization. Current investigations explore hyperpolarized 13 C pyruvate metabolic imaging, neural network applications for cardiac analysis, and low-field MRI techniques. His work consistently bridges fundamental physics with clinical translation. Publications demonstrate leadership in cardiovascular MRI innovation. Recent work establishes consensus standards for hyperpolarized MRI studies (2025), develops deep learning frameworks for flow quantification (FlowMRI-Net), and advances microstructure analysis in cardiomyopathy (2025). Earlier foundational work includes contributions to diffusion imaging, parallel MRI methods, and cardiac DTI techniques. Professor Kozerke teaches core courses including 'Biomedical Imaging' (227-0385-10L), 'Biomedical Engineering' (227-0386-00L), and leads the 'Seminar on Biomedical Magnetic Resonance' (227-0980-00L). He founded EXCITE Zurich, a joint center for experimental and clinical imaging technologies. Career progression includes: PhD and Venia legendi from ETH Zurich, research at King's College London, co-founding GyroTools (2003), professorship at King's College London (2008), University of Zurich (2010), and ETH Zurich dual appointment (2014).

• Power Systems
• Energy Transition
• Grid Reliability and Risk Engineering
• +۳ مورد دیگر

Dr. Blazhe Gjorgiev is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. His research focuses on power systems reliability, energy transition modeling, and grid risk engineering. He leads the Reliability and Risk Engineering group, addressing challenges in grid resilience, renewable integration, and cascading failure mitigation. His work bridges machine learning applications with physical grid systems, emphasizing data-driven approaches for infrastructure optimization. Key research interests include: Power grid reliability and vulnerability analysis Renewable energy integration and policy implications Data-driven modeling of transmission and distribution systems Machine learning for fault detection and grid operation Recent publications highlight advancements in anomaly detection for power line inspection, European energy transition policies, and graph-based power grid benchmarking. His work often intersects with socio-technical issues, such as energy inequities and infrastructure resilience in aging systems. Dr. Gjorgiev collaborates on projects like the Nexus-e platform for energy-economic assessments and the Cascades-Risk model for expansion planning. His research has practical implications for grid modernization, policy design, and sustainable energy systems.

• Wireless Communication
• Machine Learning
• Digital Signal Processing
• +۶ مورد دیگر

Prof. Christoph Studer is a Full Professor of Integrated Information Processing at ETH Zurich's Department of Information Technology and Electrical Engineering. He leads the Integrated Systems Laboratory and directs SwissChips. His research focuses on wireless communication, machine learning, signal processing, and hardware-efficient algorithms, with applications in B5G systems, sensing-communication integration, and low-power signal processing. He holds a Ph.D. and M.S. from ETH Zurich (2009 and 2006) and has held academic positions at Cornell University before returning to ETH in 2020. Notable honors include the NSF CAREER Award (2017), ETH Medal for Doctoral Dissertation (2011), and multiple teaching awards. Education: M.S. and Ph.D. in Information Technology and Electrical Engineering, ETH Zurich (2006, 2009) Visiting Researcher, Stanford University (2005) Research Interests: Develops algorithms and hardware for high-throughput, low-power wireless systems. Key areas include: B5G multi-antenna systems and simultaneous sensing-communication (SISCO) Analog-to-feature (A2F) conversion for low-power signal classification Hardware-software co-design for efficient microchip integration Publications: Focus on channel charting, jammer mitigation, and deep learning for communication. Recent work includes CSI2Vec, jammer-resilient synchronization, and distributed MIMO systems. Awards: US NSF CAREER Award (2017) Michael Tien Teaching Award (2016) ETH Medal for Doctoral Thesis (2011) Labs/Teams: Leads the Integrated Systems Laboratory at ETH and directs SwissChips, a national initiative for integrated circuit development.

• Structural Health Monitoring
• Wind Energy
• Graph Neural Networks
• +۳ مورد دیگر

Dr. Gregory Duthé is a researcher at the ETH Zürich in the Structural Mechanics and Monitoring department. His work bridges computational modeling and structural diagnostics with applications in renewable energy systems. Research contributions include: Development of Graph Neural Networks for aerodynamic flow reconstruction Advancements in wake-induced load estimation for wind farms Innovations in unsupervised fault detection for offshore turbine systems Key publication trends show focus on physics-informed machine learning (2025), multi-agent infrastructure decision support (2025), and leading edge erosion modeling (2021). He combines graph-based architectures with structural health monitoring techniques across wind energy applications. Contact: duthe@ibk.baug.ethz.ch

Charalampos Anastasiouمشاهده پروفایل

استاد

• Theoretical Particle Physics
• Quantum Field Theory
• Particle Phenomenology
• +۳ مورد دیگر

Charalampos Anastasiou is a Full Professor of Theoretical Particle Physics at ETH Zurich's Department of Physics. He leads research in quantum field theory and its applications to particle physics phenomenology, particularly in predicting Higgs boson signals at the Large Hadron Collider (LHC). His work includes developing mathematical methods for Feynman diagrams and precision calculations for collider experiments. He has held academic positions at ETH Zurich since 2007, including Associate Professor (2010–2013) and Full Professor (2013–present). His career includes research roles at CERN and Stanford Linear Accelerator Center. Education: Ph.D. in Particle Theory, Durham University, UK (2001) Research Interests: Anastasiou's research focuses on advancing theoretical frameworks for particle physics, with emphasis on perturbative quantum field theory, collider phenomenology, and precision calculations for Higgs boson interactions. His work bridges mathematical rigor and experimental validation, contributing to the understanding of fundamental forces and particle interactions. Publications: His recent publications span superconducting magnet design for future colliders, quantum-resistant cryptographic methods, and material science studies. These reflect his interdisciplinary approach to addressing challenges in high-energy physics and technology. Awards & Grants: ERC Advanced Grant: PertQCD (2016) ERC Advanced Grant: IterQCD (2010) University Latsis Prize (2009) Advising & Labs: Anastasiou collaborates on projects like the Muon Collider and LHC upgrades. His lab develops novel superconducting magnet technologies and computational tools for high-energy physics experiments. He actively contributes to international initiatives like the Future Circular Collider (FCC) and European Strategy for Particle Physics.

• Graph-based learning
• Adaptation and learning in non-stationary environments
• Intelligence for embedded systems
• +۵ مورد دیگر

Cesare Alippi is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), and also holds a professorship at Politecnico di Milano, Italy. He serves as a visiting Professor at Guangdong University of Technology (China) and Consultant Professor at Northwestern Polytechnic of Xi'an (China). His academic leadership extends to multiple international institutions where he has served as a visiting researcher including UCL (UK), MIT (USA), ESPCI (France), CASIA (China), A*STAR (Singapore), and University of Kobe (Japan). Professor Alippi's research interests center around graph-based learning, adaptation and learning in non-stationary environments, and intelligence for embedded, cyber-physical systems and IoT. His work bridges theoretical foundations with practical applications in sensor networks, environmental monitoring, and industrial processes. He has established significant research infrastructure including the Wireless Embedded Systems (WEmSy) Lab and the Internet of Things Lab, with notable deployments for marine environment monitoring in Queensland, Australia and the Fiji Islands, as well as rockfall and landslide monitoring systems across Italy and Switzerland. His research output shows a clear evolution toward graph-based deep learning approaches for time series analysis, anomaly detection, and spatiotemporal forecasting, reflecting the growing importance of graph neural networks in handling complex relational data in non-stationary environments. Major Awards: IEEE CIS Enrique Ruspini Meritorious Service Award (2024) IEEE CIS Outstanding Computational Intelligence Magazine Paper Award (2018) Gabor Award from International Neural Network Society (2016) IBM Faculty Award (2013) IEEE Instrumentation and Measurement Society Young Engineer Award (2004) Professor Alippi has held significant leadership roles including Past Board of Governors member of the International Neural Network Society, Past member of the Administrative Committee of the IEEE Computational Intelligence Society, and Past Vice-President for Education of the IEEE Computational Intelligence Society. He has served as Associate Editor for Proceedings of IEEE and several other prestigious journals. His research has been supported through numerous grants including an IBM Faculty Award in 2013 specifically for research on Intelligent Embedded Systems working in non-stationary environments. His research infrastructure includes the Wireless Embedded Systems (WEmSy) Lab and the Internet of Things Lab, with notable deployments including a sophisticated automatic, adaptive, sustainable and reliable wireless monitoring system for marine environments deployed in Queensland, Australia (2007) and under deployment at the Fiji Islands (2014-2015). He has also led several top-world deployments for rockfall and landslide monitoring across Italy and Switzerland since 2010, demonstrating the practical impact of his research in real-world harsh environments.

Ingo Scholtesمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Graph Mining
• +۵ مورد دیگر

Prof. Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). He holds a doctorate in computer science and mathematics from the University of Trier and has held roles including SNSF Professor at the University of Zurich, Full Professor at Bergische Universität Wuppertal, and Senior Assistant at ETH Zürich. His research focuses on higher-order graph analytics for temporal networks, machine learning, and computational social science. Education: PhD in Computer Science (University of Trier, Germany), Postdoctoral Research at ETH Zürich (2011–2016), and prior roles at Karlsruhe Institute of Technology and CERN. Research Interests: Machine learning on graphs, temporal network analysis, higher-order network models, and their applications in software engineering and social systems. He develops open-source tools like pathpy and git2net for network analysis. Recent Work: Focuses on causality-aware graph neural networks, temporal graph isomorphism, and network science applications in AI. Recent articles include studies on temporal network dynamics, path prediction, and Bayesian inference of network transitions. Awards: SNSF Professorship (2018), Junior-Fellowship (2014), and German Academic Scholarship Foundation (2004-2005). Active in editorial roles for EPJ Data Science and leadership in GI's Computational Social Science working group.

Robert Weibelمشاهده پروفایل

استاد

• Geographic Information Science
• Cartographic Generalization
• Movement Analysis
• +۱۰ مورد دیگر

Robert Weibel is a Professor in the Department of Geography at the University of Zurich, where he conducts cutting-edge research in Geographic Information Science (GIScience), cartographic generalization, movement analysis, and human mobility. His work bridges computer science, geography, and health sciences, with strong applications in aging, environmental monitoring, and urban planning. Research Interests: His primary research areas include: Computational movement analysis and trajectory modeling Cartographic and map generalization using AI and deep learning Indoor and outdoor navigation systems Spatial cognition and human mobility GIScience and geospatial data science Linguistic geography and dialect modeling Publication Trends: His recent publications (2023–2025) reflect a strong focus on AI-driven map generalization (e.g., SpaGAN, GNNs), explainable AI in cartography, indoor navigation modeling, and interdisciplinary studies on human mobility in aging and post-stroke recovery. He frequently publishes in top journals such as the International Journal of Geographical Information Science , ISPRS Journal , and Computers, Environment and Urban Systems . Scientific Contributions: Robert Weibel is a leading figure in GIScience, particularly in map generalization and movement data science. He has contributed to major research initiatives including the MOBITEC-Stroke project and has co-organized key conferences like GIScience. His work on real-time generalization, semantic modeling, and deep learning for cartography has advanced the field significantly. Advising and Collaboration: He has supervised numerous students and collaborators across disciplines, including in gerontology, computer science, and ecology. His research often involves interdisciplinary teams and international partnerships. Labs and Teams: He is affiliated with the GIScience research group at the University of Zurich, contributing to projects involving mobile mapping, real-time data processing, and spatial analytics. His work is integral to the development of intelligent geospatial systems and e-learning in GIS (e.g., the GITTA project).

Kerstin Deneckeمشاهده پروفایل

استاد

• Natural Language Processing in Healthcare
• Conversational Agents and Chatbots
• Patient-centered Digital Health
• +۶ مورد دیگر

Prof. Dr. Kerstin Denecke is a Professor at Bern University of Applied Sciences (BFH) in the Engineering and Computer Science department, where she co-leads the Institute for Patient-centered Digital Health (PCDH). She maintains an affiliate position at the University of Victoria's School of Health Information Science and has conducted research visits at Canadian and New Zealand institutions. Her academic journey includes leadership roles in national and EU research projects, heading the 'Digital Patient Model' research group at Leipzig University prior to joining BFH in 2015. Dr. Denecke's research focuses on the intersection of artificial intelligence and healthcare, with particular expertise in natural language processing, conversational agents, and patient-centered digital health solutions. Her work spans multiple clinical domains including mental health interventions, radiology documentation, and suicide prevention. She employs participatory design methodologies that actively involve healthcare professionals and patients in the development process. Analysis of her recent publications reveals a strong emphasis on the practical application of large language models in clinical settings, ethical considerations in healthcare AI, and the development of taxonomies for conversational agents. Her research demonstrates a consistent trajectory toward making AI tools clinically usable while addressing real-world healthcare challenges. Young Talent Award of the Friedrich Wingert Foundation for outstanding approaches in medical linguistics and semantics Best Poster Award at MIE 2021 for 'Crowdsourcing for Creating a Dataset for Training a Medication Chatbot' Professor Denecke actively supervises bachelor, master, and PhD students, with current doctoral candidates working on medical text classification, social media chatbots for physical activity, and NLP applications in radiology. Her research is supported by multiple active grants including Innosuisse-funded projects on generative AI for clinical documentation. She leads the PCDH/AI for Health research unit which develops practical AI solutions for healthcare challenges while maintaining strong connections with clinical partners. The Institute for Patient-centered Digital Health under her co-leadership focuses on developing human-centered digital health solutions that address real clinical needs through interdisciplinary collaboration between computer scientists, healthcare professionals, and patients.

Anastasios Kouvelasمشاهده پروفایل

مدرس

• Traffic flow theory
• System and control theory
• Optimization
• +۱۰ مورد دیگر

Dr. Anastasios Kouvelas is a Lecturer at ETH Zurich, where he serves as head of the Road Traffic Engineering research group at the Institute of Transport Planning and Systems (IVT), Department of Civil, Environmental and Geomatic Engineering. He has held this position since August 2018, succeeding Dr. Monica Menendez who moved to New York University in Abu Dhabi. Prior to joining ETH Zurich, he was a research associate at the Urban Transport Systems Laboratory (LUTS) at EPFL (2014-2018) and a postdoctoral fellow at Partners for Advanced Transportation Technology (PATH) at the University of California, Berkeley (2012-2014). Dr. Kouvelas' research focuses on modeling, simulation, optimization and traffic flow control. His work aims to develop real-time solutions based on control theory and operations research methods. The Road Traffic Engineering group develops algorithmic solutions that are components of intelligent transportation systems used in traffic control centers. Recent technological advances in autonomous vehicles have expanded their research topics as the industry seeks efficient operational solutions for autonomous mobility. They are particularly interested in extending their work to the design of advanced management strategies for urban networks that utilize connected vehicles to improve traffic operations and develop network-wide control strategies that minimize environmental impacts. His recent publications (2023-2025) demonstrate strong focus on traffic prediction using deep learning techniques, bike lane allocation impacts on urban networks, transit network resilience against disruptions, vehicle trajectory extraction from aerial recordings, and traffic control for mixed traffic systems with connected and autonomous vehicles. His work bridges theoretical developments in control theory with practical traffic engineering challenges. Scientific Awards No specific scientific awards were mentioned in the provided information. Advising and Grants Dr. Kouvelas supervises PhD and Master's students in traffic engineering and intelligent transportation systems. His research is supported by various grants including a grant from the Hong Kong Research Grant Council (Grant No. GRF 11216323) for research on traffic speed prediction. Laboratories and Teams Dr. Kouvelas leads the multidisciplinary Road Traffic Engineering research group at IVT, which consists of researchers with backgrounds in civil engineering, electrical engineering, mechanical engineering, computer science, control, and operations research. The group's work spans multiple areas including traffic flow theory, traffic operations, connected and automated vehicles, and intelligent transportation systems.