جست‌وجوی استاد و پژوهشگر

Dimosthenis Pasadakisمشاهده پروفایل

پژوهشگر ارشد

• Graph Theory and Algorithms
• Data Science
• Machine Learning
• +۳ مورد دیگر

Dimosthenis Pasadakis is a Research Fellow at the Institute of Computing (CI) of the Università della Svizzera italiana (USI), where he serves as the Principal Investigator (PI) of the Huawei Research Center Zürich-funded project on Directed Acyclic Graph Partitioning for Scheduling Tasks. His research focuses on graph learning, combinatorial optimization, and large-scale algorithms for clustering, anomaly detection, and data analysis. He also holds the role of Chief Operating Officer (COO) at Panua Technologies Sagl, a Lugano-based software company specializing in high-end solutions for graph analytics and optimization. His academic journey includes a PhD successfully defended in 2023, supported by the Swiss National Science Foundation (SNSF) project on Balanced Graph Partition Refinement. He has organized minisymposia at SIAM LA 24 and PASC 24, and his work has been recognized with awards such as the IEEE HPEC Outstanding Paper Award (2024) and the IEEE SDS24 Best Poster Award (2024). Key research interests include spectral clustering, graph-based fraud detection, and high-performance computing. He actively collaborates with industry through grants and maintains an academic-industrial bridge through Panua Technologies.

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.

• Quantum Information
• Quantum Computing
• Trapped Ion Systems
• +۳ مورد دیگر

Dr. Pavel Hrmo is a Researcher at the Institute of Quantum Electronics (ETH Zürich), affiliated with the Department of Physics (D-PHYS). He specializes in experimental quantum information science, focusing on trapped ion systems for quantum computing and quantum simulation. His work includes developing microfabricated Penning traps, optimizing quantum control protocols, and studying ion dynamics under various experimental conditions. Research interests center on quantum hardware development, quantum gate calibration, and noise mitigation in trapped ion systems. He has contributed to advancing multiqubit operations, entanglement protocols, and scalable quantum architectures. Notable projects include experimental realizations of nonunitary multiqubit operations and native qudit entanglement in trapped ion processors. Recent publications (2018–2024) highlight his work on Penning trap designs, sideband cooling dynamics, and Bayesian calibration methods for entangling operations. His research bridges theoretical modeling with experimental validation, addressing challenges in quantum error correction and coherence preservation. Advising doctoral student Samuel Jele at ETH Zürich, his lab focuses on advancing trapped ion technologies for next-generation quantum computing systems. No scientific awards are listed in available records.

• Calculus of Variations
• Partial Differential Equations
• Optimal Transport
• +۴ مورد دیگر

Alessio Figalli is a renowned mathematician and Professor at the Department of Mathematics , ETH Zurich , where he also serves as Director of the Forschungsinstitut für Mathematik (FIM) . Born in Rome (Italy) in April 1984, he holds Italian citizenship and is a Swiss permanent resident. His academic journey includes a PhD from the Scuola Normale Superiore (Pisa) and École Normale Supérieure (Lyon), with postdoctoral roles at institutions like the University of Nice and École Polytechnique. Prior to ETH Zurich, he held professorships at the University of Texas at Austin (2009–2019). Figalli's research focuses on Calculus of Variations , Partial Differential Equations (PDEs) , Optimal Transport , and Free Boundary Problems . His work bridges pure and applied mathematics, with applications in geometry, probability, and physics. He is particularly known for contributions to optimal transport theory, regularity in free boundary problems, and stability estimates for geometric inequalities. Figalli has authored over 200 publications, including seminal papers on regularity of optimal transport maps , stability of geometric inequalities , and nonlocal PDEs . His invited lectures include plenary talks at the International Congress of Mathematicians (2018) and the Bourbaki seminar . He has supervised numerous PhD students and postdocs, fostering collaborations globally. Awards: Fields Medal (2018), EMS Prize (2012), Feltrinelli Prize (2017). Editorial Roles: Arch. Ration. Mech. Anal., Duke Math. J., and 10+ other journals . Grants: ERC Grant RSPDE , NSF awards, and the Swiss National Science Foundation. His ERC-funded project RSPDE explores regularity and stability in PDEs, while his mentorship has shaped the careers of over 30 students/postdocs. Figalli actively promotes mathematics through outreach, including public lectures and media engagements.

Rebecca Waagمشاهده پروفایل

پژوهشگر

• Neuroscience
• Molecular Biology
• Behavioral Neuroscience
• +۳ مورد دیگر

Dr. Rebecca Waag is a Researcher affiliated with the Department of Molecular and Behavioral Neuroscience at ETH Zurich. She is part of the Professorship for Molecular and Behavioral Neuroscience, contributing to cutting-edge research in neuroscience and molecular biology. Her work focuses on understanding stress physiology, neurovascular interactions, and cellular metabolism, particularly in the context of neurological disorders and adaptive responses to environmental challenges. Her research interests span neurovascular biology, molecular mechanisms of stress habituation, and the development of novel tools for behavioral assessment. She has pioneered methods like GrimACE, an automated system for evaluating pain and well-being in preclinical models. Her studies often integrate multi-omics approaches, such as single-nucleus RNA sequencing and quantitative behavioral analysis, to uncover latent biological phenomena. Dr. Waag's contributions include advancing knowledge on astrocytic roles in glucose metabolism, noradrenaline's influence on hippocampal gene expression, and the vascular gene Apold1's role in angiogenesis. Her work bridges fundamental research with translational applications, emphasizing ethical and technological innovations in neuroscience. While no specific awards are listed, her publications reflect a prolific and impactful career. She is actively involved in lab operations at ETH Zurich’s Institute for Neuroscience, though no student advisees or grants are explicitly mentioned in available records.

• 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

• Artificial Intelligence
• Machine Learning
• Computational Fluid Dynamics
• +۳ مورد دیگر

Dr. Georgios Kissas is a Lecturer at the Department of Computer Science at ETH Zurich, affiliated with the ETH AI Center. His research focuses on integrating machine learning with computational science and engineering, particularly in neuro-symbolic AI, operator learning, and cardiovascular modeling. He explores applications in medical biophysics, computational fluid dynamics, and scientific computing. Recent work emphasizes hybrid neural operator networks, differentiable simulations, and digital twin technologies for personalized medicine. Key research themes include: Development of neuro-symbolic systems for analytical solutions of differential equations Machine learning approaches for hemodynamic simulations and vascular parameter estimation Reduced-order modeling for cardiovascular disease analysis using biobank data His publications demonstrate contributions to operator learning frameworks, uncertainty quantification methods, and biomedical engineering applications. Current efforts bridge AI innovation with practical clinical and engineering challenges.

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.

• Probabilistic Machine Learning
• Causal AI
• Imprecise Probabilities
• +۲ مورد دیگر

Marco Zaffalon is Professor and Scientific Director at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), affiliated with the Università della Svizzera italiana's Faculty of Informatics. He leads a 30-member research group on probabilistic machine learning and has published over 150 papers. Education: M.Sc. in Computer Science (Università degli Studi di Milano) Ph.D. in Applied Mathematics (Università degli Studi di Milano) Research spans probabilistic machine learning, causal AI, imprecise probabilities, and quantum computation. His work develops theoretical foundations for uncertainty reasoning and applies them to AI systems. Recent publications focus on causal inference with LLMs, counterfactual computation, and quantum decision models. Articles consistently explore intersections of probability theory, computational methods, and real-world applications like healthcare. Trends include advancing tractability in causal queries and bridging logical frameworks with machine learning. Administrative roles include co-founding Artificialy (as Chief Scientist) and directing IDSIA since 2019. He teaches courses in Causal AI, Uncertain Reasoning, and Probability.

• Fluid Mechanics
• Hydraulic Engineering
• Turbomachinery
• +۳ مورد دیگر

Prof. François Avellan is a prominent academic at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences and the Department of Mechanical Engineering. His research focuses on hydraulic machinery, particularly Francis turbines, Pelton turbines, and cavitation phenomena. Key areas include CFD simulations, turbine design optimization, and hydropower system analysis. He leads projects like DuoTurbo (counter-rotating hydroturbines for energy recovery) and investigates fluid-structure interactions in off-design turbine operations. His work bridges experimental fluid mechanics with computational modeling, addressing challenges in renewable energy and grid stability. Education details are not explicitly provided in the text, but his extensive scholarly output indicates a strong academic background in mechanical engineering and fluid dynamics. Collaborations with institutions like the Laboratory of Hydraulic Machines (LMH) and the Swiss Federal Institute of Technology underscore his interdisciplinary involvement. Research interests emphasize cavitation dynamics, turbine instability mechanisms, and hydroacoustic resonance prevention. Recent studies explore part-load resonance risks, vortex rope behavior, and the integration of emerging hydropower technologies. His contributions span both fundamental and applied research, impacting turbine efficiency, energy recovery systems, and sustainable energy solutions. Publications highlight advancements in CFD validation, particle-based methods for erosion prediction, and predictive control of unstable flows. Innovations like the Y-junction hydraulic short-circuit and variable-speed pump-turbine simulations demonstrate practical applications of his research. Ongoing work includes multiscale erosion modeling and strategic hydropower potential assessments for Switzerland. Laboratory affiliations include the Laboratory of Hydraulic Machines (LMH), where experimental facilities support his investigations into turbine dynamics and fluid mechanics. His research directly informs industrial practices in hydropower plant design and operational reliability.

• Robotics
• Haptic Interfaces
• Mobile Robotics
• +۳ مورد دیگر

Hannes Bleuler is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the Laboratory of Robotics Systems (LSRO). His primary email contact is hannes.bleuler@epfl.ch, confirming his active status as a current EPFL member. His research spans multiple disciplines including Robotics, Haptic Interfaces, Mobile Robotics, Field Robotics, Microengineering, and Electrochemistry. Professor Bleuler's work demonstrates strong interdisciplinary connections between mechanical engineering, electrical systems, and human-computer interaction, with particular emphasis on practical applications in medical, industrial, and virtual environments. Analysis of his publication trends shows consistent research activity over two decades, with 206 scholarly works including 32 doctoral theses (suggesting significant student supervision), 45 research articles, and 76 conference papers. His recent work (2020-2023) focuses on advanced haptic feedback systems, energy efficiency in mobile robotics, and novel actuation methods for miniature robotic systems, reflecting ongoing innovation in his field. Professor Bleuler has collaborated extensively with researchers including Rolf Wüthrich, Roger Gassert, Mohamed Bouri, and Francesco Mondada, indicating strong institutional connections within EPFL's robotics community. His research has been published in prestigious venues including Electrochimica Acta, Journal of Micromechanics and Microengineering, and Emerging Trends in Mobile Robotics, demonstrating both theoretical rigor and practical application of his work.

• Process Control
• Real-Time Optimization
• Chemical Engineering
• +۳ مورد دیگر

Dominique Bonvin is a Professor at the School of Engineering (STI) of École Polytechnique Fédérale de Lausanne (EPFL), holding a position within the Automatic Control Laboratory (LA). His research focuses on advanced process control, real-time optimization, and model-based systems engineering. Key topics include modifier adaptation for uncertain systems, dynamic optimization of chemical processes, and control strategies for reaction systems and energy systems. He has published extensively in top-tier journals such as Computers & Chemical Engineering and Journal of Process Control , with over 450 scholarly works to date. His work addresses challenges in industrial processes like wastewater treatment plants, gas compressors, and solid-oxide fuel cells, emphasizing robustness against plant-model mismatches. Collaborations span global institutions, with notable contributions to bioprocess modeling and control of energy-harvesting systems such as kite power. Research Themes: Real-Time Optimization (RTO), Model Predictive Control (MPC), Process Automation, and Systems Engineering Key Technologies: Modifier Adaptation, Dynamic Optimization, Port-Hamiltonian Systems, and Extent-Based Reaction Modeling His recent efforts include accelerating optimization algorithms (e.g., gradient-directed modifier adaptation) and integrating transient measurements for improved plant modeling. He has also pioneered the use of vessel extents to simplify reaction system analysis, enabling decoupled state estimation without full kinetic knowledge.

Roser Vinals Terresمشاهده پروفایل

پژوهشگر

• Signal Processing
• Electrical Engineering

Roser Vinals Terres serves as a Researcher at the Signal Processing Laboratory 5 (LTS5) within the Institute of Electrical Engineering and Microengineering at EPFL's School of Engineering, while concurrently pursuing doctoral studies in the Doctoral Program in Electrical Engineering (EDEE). Based at EPFL's Lausanne campus, her office is located in Building EL (ELD 224), with contact details including email roser.vinalsterres@epfl.ch and phone +41 21 693 89 15. Her research centers on Signal Processing , encompassing theoretical and applied aspects of signal analysis, interpretation, and manipulation. This work operates within the broader discipline of Electrical Engineering , with potential applications spanning telecommunications, biomedical systems, and data science. The LTS5 laboratory specializes in statistical signal processing, machine learning integration, and algorithmic development for real-world signal challenges. As an active member of LTS5, Vinals Terres contributes to the laboratory's research ecosystem through collaboration with faculty, fellow researchers, and students. The laboratory maintains strong industry partnerships and participates in nationally and internationally funded projects, fostering innovation in signal processing theory and its cross-disciplinary applications. Her dual role bridges advanced research execution with doctoral-level academic training within EPFL's engineering framework.

Edy Portmannمشاهده پروفایل

استاد

• Fuzzy Logic
• Cognitive Cities
• Computational Intelligence
• +۱۲ مورد دیگر

Edy Portmann is a Professor at the Department of Informatics, University of Fribourg, where he leads the Resilient Systems Group at the Human-IST Institute and chairs the Mobiliar Cluster for Resilience. He holds a doctorate in fuzzy logic and has extensive experience in both academia and industry, having worked at Swisscom, PwC, and EY, and conducted research at the National University of Singapore, UC Berkeley, and the University of Bern. His research interests include Fuzzy Logic , Cognitive Cities , Computational Intelligence , Digital Ethics , and Human-Computer Interaction . He explores how fuzzy systems can enhance knowledge representation, decision-making, and urban governance, particularly in smart and resilient city contexts. His work integrates digital humanities and connectivist learning theories to bridge the social and semantic web. The recent publications (2022–2025) highlight a strong trend toward ethical AI , explainable systems , public participation , and the application of fuzzy logic in conversational agents , decision support , and urban informatics . He frequently publishes on topics such as digital ethics, emotional chatbots, and systemic consensus in group decision-making. HMD Best Paper Award 2018 He advises several PhD and research students and collaborates extensively with researchers such as Sara D’Onofrio, Patrick Kaltenrieder, and Luis Terán. His work often involves interdisciplinary grants and partnerships with public institutions like Swiss Post. He leads a dynamic research team focused on resilient, human-centered computing and cognitive city technologies.

Anders Karlssonمشاهده پروفایل

دانشیار

• Ergodic theory and random walks
• Metric geometry and group actions
• Spectral invariants
• +۱ مورد دیگر

Anders Karlsson is an Associate Professor at the University of Geneva since 2010 and holds a concurrent professorship at Uppsala University since 2013. His academic journey began with engineering physics studies at KTH, followed by a mathematics PhD from Yale University in 2000, with subsequent positions at ETH Zurich, Neuchâtel, Bielefeld, Yale, and KTH. His research spans multiple mathematical domains with significant interdisciplinary applications: Ergodic theory and random walks Metric geometry and group actions Spectral invariants Deep learning and neural networks Karlsson's theoretical work focuses on noncommuting random products and metric functional analysis, applied to random walks on groups, operator theory, complex variables, stochastic game theory, and machine learning. He also investigates connections between zeta functions of graphs, spaces, and numbers using heat kernel analysis. He leads the Algebra and Geometry research group, mentoring PhD students Kamila Kashaeva and Dylan Mueller, and supervising postdoctoral researcher Tsviqa Lakrec. His teaching portfolio includes various mathematics courses documented in the university database, reflecting his expertise across theoretical and applied mathematics domains.