جستجوی استاد راهنمای دانشگاهی

• Wave Physics
• Computational Seismology
• Earthquake Seismology
• +۴ مورد دیگر

Dr. Maria Koroni is a senior researcher at the Swiss Seismological Service (SED) within ETH Zürich's Department of Earth Sciences. She specializes in computational seismology, focusing on physics-based numerical modeling of ground motion and hybrid methodologies combining deterministic and stochastic approaches. Her work directly supports seismic hazard assessment for Swiss nuclear facilities through ENSI-funded projects. Education: PhD in Computational Seismology (2018), Utrecht University MSc in Geophysics (2014), Utrecht University BSc in Earth Science (2011), Aristotle University of Thessaloniki Research Interests: Wave physics, earthquake seismology, full-waveform inversion, and high-performance computing. Her current projects involve modular numerical meshes for high-fidelity seismic simulations using 3D velocity models and integrating machine learning into seismic waveform modeling. Publications reflect a strong focus on: Core-mantle boundary studies using adjoint methods Alpine valley ground motion modeling Gender representation in seismology authorship 3D basin structure analysis in Swiss geological contexts Grants include ERC Horizon 2020 funding (2018-2021) and ongoing ENSI support. She teaches Engineering Seismology at ETH Zürich and collaborates with the Institute of Geophysics on advanced seismic instrumentation projects.

Patrick Thiranمشاهده پروفایل

استاد

• Network Science
• Wireless Networks
• Machine Learning
• +۳ مورد دیگر

Patrick Thiran is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, affiliated with the School of Computer and Communication Sciences and the Department of Computer Science. His research focuses on network science, wireless networks, machine learning, and complex systems. Laboratoire de la dynamique de l'information et des réseaux Chair in Communication Systems Teaching: Modèles stochastiques pour les communications, Networks out of control His work explores the fundamental properties of wireless networks , source localization in large-scale networks , and network tomography . Recent publications demonstrate expertise in graph neural networks, epidemic modeling, and stochastic optimization. His 15 most recent publications show consistent contributions to network science, machine learning, and wireless communications, with a focus on graph algorithms , source localization , and network dynamics . Key subfields include metric dimension , community detection , and Bayesian optimization . He has advised numerous PhD students including Elahi Sepehr, Fua Raphaël Andrew, and Kuroda Daichi, reflecting his significant contributions to graduate education and research mentorship.

• Decentralized Systems
• Distributed Systems
• Privacy and Anonymous Communication
• +۷ مورد دیگر

Prof. Bryan Alexander Ford is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Decentralized and Distributed Systems (DEDIS) lab within the School of Computer and Communication Sciences, Department of Computer Science. He also holds teaching positions in SIN (Systems and Networking) and SSC (Security and Software Composition) at EPFL, and serves on the Open Science Strategic Committee. His academic journey includes faculty positions at Yale University following his Ph.D. at MIT. Dr. Ford's research spans multiple domains with a primary focus on building secure decentralized systems. His work encompasses privacy and anonymous communication systems like Dissent, systems security, blockchain technology, and novel approaches to operating systems for deterministic parallel computing such as Determinator. He has made seminal contributions to parsing theory through his development of Parsing Expression Grammars (PEGs) and packrat parsing algorithms, which provide linear-time parsing with backtracking capabilities. His research also extends to networking protocols including the Unmanaged Internet Architecture and Structured Stream Transport, as well as virtualization technologies like VX32. Ford's publication record demonstrates a remarkable evolution from foundational work in parsing and programming languages to cutting-edge research in decentralized systems and security. His early career focused on operating systems theory, parsing algorithms, and language design, culminating in influential papers on packrat parsing and PEGs. More recently, his work has shifted toward practical decentralized systems, blockchain technology, and security architectures, while maintaining connections to programming language theory through projects like Matchertext and MinML. This trajectory reflects both continuity in his interest in system architecture and a strategic pivot toward emerging challenges in decentralized computing. As an educator and mentor, Ford advises multiple PhD students at EPFL through the EDIC program and welcomes prospective students and researchers to join his lab. His teaching includes courses on decentralized systems engineering and technologies for democratic society, reflecting his commitment to both technical rigor and societal impact of computing technologies.

Weronika Potok-Szybinskaمشاهده پروفایل

مدرس

• Neuroscience
• Neurophysiology
• Transcranial Stimulation
• +۴ مورد دیگر

Dr. Weronika Potok-Szybinska is a Lecturer at the Department of Health Sciences and Technology at ETH Zürich. Her work focuses on neurophysiological mechanisms of sensory and motor systems, with particular emphasis on the effects of non-invasive brain stimulation techniques such as transcranial random noise stimulation (tRNS) and transcranial alternating current stimulation (tACS). She investigates how these interventions modulate visual contrast sensitivity, motor cortex responsiveness, and neural plasticity across various brain regions. Her research integrates neuroimaging (e.g., fMRI), electrophysiological recordings, and behavioral assessments to explore topics like handedness-related brain lateralization, praxis-language network interactions, and pandemic-era guidelines for clinical neurostimulation safety. Key areas of expertise include transcranial stimulation protocols, sensory-motor integration, and neuroplasticity mechanisms. Research Themes: Transcranial Stimulation Effects, Neuroplasticity, Visual-Motor Systems, Cerebral Lateralization Methodologies: fMRI, TMS/tES, Electrophysiology, Behavioral Testing Recent publications highlight advancements in understanding how tRNS acutely lowers motor circuit thresholds, enhances visual contrast detection, and interacts with brain regions such as the primary visual cortex and supramarginal gyrus. Collaborative projects include exploring the neural basis of manual praxis and language production networks in left-handed individuals.

• Mechanical Engineering
• Aerospace Engineering
• Systems Engineering
• +۳ مورد دیگر

Gerhard Stefan Székely is a Lecturer and Head of the Bachelor's Program in Mechanical Engineering at the Lucerne School of Engineering and Architecture (HSLU). He specializes in mechanical systems for space research, systems engineering, and innovative teaching methods like flipped classrooms and blended learning. His industry experience includes 16 years at RUAG Space Zurich, leading R&D in FEM analysis for satellite mechanisms and systems engineering for projects like GAIA BRM and ALADIN instruments. Education: TU München: Mechanical Engineering (Aerospace focus) PhD from Universität Innsbruck: Stochastic structural mechanics for ESA projects Research Focus: Space mechanical systems, systems engineering methodologies, and educational innovation in STEM. Notable projects include the VenSpec-H spectrometer for ESA's EnVision mission and the COW experiment studying cartilage in microgravity. Professional Contributions: Authored over 20 peer-reviewed publications and technical reports on space mechanisms, structural reliability, and educational methodologies. Leads HSLU's contributions to national MINT education networks.

Romain Jacobمشاهده پروفایل

مدرس

• Computer Networks
• Real-Time Systems
• Wireless Communication
• +۵ مورد دیگر

Romain Jacob is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich, where he also holds a postdoctoral researcher position in Prof. Laurent Vanbever's group. His research focuses on computer networks, real-time systems, and sustainable networking, with an emphasis on reproducible experimental design and open science advocacy. He completed his PhD under Prof. Lothar Thiele at ETH Zürich in 2019, followed by a visiting scholar position at UC Berkeley in 2014. His academic background includes a Master of Science in Engineering of Complex Systems from École Normale Supérieure de Cachan (now ENS Paris-Saclay). Key research interests include: Time-triggered wireless architectures for cyber-physical systems Energy-efficient networking and sustainability Reproducibility frameworks like TriScale Real-time communication protocols Notable contributions include the Best Paper Award at HotCarbon'24 for work on router energy optimization. He actively promotes open science through initiatives like the ORCID Researcher Advisory Council and serves as Editor-in-Chief for JSys. His teaching includes a master's lecture on Sustainable Networking, emphasizing practical applications and open educational resources. He collaborates with the Swiss Reproducibility Network (SwissRN) to advance rigorous research practices.

Maximilian Probstمشاهده پروفایل

مدرس

• Graph Algorithms
• Convex Optimization
• Dynamic Algorithms
• +۳ مورد دیگر

Maximilian Probst is a Lecturer at ETH Zürich's Department of Computer Science, affiliated with Rasmus Kyng's Algorithms and Optimization Group. He holds a Ph.D. from the University of Copenhagen (2020) and has conducted research at MIT. His research focuses on graph algorithms, dynamic networks, and optimization, with notable contributions to expander decomposition and network flow algorithms. Probst has received prestigious awards including the 2023 Frontiers of Science Award and the 2022 FOCS Best Paper Award. Education: Ph.D. in Computer Science (2020), University of Copenhagen; M.Sc. in Computer Science, University of Copenhagen. Supervisors: Christian Wulff-Nilsen and Mikkel Thorup. Research Interests: Graph Algorithms (Dynamic, Expander-based), Network Flows, Convex Optimization, and Algorithm Design. His work emphasizes theoretical guarantees for real-world graph problems, particularly in dynamic environments. Publications: Probst's work spans foundational algorithmic breakthroughs, including nearly-linear-time maximum flow algorithms and dynamic graph sparsification techniques. His research bridges theory and practice, with applications in routing, congestion balancing, and expander decomposition. Awards: Frontiers of Science Award (2023) FOCS Best Paper (2022) ESA Best Student Paper (2018) Teaching: Co-instructor for courses like 'Advanced Graph Algorithms and Optimization' and 'Advanced Algorithms' at ETH Zurich. Lab/Team: Member of the Algorithms and Optimization Group, collaborating closely with Rasmus Kyng and leading Ph.D. students in theoretical computer science.

• Machine Learning
• Smart Buildings
• Energy Efficiency
• +۳ مورد دیگر

Frédéric Montet is a Researcher and Doctoral Student at the Fribourg School of Engineering and Architecture (HES-SO), part of the iCoSys Institute for Complex Systems. His primary role involves leading and contributing to projects focused on smart building technologies, energy efficiency, and machine learning applications. He is the Principal Applicant of the ongoing FACILITY 4.0 project (2019-2021), which develops data-driven solutions for building management and facility optimization using AI and IoT technologies. Research interests include: Machine Learning for energy systems, predictive modeling in smart buildings, radon gas monitoring infrastructure, and integration of large language models into control systems. His work bridges theoretical data science with practical applications in renewable energy management and industrial automation. Key contributions include the BBData 2.0 platform for smart building data integration, predictive domestic hot water temperature modeling in district heating systems, and benchmarking zero-shot time series forecasting models. His projects often involve collaboration with industry partners to co-create scalable ICT solutions. Current focus areas: Optimizing PV installations at grid level, predictive maintenance systems, and ethical challenges in LLM-based control systems for shared appliances.