جست‌وجوی استاد و پژوهشگر

Julien Fageotمشاهده پروفایل

پژوهشگر

• Sparsity
• Random Processes
• Approximation Theory
• +۷ مورد دیگر

Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL. His educational background includes: Ph.D. in Electrical Engineering at EPFL (2012-2017) M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011) M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009) École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012) Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs). His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis. His scientific achievements have been recognized with: Best Paper Award at the MIDL Conference (2019) EPFL Best Doctorate Award (2018) Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017) Education Award from the Life Science Department, EPFL (2013) Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018). As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.

• Seismology
• Ambient Seismic Noise
• Seismic Interferometry
• +۶ مورد دیگر

Dr. Anne Christine Obermann is a Senior Researcher and Lecturer at the Swiss Seismological Service (SED), ETH Zurich. She holds a PhD in seismology from ISTerre (Grenoble, France), completed in 2013 under Professors Michel Campillo and Eric Larose. Her research focuses on seismic monitoring techniques, including ambient noise interferometry and coda wave analysis, with applications to volcanic eruptions, earthquake precursors, and crustal deformation studies. She has applied these methods to case studies such as Piton de la Fournaise volcano in Réunion Island and the 2008 Wenchuan earthquake in China. Education: PhD in Seismology (ISTerre, Grenoble, 2013) Key Techniques: Probabilistic inversion for crustal deformation localization, numerical modeling of coda wave sensitivity Her work bridges numerical analysis and field applications, addressing challenges in resolving subsurface changes linked to hazardous events. Recent studies include 3D sensitivity kernel implementations for concrete fracturing analysis under tension.

Nina Effenbergerمشاهده پروفایل

پژوهشگر

• Climate Science
• Machine Learning
• Renewable Energy
• +۳ مورد دیگر

Dr. Nina Effenberger is a Postdoctoral Researcher in the Climate Physics group at ETH Zurich, led by Professor Reto Knutti. She previously completed her Ph.D. at the University of Tübingen under Nicole Ludwig’s Machine Learning in Sustainable Energy Systems group and was affiliated with the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Her work bridges climate science, machine learning, and energy modeling to address challenges in renewable energy systems under climate change. Her research focuses on multi-decadal wind power forecasting using climate models (CMIP6) and Gaussian Processes, with a particular emphasis on turbine-location-aware downscaling techniques. She has also contributed to mental health advocacy in academia, including co-authoring a 2023 publication in Plos One on stress and well-being among Ph.D. students. Her recent work includes arXiv preprints on climate data selection and temporal resolution impacts in wind power predictions. Dr. Effenberger is an alumnus of the sustainAbility Ph.D. initiative and trained in mental health first aid. She can be contacted via email at nina.effenberger@env.ethz.ch .

Scott Keatingمشاهده پروفایل

مدرس

• Seismic Inversion Methodology
• Uncertainty Quantification
• Numerical Optimization
• +۴ مورد دیگر

Dr. Scott Keating is a Lecturer at ETH Zurich’s Department of Earth and Planetary Sciences (D-EAPS), affiliated with the Institute of Geophysics. His research focuses on seismic inverse problem methodologies, particularly uncertainty quantification, numerical optimization, and the development of automated workflows for regional inversion updates. Current projects include full-waveform inversion of ambient noise measurements and CO2 storage monitoring using advanced sensor technologies like distributed acoustic sensing (DAS) and accelerometers. His work integrates cutting-edge computational methods, such as probabilistic inversion frameworks and adjoint-based optimization, to address challenges in subsurface imaging and parameter estimation. Applications span environmental seismology, carbon sequestration, and reservoir characterization, with a strong emphasis on practical, cost-effective solutions for real-world problems such as CO2 storage validation. Publications highlight contributions to elastic full-waveform inversion (FWI), including multiparameter analysis using combined geophone and DAS data, and innovative techniques like targeted nullspace shuttling to enhance inversion reliability. His research also addresses data sparsity and modeling uncertainties, advancing methodologies for robust subsurface monitoring and decision-making.

Alcherio Martinoliمشاهده پروفایل

استاد

• Autonomous Robotics
• Distributed Robotics
• Swarm Robotics
• +۸ مورد دیگر

Alcherio Martinoli is a Full Professor at the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL), leading the Distributed Intelligent Systems and Algorithms Laboratory (DISAL). He holds additional roles as Director of the ENAC School’s SSIE-GE department and is involved in administrative committees such as the ENAC School Direction and CDS. His expertise spans autonomous robotics, distributed systems, and environmental monitoring. Education: He earned his Diploma in Electrical Engineering from ETH Zurich and a Ph.D. in Computer Science from EPFL. His research focuses on designing, controlling, and optimizing distributed systems, including multi-robot teams and sensor networks, with applications in environmental and civil engineering. Research Interests: His work integrates theoretical models with physical experiments, emphasizing mixed societies of natural and artificial systems. Key areas include swarm robotics, intelligent vehicles, and fluid-mediated self-assembly. He emphasizes closing the theory-experiment loop through computational methods. Publications: His recent work addresses multi-robot system architectures, gas sensing with nano aerial vehicles, and distributed control algorithms. These contributions advance environmental monitoring, autonomous navigation, and swarm coordination. Awards: Recognitions include the Best Teacher Award (2016), Best Poster Award (2016), and SNSF Professorships (2003, 2007). Grants & Advising: Supervises numerous PhD students and has advised over 20 graduates. His grants include projects on distributed robotics and environmental systems, though specific grant details are not explicitly listed in the provided texts. Labs & Teams: Directs the DISAL lab, fostering interdisciplinary research in intelligent systems and their applications. Collaborates on projects involving multi-robot coordination and autonomous systems.

• Control Systems
• Optimization
• Machine Learning
• +۵ مورد دیگر

Prof. Maryam Kamgarpour is a faculty member at ETH Zurich's Automatic Control Laboratory within the Department of Information Technology and Electrical Engineering. Her research focuses on control systems, optimization, and machine learning, with applications in robotics, power systems, and multi-agent systems. She has contributed significantly to safe reinforcement learning, stochastic control, and distributed optimization frameworks. Her work bridges theoretical foundations and practical implementations in areas like safe trajectory planning, market mechanisms for energy systems, and algorithmic guarantees for greedy methods. Key technical areas include model-based multi-agent reinforcement learning, robust control policies under uncertainty, and parameterization techniques in control theory. Collaborations with institutions like ETH Zurich's Automatic Control Lab and researchers such as Andreas Krause highlight her interdisciplinary approach. Her research has been supported by grants from the Swiss National Science Foundation (NCCR Automation) and the European Union (Reliable Data-Driven Decision Making in Cyber-Physical Systems). Publications span top journals and conferences in control systems, machine learning, and robotics, emphasizing rigorous analysis and practical scalability. Recent work addresses challenges in safe black-box optimization, stochastic hazard management in robotics, and market-based resource allocation in power grids.

Andrea Carronمشاهده پروفایل

مدرس ارشد

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +۷ مورد دیگر

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

Kevin J Liangمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• 3D Computer Vision
• +۵ مورد دیگر

Dr. Kevin J Liang is a Research Scientist at Meta Platforms, Inc. , specializing in Deep Learning , Computer Vision , and 3D Reconstruction . He earned his PhD in Electrical & Computer Engineering from Duke University in 2020, with a dissertation on Deep Automatic Threat Recognition for Airport X-Ray Baggage Screening . His research focuses include: 3D Computer Vision (ICON, Fast3R) Few-Shot Learning (Sylph, HyperMix) Federated Learning (WAFFLe) Object Detection (EgoTracks, Self-Supervised Methods) Recent publications demonstrate his leadership in Egocentric Vision (Ego-Exo4D) and Transformer Applications (GliTr). He has received numerous awards including the E Bayard Halsted Fellowship (2017) and Summa cum laude (2015), and serves on program committees for major conferences like NeurIPS and CVPR . As an educator, he developed and taught tutorials for Duke University's Machine Learning School and Coursera courses, covering TensorFlow, PyTorch, and foundational ML concepts for over 600 students.

Annemarie Friedrichمشاهده پروفایل

استاد

• Natural Language Processing
• Computational Linguistics
• Computational Syntax
• +۷ مورد دیگر

Annemarie Friedrich is a tenured University Professor for Natural Language Understanding (Computational Linguistics) at the Faculty of Applied Computer Science, University of Augsburg. She also holds membership in the Faculty of Philology and History. Previously, she worked as a Senior Expert on Natural Language Processing and Computational Linguistics at the Bosch Center for Artificial Intelligence. Currently, she serves as president of the German Society for Computational Linguistics (GSCL), the primary scientific association for NLP research in German-speaking regions, and is a member of the ACL Special Interest Group for Annotation (ACL SIGANN). University: University of Augsburg School: Faculty of Applied Computer Science Department: Institute of Computer Science Position: University Professor (tenured) for Natural Language Understanding Professor Friedrich's research focuses on computational linguistics and natural language processing with emphasis on semantics and information extraction from text. Her work spans both machine-learning oriented approaches to text mining for scientific text, syntactic and semantic parsing, and uncertainty in deep learning for NLP, as well as corpus-linguistic research on syntax-semantics interface, discourse, pragmatics, aspect, genericity, and modal verbs. She has particular expertise in annotation and corpus creation, recognizing that machine learning models depend fundamentally on underlying data quality. Her research group at Augsburg actively contributes to computational linguistics through numerous publications and datasets. Analysis of Professor Friedrich's recent publications reveals a strong focus on table question answering, patent text processing, uncertainty modeling, and multimodal scientific document understanding. Her work consistently bridges theoretical linguistics with practical NLP applications, with increasing emphasis on robust evaluation methodologies and domain-specific adaptations of language models. Notably, her research group has produced significant resources including the AnnoCTR dataset for cyber threat reports, PAP2PAT for patent generation, and FREB-TQA for evaluating table QA robustness. Professor Friedrich actively mentors multiple PhD students working on diverse topics including document-level patent processing (Valentin Knappich), temporal processing (Timo Schrader), document-level text modeling (Wei Zhou), and topic modeling for digital forensics (Jenny Maria Felser). Her collaborative approach is evident in co-supervision arrangements with researchers from institutions including Bosch Center for Artificial Intelligence, TU Dresden, and Hochschule Mittweida. She has also successfully guided previous PhD students including Sophie Henning (Uncertainty Modeling), Stefan Grünewald (Syntactic Dependencies), and Subhash Pujari Chandra (Neural Patent Classification). Her teaching responsibilities include courses such as Introduction to Natural Language Processing, Introduction to Python Programming, and specialized seminars on Natural Language Understanding for both Bachelor's and Master's students. These courses reflect her commitment to both theoretical foundations and practical implementation skills in computational linguistics.

Anastasiia Shynkarenkoمشاهده پروفایل

پژوهشگر

• Seismology
• Geotechnical Engineering
• Natural Hazards
• +۳ مورد دیگر

Dr. Anastasiia Shynkarenko is a Researcher at the Swiss Seismological Service (SED) within ETH Zürich's School of Architecture, Civil and Environmental Engineering. Her work focuses on seismic risk assessment, subaqueous slope stability, and lake tsunami hazards in Swiss perialpine lakes. She specializes in integrating geophysical and geotechnical methods to investigate sediment properties and failure mechanisms using advanced techniques like Ocean Bottom Seismometers (OBS), ambient vibration analysis, and in-situ testing (CPTu). Key projects include the comprehensive study of Lake Lucerne's submerged slopes, addressing slope stability through interdisciplinary approaches. Her research combines seismological data inversion, spectral analysis, and numerical modeling to evaluate subsurface structures and potential hazards. Recent contributions include developing dynamic earthquake risk frameworks and advancing methodologies for extracting phase velocities from OBS arrays to characterize sedimentary sites. While no formal student advising or grant information is explicitly stated, her work has been published in leading geophysics and engineering journals. Collaborations involve both academic and governmental entities in Switzerland focused on environmental and geohazard mitigation strategies.

Christos Dimitrakakisمشاهده پروفایل

استاد

• Reinforcement Learning
• Reverse Reinforcement Learning
• Preference Elicitation
• +۱۱ مورد دیگر

Christos Dimitrakakis is a Full Professor of Data Science at the University of Neuchâtel, Switzerland, where he is affiliated with the Institute of Computer Science within the Faculty of Science. His research focuses on artificial intelligence, particularly reinforcement learning, fairness, and algorithmic privacy. He has held academic positions at several prestigious institutions including the University of Oslo, Chalmers University of Technology, and the University of Lille. His educational background includes: 1997: BEng from the University of Manchester 1998: MSc from the University of Essex 2006: PhD from IDIAP/EPFL under the supervision of Samy Bengio Professor Dimitrakakis specializes in reinforcement learning and decision making under uncertainty, with particular expertise in reverse reinforcement learning, preference elicitation, and human-AI collaboration. His research bridges theoretical foundations with practical applications, especially in autonomous systems and privacy-preserving AI. He has made significant contributions to understanding representations of uncertainty, differential privacy, and group fairness in algorithmic decision-making. His work often combines Bayesian inference with reinforcement learning frameworks to develop robust and fair AI systems that can operate effectively in uncertain environments. His recent publications demonstrate a strong focus on theoretical foundations of reinforcement learning while addressing practical challenges in fairness, privacy, and decision making. The research shows an evolving trajectory from core reinforcement learning techniques toward applications in fair and transparent AI systems, with increasing emphasis on real-world constraints and ethical considerations. His work spans multiple domains including game theory, matching markets, and bandit algorithms, reflecting an interdisciplinary approach to AI research. Notable scientific achievements include: Best Paper Award at Cooperative-AI@NeurIPS 2021 for "Interactive Inverse Reinforcement Learning for Cooperative Games" Student paper award at EAAMO'22 for "On Meritocracy in Optimal Set Selection" Professor Dimitrakakis actively mentors PhD students and has supervised numerous researchers who have gone on to academic and industry positions. His current research group at the University of Neuchâtel includes five PhD students working on reinforcement learning, fairness, and uncertainty in AI systems. He has secured funding for projects including a national initiative to develop intelligent bird deterrent systems for crop protection, demonstrating the practical impact of his research. His group collaborates with industry partners such as Agroscope, and he maintains academic connections with institutions worldwide. His research group at the University of Neuchâtel is actively working on interdisciplinary projects, most notably the development of an "intelligent scarecrow" system for bird pest control in agriculture. This project combines computer vision, reinforcement learning, and behavioral modeling to create adaptive AI systems that can minimize crop damage while accounting for bird behavior patterns. The group maintains expertise in both theoretical foundations and practical implementations of AI systems.

Federico Wadehnمشاهده پروفایل

مدرس ارشد

• Digital Signal Processing
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Dr. Federico Wadehn is a Senior Lecturer at the Institute of Signal Processing and Wireless Communications within the School of Engineering at Zurich University of Applied Sciences (ZHAW), specializing in digital signal/image processing and machine learning applications for biomedical and engineering systems. His role includes teaching core courses and supervising graduate theses. Education: PhD in Statistical Signal Processing with Probabilistic Graphical Models, ETH Zürich (2014-2018) Teaching Certificate, ETH Zürich and ZHAW (2017-2018) Visiting Researcher in Neuromonitoring, MIT (2016) MSc/BSc in Electrical Engineering and Information Technology, ETH Zürich (2009-2014) Erasmus Exchange in Control Engineering, Imperial College London (2012-2013) Continuing Education: MATLAB C Coder (2023), Embedded C (2021), IBM Rational Rhapsody (2020) Research Interests: His work bridges Digital Signal Processing (classical, ML, and deep learning), Computer Vision , Sensor Fusion/Kalman Filtering for inertial systems, Probabilistic Graphical Models for navigation, and Physiological System Modeling (respiratory/cerebrovascular). He emphasizes model-based approaches for real-world biomedical applications, particularly in neuromonitoring and respiratory care. Publication Trends: His 2015-2020 publications reveal a consistent focus on advanced signal processing for physiological monitoring, featuring model-based separation of biological signals (eye movements, blood flow), sparse state-space modeling, and Kalman smoothing innovations. The work integrates probabilistic graphical models with biomedical constraints, demonstrating strong cross-disciplinary impact in IEEE journals and conferences. Scientific Awards: Study and doctoral scholarship from German Academic Scholarship Foundation (2014) Abiturpreis Physik from Deutsche Physikalische Gesellschaft (2008) Advising and Professional Activities: He supervises bachelor's/master's theses at ZHAW while maintaining industry connections through prior roles as Systems Engineer at Bosch Sensortec (2021-2023), Research Engineer at Hamilton Medical AG (2019-2021), and Computer Vision Freelancer (2021). His dual academic-industry experience informs practical curriculum development. Laboratories and Teams: He contributes to ZHAW's Digital Health Lab and Medical Systems Platform, focusing on translating signal processing innovations into clinical tools for respiratory monitoring and motion analysis, with recent patent work in ventilation technology.

Martin Zachمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Imaging
• Inverse Problems
• MRI Reconstruction
• +۲ مورد دیگر

Martin Zach is a postdoctoral researcher at the Center for Biomedical Imaging (CIBM), EPFL , specializing in inverse problems in biomedical imaging. He joined the Mathematical Imaging Section under the supervision of Prof. Michael Unser in September 2024. PhD : Graz University of Technology (2024), advised by Thomas Pock Martin’s research bridges model-based reconstructions and data-driven approaches in imaging, with a focus on MRI reconstruction and diffusion models . His work explores regularization techniques, energy-based priors, and probabilistic modeling. Recent publications highlight advancements in Gaussian mixture models , non-linear inversion , and Langevin sampling for biomedical imaging. His Google Scholar profile reveals a strong focus on inverse problems (2020–2025), with applications in MRI , CT , and quantitative phase imaging . Key methodologies include diffusion models , generative priors , and regularization algorithms , spanning disciplines from machine learning to computational biology . Martin’s current role at EPFL involves collaborative research in mathematical imaging , with affiliations to the BioMedical Imaging (BIG) Group in Lausanne, Switzerland.

• Structural Engineering
• Timber Engineering
• Civil Engineering
• +۳ مورد دیگر

Gerhard Fink serves as a Lecturer at the Institute of Structural Engineering (IBK) within the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich. His research focuses on timber structural systems, with a particular emphasis on glued laminated timber (GLT) beams and connections. Key areas include material variability, failure behavior analysis, and probabilistic design methodologies. Expertise: Structural reliability, timber mechanics, failure analysis, and engineering design. Key Projects: Large-scale bending tests on GLT beams, quasi-brittle fracture studies, and safety format assessments for timber connections. Publications emphasize experimental validation of structural models and probabilistic approaches to enhance timber engineering standards. Collaborates closely with institutions like Aalto University and the International Network on Timber Engineering Research (INTER).

Hans-Andrea Loeligerمشاهده پروفایل

استاد

• Signal Processing
• Information Theory
• Communications
• +۶ مورد دیگر

Prof. Hans-Andrea Loeliger is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and serves as Deputy Head of the Signal and Information Processing Laboratory. With a career spanning over two decades at ETH Zurich since 2000, he has established himself as a leading researcher in signal processing, information theory, and related fields. His work bridges theoretical foundations with practical applications in communications, electronics, and machine learning. Loeliger's research interests encompass a broad spectrum of topics including signal processing, information theory, communications, system theory, electronics, machine learning, quantum systems, error correcting codes, and neural computation. His work on factor graphs and message passing algorithms has been particularly influential, providing a unifying framework for various signal processing techniques. His recent publications demonstrate continued innovation in areas such as NUV priors, control-bounded analog-to-digital conversion, and neural network applications. His publication record shows consistent high-impact contributions across multiple disciplines, with recent work focusing on the intersection of statistical signal processing, machine learning, and circuit design. The trend in his research demonstrates an evolution from foundational work in factor graphs and information theory toward increasingly sophisticated applications in machine learning, neural computation, and practical circuit implementations. Fellow of the IEEE Loeliger has supervised numerous PhD students and master's candidates through ETH Zurich's Signal and Information Processing Lab, though specific student names aren't listed in the provided text. His teaching responsibilities include courses such as Discrete-Time and Statistical Signal Processing, Electronic Circuits & Signals Exploration Laboratory, and Introduction to Estimation and Machine Learning. His research has been supported by various grants enabling the development of novel signal processing techniques and their implementation in practical systems. The Signal and Information Processing Laboratory under his leadership serves as a hub for interdisciplinary research connecting theoretical signal processing with applications in communications, imaging, and neural systems. The lab maintains strong connections with both academic and industrial partners, facilitating the translation of theoretical advances into practical implementations.