جست‌وجوی استاد و پژوهشگر

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.

• Signal Processing
• Information Theory
• Multimedia Security
• +۵ مورد دیگر

Svyatoslav Voloshynovskyy is a Professor at the University of Geneva's Faculty of Science, Department of Computer Science, with an impressive publication record of 195 works and supervision of 26 graduate projects. His extensive research portfolio demonstrates significant contributions to signal processing, information theory, and multimedia security, evolving into cutting-edge applications in medical imaging and privacy-preserving technologies. Dr. Voloshynovskyy's research interests span multiple interconnected domains at the intersection of theoretical foundations and practical applications. His work has progressed from foundational research in data hiding and watermarking to contemporary applications in privacy-preserving medical imaging. Key research areas include: Information-theoretic approaches to multimedia security Advanced signal processing techniques for image and data analysis Federated learning frameworks for healthcare applications Privacy-preserving machine learning methodologies Medical imaging analysis with focus on PET/CT technologies Theoretical foundations of data hiding and watermarking Analysis of his recent publications (2022-2024) reveals a strategic shift toward healthcare applications, particularly in medical imaging with privacy preservation. His work integrates federated learning, differential privacy, and deep learning to address critical challenges in healthcare data analysis while maintaining patient confidentiality. This represents a natural evolution from his earlier theoretical work in signal processing and information theory to impactful real-world healthcare applications requiring multi-institutional collaboration. With 26 supervised works documented in the University of Geneva archive, Dr. Voloshynskyy has demonstrated significant commitment to academic mentoring and research supervision. His publication pattern suggests successful grant acquisition for projects at the intersection of signal processing, machine learning, and healthcare applications, though specific funding details aren't provided in the available information. His research trajectory indicates leadership in a specialized research group focused on signal processing and machine learning applications, with recent emphasis on medical imaging. The consistent mention of multi-institutional collaborations in his latest publications suggests an active research team working on federated learning approaches for medical image analysis, likely involving partnerships with healthcare institutions across multiple countries.

Francesco Vaninمشاهده پروفایل

مدرس

• Earthquake Engineering
• Structural Dynamics
• Seismic Assessment
• +۳ مورد دیگر

Francesco Vanin is a Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering. He works within the Civil Engineering department, focusing on earthquake engineering and structural dynamics. His teaching includes courses on structural dynamics and seismic design. Earthquake Engineering and Structural Dynamics Laboratory (EESD), ENAC, IIC SGC-ENS, ENAC-SGC His research centers on seismic behavior of masonry structures, equivalent frame modeling, and retrofitting of historical buildings. He investigates uncertainties in seismic assessment and develops numerical models for structural analysis. Vanin's publications emphasize finite element modeling, FRP strengthening, and experimental validation of masonry structures. His work spans both computational and physical testing methodologies. He supervises PhD student Bozulic Ivana and contributes to education in structural engineering through his teaching roles.

Katrin Beyerمشاهده پروفایل

استاد

• Earthquake Engineering
• Structural Dynamics
• Masonry Structures
• +۵ مورد دیگر

Katrin Beyer is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , leading the Earthquake Engineering and Structural Dynamics Laboratory (EESD) and serving as Dean of the School of Architecture, Civil and Environmental Engineering (ENAC) . She also holds roles as Director of the Structural Engineering Group (GIS-GE) and President of the ENAC School Council. Education: BSc in Civil Engineering (ETH Zurich, 2001) Master’s in Earthquake Engineering (IUSS Pavia, 2003) PhD in Earthquake Engineering (University of Pavia, 2007) Her research focuses on the seismic behavior of unreinforced masonry structures , particularly historical buildings. She combines large-scale experimental testing , numerical modeling , and digital twinning to advance structural assessment methodologies. Recent work explores machine learning for crack detection and upcycling demolition waste into sustainable masonry systems. Her publications reflect trends in: seismic risk mitigation , URM building fragility , image-based structural analysis , and innovative digital twin applications . Beyer has advised over 25 PhD students and contributed to Eurocode 8 revisions. She led the ETH Domain’s CHF 15 million Open Research Data program and co-founded the startup SwissInspect for structural assessment technologies.

Nada Aminمشاهده پروفایل

استادیار

• Programming Language Theory
• Metaprogramming
• Generative Programming
• +۱۱ مورد دیگر

Nada Amin is an Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). She holds a PhD from École Polytechnique Fédérale de Lausanne (EPFL) and degrees from MIT. Her research spans programming language theory, metaprogramming, semantics, and functional programming. Her educational background includes a Doctoral Program in Computer, Communication and Information Sciences from EPFL (2011–2016) and a Master of Engineering and Bachelor of Science from MIT. She has held research positions at TU Darmstadt, University of Cambridge, EPFL, Microsoft Research, and MIT CSAIL. Nada Amin's research focuses on foundational and practical aspects of programming languages. She investigates type systems, staged computation, generative programming, program synthesis, and the semantics of programming languages. Her work often bridges theory and implementation, with applications in verification, compiler construction, and language design. She is particularly known for her contributions to the DOT calculus, collapsing towers of interpreters, and relational programming with miniKanren. Her recent publications reveal a strong trend in advancing programming language foundations, especially in type systems and semantics (e.g., Persimmon, Dolorem, Extensible Metatheory). She integrates functional and logic programming paradigms, explores staged and generative techniques for efficiency and abstraction, and applies these to domains like probabilistic programming and biomedical reasoning. Recipient of the first Michigan Cambridge Research Initiative Grant (with Baris Kasikci, May 2018) 6.170 Letter of Commendation from Prof. Michael Ernst (2005) Peer Bonus at Google for enabling Gmail's CSS compiler for Android Peer Bonus at Google for benefiting numerous projects including Google Apps Nada Amin actively mentors students and collaborates widely in the programming languages community. She has served on numerous program committees (POPL, PLDI, ICFP, OOPSLA, etc.) and organized workshops. Her work is supported by academic and industrial collaborations, and she has contributed to open-source projects. She leads research involving interpreters, compilers, and program transformation systems, often developing tools and frameworks like LMS and Pink.

• Biomedical Imaging
• Applied Functional Analysis
• Sampling Theory
• +۴ مورد دیگر

Michael Unser is a Full Professor at EPFL’s School of Engineering and Academic Director of EPFL’s Center for Imaging in Lausanne, Switzerland. His research focuses on biomedical imaging, applied functional analysis, sampling theory, wavelets, splines, and computational bioimaging. Current Position: Full Professor, EPFL School of Engineering Academic Role: Director of EPFL Center for Imaging Key Research Areas: Inverse problems, sparse stochastic processes, machine learning for imaging Dr. Unser’s research bridges mathematical theory with practical biomedical imaging applications. He has pioneered the use of splines for image processing, developed advanced regularization frameworks for inverse problems, and contributed to computational bioimaging techniques like SMLM and optoacoustic tomography. His recent work explores connections between deep learning and classical kernel methods through variational formulations. He has published over 400 journal papers and authored the seminal book on sparse stochastic processes. His scientific contributions have been recognized with multiple awards including IEEE and EURASIP Technical Achievement Awards, five IEEE-SPS Best Paper Awards, and three consecutive ERC Advanced Grants (FUN-SP, GlobalBioIm, FunLearn). He has served on editorial boards for leading journals and founded the IEEE SPS Bio Imaging and Signal Processing Technical Committee.

Niao Heمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Reinforcement Learning
• +۴ مورد دیگر

Prof. Niao He is an Associate Professor in the Department of Computer Science at ETH Zürich. His research focuses on optimization theory, reinforcement learning, stochastic systems, and their applications in machine learning and multi-agent systems. He holds an academic position at one of the world's leading technical universities, contributing to both theoretical advancements and practical algorithmic solutions. His research interests span optimization theory (e.g., convex/non-convex optimization, stochastic optimization), reinforcement learning (policy gradient methods, multi-agent systems), and statistical learning (risk-averse methods, entropy regularization). He has also explored applications in data science, network revenue management, and deep reinforcement learning for complex systems. Recent work emphasizes algorithmic reproducibility, robustness under model uncertainty, and efficient methods for large-scale problems. Notable contributions include novel approaches to minimax optimization, mean-field games, and adaptive learning frameworks. His articles consistently address theoretical guarantees while maintaining practical relevance for real-world computational challenges.

Volkan Cevherمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Reinforcement Learning
• +۵ مورد دیگر

Volkan Cevher is an Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he leads the Laboratory for Information and Inference Systems (LIONS). He also serves as an Amazon Scholar and previously held positions as a Research Scientist at the University of Maryland (2006-2007) and Rice University (2008-2009), as well as a Faculty Fellow at Rice University (2010-2020). Dr. Cevher received his B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and his Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in 2005. His research spans machine learning, optimization theory, signal processing, and their applications. He focuses on developing theoretically grounded algorithms for robust machine learning, with particular emphasis on non-convex optimization, adversarial training, and distributed learning. His work addresses fundamental challenges in optimization landscapes for neural networks and min-max problems like GANs and robust reinforcement learning. Dr. Cevher's recent publications demonstrate strong trends in optimization for machine learning, with significant contributions to adversarial robustness, federated learning, and efficient training methods. His work bridges theoretical foundations with practical applications across multiple domains including NLP, computer vision, and reinforcement learning. Dr. Cevher has received numerous prestigious awards including: IEEE Fellow (2024) ELLIS fellow ICML AdvML Best Paper Award (2023) Google Faculty Research award (2018) IEEE Signal Processing Society Best Paper Award (2016) ERC Consolidator Grant (2016) ERC Starting Grant (2011) As head of the Laboratory for Information and Inference Systems at EPFL, Dr. Cevher leads a research group focused on advancing the theoretical foundations of machine learning and optimization. His lab has secured significant funding through competitive grants including multiple ERC grants. He actively mentors PhD students and postdoctoral researchers, with many of his advisees going on to successful careers in academia and industry. The Laboratory for Information and Inference Systems (LIONS) at EPFL is a leading research group in optimization and machine learning, known for developing theoretically sound algorithms with practical impact. The lab collaborates extensively with industry partners and other academic institutions worldwide.

Pier Luigi Dragottiمشاهده پروفایل

استاد

• Signal Processing
• Wavelet Theory
• Sampling Theory
• +۷ مورد دیگر

Prof. Pier Luigi Dragotti is a Professor of Signal Processing in the Department of Electrical and Electronic Engineering at Imperial College London, Faculty of Engineering. He leads the Communications, Signal Processing, and Control (CSP) research group and is actively engaged in both theoretical and applied research in signal processing and computational imaging. His research focuses on wavelet theory, sampling theory, sparse signal processing, computational imaging, and data-driven image processing . He integrates classical signal processing frameworks with modern deep learning techniques, particularly in solving inverse problems and image restoration. His work bridges model-based algorithms with data-driven approaches, enabling breakthroughs in fields such as art conservation and neuroscience imaging. The recent publications highlight a strong trend towards invertible neural networks, physics-informed deep learning, and hybrid model-data methods for image restoration, microscopy, and cultural heritage analysis. His team develops algorithms that are not only accurate but also interpretable and grounded in physical models. Scientific Awards and Recognitions: Editor-in-Chief of IEEE Transactions on Signal Processing (2018–2020), Certificate of Merit Supervised students winning: Eurasip 3M Thesis Competition, IEEE MMSP Best Paper Award, Outstanding PhD Thesis Awards (2020–2022), Ivor Tupper Prize Student Adam won Gold at the Commonwealth Games Advising and Grants: Prof. Dragotti has successfully supervised numerous PhD and MSc students, many of whom have gone on to achieve significant recognition. His group has secured funding that supports interdisciplinary research in imaging science, and several of his former students have launched innovative projects, including a start-up company. He fosters a dynamic research environment with strong industry and cross-institutional collaborations. Labs and Research Groups: He is a key member of the CSP (Communications, Signal Processing, and Control) group at Imperial College London, where he leads a vibrant team working on cutting-edge signal processing challenges. The group is known for its contributions to sampling theory, wavelets, and the integration of deep learning in imaging systems.

Savvas Saloustrosمشاهده پروفایل

مدرس

• Structural Engineering
• Earthquake Engineering
• Heritage Structures
• +۳ مورد دیگر

Savvas Saloustros serves as a Lecturer and Ambizione Fellow at École Polytechnique Fédérale de Lausanne (EPFL) within the School of Architecture, Civil and Environmental Engineering (ENAC). He is affiliated with the Institute of Civil Engineering (IIC) and the Earthquake Engineering and Structural Dynamics Laboratory (EESD), while also contributing to the ENAC Diversity Office and teaching unit (SGC-ENS). His academic foundation includes a PhD in Construction Engineering from UPC-BarcelonaTech (2017), an MSc in Structural Analysis of Monuments and Historical Constructions from the University of Padova and UPC-BarcelonaTech (2013), and a Diploma in Civil Engineering from the National Technical University of Athens (2012). Dr. Saloustros specializes in the seismic assessment of historical masonry structures through integrated experimental and computational approaches. His research develops advanced numerical models for crack propagation, material uncertainty quantification, and structural vulnerability analysis in heritage buildings, with emphasis on preserving cultural assets under earthquake loads. Key methodologies include enhanced finite element techniques and stochastic analysis for realistic simulation of structural behavior. His publication portfolio reveals concentrated expertise in masonry structural dynamics, featuring innovations in crack-tracking algorithms, shear characterization, and fragility curve development. Recurring themes address the complex interplay between historical construction techniques, material degradation, and seismic performance, with strong emphasis on practical applications for heritage conservation. He received the International Masonry Society's Outstanding Doctoral Thesis Award in 2018. The Masonry Society Outstanding Doctoral Thesis Award (2018) Dr. Saloustros supervises four PhD candidates while leading an Ambizione-funded research group focused on structural dynamics of built heritage. His work bridges academic research with professional practice through co-founding FENEC (Non-Destructive Testing Network for Heritage Structures) and prior industry experience at Windmill Structural Consultants. He actively contributes to the Earthquake Engineering and Structural Dynamics Laboratory at EPFL, directing research on computational modeling of historical structures and developing next-generation assessment frameworks for seismic resilience of cultural heritage.

Jon Cockayneمشاهده پروفایل

دانشیار

• Bayesian Numerical Methods
• Probabilistic Numerical Methods
• Differential Equations
• +۴ مورد دیگر

Jon Cockayne is an Associate Professor at the University of Southampton specializing in Bayesian Numerical Methods , Probabilistic Numerical Methods , and Uncertainty Quantification . His research focuses on integrating probabilistic approaches into numerical algorithms, particularly for solving linear systems , differential equations , and MCMC output analysis . He has contributed to BayesCG (Bayesian Conjugate Gradient) and Computation-Aware Gaussian Processes . Notable collaborations include work on Statistical Finite Element Methods , Probabilistic Iterative Methods , and Calibrated Numerical Algorithms for industrial applications like Hydrocyclone Equipment state estimation.

Stephen R. Greenمشاهده پروفایل

استاد پژوهشی

• Gravitational Waves
• Black Hole Physics
• Black Hole Perturbation Theory
• +۴ مورد دیگر

Stephen R. Green is a UKRI Future Leaders Fellow and theoretical physicist based at the University of Nottingham’s School of Mathematical Sciences, where his research focuses on gravitational waves, black hole perturbation theory, and machine-learning-enhanced data analysis. Education: PhD in Physics, 2012 – University of Chicago SM in Physical Sciences, 2006 – University of Chicago BSc in Mathematics and Physics, 2005 – University of Toronto Research Interests: Green’s research integrates advanced mathematical techniques with cutting-edge machine-learning methodologies to address fundamental questions in gravitational physics. His primary focus lies in black hole perturbation theory , particularly the nonlinear behavior of perturbations around Kerr black holes, and the development of probabilistic deep-learning frameworks for rapid and reliable inference from gravitational-wave detector data. He is actively engaged in improving waveform models for extreme mass-ratio inspirals detectable by LISA, exploring superradiant instabilities, and understanding spacetime turbulence. Key Scientific Contributions: Across more than two dozen peer-reviewed publications since 2011, Green has advanced our understanding of quasinormal-mode orthogonality in Kerr spacetime, pioneered neural-importance-sampling techniques that accelerate Bayesian parameter estimation for LIGO-Virgo detections, and developed new formalisms for nonlinear metric perturbations sourced by point particles. His 2022 works on conserved currents and neural inference have already garnered significant attention within the gravitational-wave and machine-learning communities. Awards & Funding: UKRI Future Leaders Fellowship (2022 – present) – includes £25,000 per annum research funding. Current & Past Positions: Nottingham Research Fellowship (Nov 2022 – present) – University of Nottingham, School of Mathematical Sciences, UK Senior Scientist (Jul 2021 – Oct 2022) – Max Planck Institute for Gravitational Physics, Potsdam, Germany Postdoc / Junior Scientist (Sep 2017 – Jul 2021) – Max Planck Institute for Gravitational Physics, Potsdam, Germany Postdoctoral Fellow (Sep 2014 – Aug 2017) – Perimeter Institute for Theoretical Physics, Waterloo, Canada CITA National Postdoctoral Fellow (Sep 2012 – Aug 2014) – University of Guelph, Canada Labs, Teams & Collaborations: Green is a member of the LIGO Scientific Collaboration and has collaborated extensively with researchers at the Albert Einstein Institute, Perimeter Institute, and various international partners. While the text does not specify a named lab, his work is closely tied to the gravitational-wave and theoretical-physics groups within the School of Mathematical Sciences at the University of Nottingham.

• Natural Language Processing
• Machine Learning
• Uncertainty Estimation
• +۴ مورد دیگر

Wilker Aziz is an assistant professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning research group. His academic journey includes a PhD from the University of Wolverhampton (2014), followed by research positions at the University of Sheffield and ILLC before joining the UvA faculty in January 2019. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Aziz investigates how to design models and algorithms that learn to represent, understand, and generate language data, with particular emphasis on uncertainty estimation in language models, probabilistic inference techniques, and developing novel decoding algorithms for text generation. His work addresses fundamental problems in language modeling, machine translation, syntactic parsing, textual entailment, text classification, and question answering. Aziz's recent publications (2022-2024) demonstrate a strong focus on uncertainty in natural language generation, with multiple papers accepted at top-tier conferences including EACL and EMNLP. His research shows how language models can better align with human uncertainty patterns and how to properly evaluate model confidence when humans themselves disagree on annotations. Best paper award at Coling 2020 for 'Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation' Outstanding Area Chair award at ICLR 2022 Active program committee member for major ML/NLP conferences including NeurIPS, ICML, ACL, EMNLP, and ICLR Aziz actively supervises PhD and MSc students, with a current cohort of five ongoing PhD candidates and numerous recent graduates. His Probabilistic Language Learning group develops theoretical frameworks and practical applications for uncertainty-aware natural language processing. He has contributed to open-source projects including PET (Post-Editing Tool), grasp (Randomised Semiring Parsing), and mosesdecoder, demonstrating his commitment to advancing the field through shared research tools.

Luiz Chamonمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Constrained Optimization
• +۳ مورد دیگر

Luiz Chamon is an Assistant Professor (tenure-track) and Hi!PARIS Chair holder at the Center for Applied Mathematics (CMAP) , École polytechnique, Paris, France. His research bridges machine learning with control theory and signal processing, focusing on intelligent systems that extract, process, and act on information under constraints. Research Interests: He specializes in constrained learning frameworks for machine learning, with applications to reinforcement learning, statistical signal processing, and optimization. His work includes adversarial robustness, safe policy design, and duality analysis in constrained settings. Article Trends: Recent publications span differential equation solving (ICLR 2025), primal-dual sampling (NeurIPS 2024), and resilient reinforcement learning (IEEE Trans. Autom. Control 2024). Common themes include constrained optimization, robustness, and bridging theory with practical applications in wireless systems and graph signal processing. Scientific Awards: Hi!PARIS Chair (2025) ELLIS Scholar (2025) Best Student Paper Award (ICASSP 2020) Best Paper Award (ICASSP 2020) Top 50 Most Accessed Articles in IEEE TSP (2019) Grants & Projects: Currently leads a 4-year ANR JCJC project on "Beyond (Robust) Learning: Striking Compromises in Machine Learning" (2024). Previously involved in technical reports on aircraft vibroacoustics and wireless power systems. Labs & Teams: Heads a research group at École polytechnique developing tools for constrained learning, collaborating with institutions like University of Pennsylvania and IMPRS-IS. Organizes tutorials and panels on constrained learning and career development.

David Ginsbourgerمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Uncertainty Quantification
• +۷ مورد دیگر

David Ginsbourger is a Professor and Head of Research Group at the Institute of Mathematical Statistics and Actuarial Science (IMSV) within the University of Bern, Switzerland. He maintains dual affiliations through his role at IMSV and as a member of the Multidisciplinary Center for Infectious Diseases (MCID), reflecting interdisciplinary engagement across statistical methodology and applied domains. His research program centers on advanced statistical methodologies with emphases on Gaussian process modeling, uncertainty quantification, and experimental design for computer experiments. Key contributions include novel kernel constructions for equivariant systems, sequential design strategies for excursion set estimation, and efficient computational frameworks for spatial distributional modeling. His work bridges theoretical statistics with practical applications in agriculture, chemoinformatics, environmental science, and risk assessment, demonstrating consistent innovation in handling complex prediction problems under uncertainty. Analysis of his 15 most recent publications (2024-2025) reveals persistent methodological development in Gaussian process theory alongside expanding application domains. Recurring themes include integration-free kernel design for structured data, rare event probability estimation, and multivariate forecast calibration. His research exhibits strong continuity in addressing computational challenges for large-scale inverse problems while increasingly incorporating domain-specific constraints from fields like molecular chemistry and agricultural science. Ginsbourger leads a dedicated research group at IMSV focused on advancing statistical frameworks for computer experiments and uncertainty quantification. The group maintains active collaborations across disciplines, particularly evident in recent work connecting statistical methodology to infectious disease modeling through MCID affiliations and agricultural optimization projects.