جست‌وجوی استاد و پژوهشگر

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +۶ مورد دیگر

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Wenzel Jakobمشاهده پروفایل

دانشیار

• Inverse Graphics
• Differentiable Rendering
• Physically Based Rendering
• +۷ مورد دیگر

Wenzel Jakob is an Associate Professor and leader of the Realistic Graphics Lab at EPFL's School of Computer and Communication Sciences , currently on sabbatical at the University of Tokyo until Fall 2025. His work bridges inverse graphics , physically based rendering , and compiler/systems research , with a focus on developing robust differentiable rendering frameworks. Key research themes include: Backpropagation through rendering algorithms for inverse problems Material appearance modeling and optical measurement systems Compiler design for differentiable rendering pipelines Manifold sampling techniques and light transport derivatives His group created Mitsuba renderer , Dr.Jit , and Instant Meshes (recipient of the SGP Software Award). Recent publications (2021–2024) explore volumetric rendering, SDF-based differentiable systems, and efficient Monte Carlo estimators. Awards include the ACM SIGGRAPH Significant Researcher Award , Eurographics Young Researcher Award , and ERC Starting Grant . Teaching roles (2016–2024) span Advanced Computer Graphics and Numerical Methods for Visual Computing courses at EPFL.

Patrick Jennyمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Turbulent Reactive Flows
• Multiphase Flow Modeling
• +۳ مورد دیگر

Prof. Patrick Jenny is a Full Professor at the Department of Mechanical and Process Engineering and Head of the Institute of Fluid Dynamics at ETH Zurich. His research focuses on computational fluid dynamics (CFD), numerical methods for turbulent and multiphase flows, and reservoir simulation. He has held positions at ChevronTexaco and Cornell University, and received the National Latsis Prize 2005. PhD in CFD from ETH Zurich (1997) Postdoctoral work at Cornell University (1997–1999) Senior Researcher at ChevronTexaco (1999–2003) Research interests include: turbulent reactive flows, PDF modeling, multi-scale reservoir simulation, and data assimilation in engineering systems. He teaches courses on fluid dynamics, turbulence, and computational methods. Over 100 peer-reviewed publications span topics like fracture modeling, LES/RANS coupling, and particle-laden flows. His work bridges academia and industry, addressing challenges in energy systems, environmental engineering, and numerical algorithms. Winner: National Latsis Prize 2005 Led over 20 PhD projects and collaborates with institutions globally. His lab develops open-source tools for CFD and energy systems analysis.

Shayan Aziznejadمشاهده پروفایل

پژوهشگر

• Machine Learning
• Acoustic Imaging
• Neural Networks
• +۳ مورد دیگر

Shayan Aziznejad is a Senior ML Scientist at Distran, working on the intersection of machine learning and acoustic imaging. He was previously an ML researcher at Daedalean AI (October 2022–December 2024) and a Ph.D. candidate at Ecole Polytechnique Fédérale de Lausanne (EPFL) , where he focused on mathematical optimization and signal processing under Prof. Michael Unser. His academic background includes dual B.Sc. degrees in Electrical Engineering and Pure Mathematics from Sharif University of Technology . Research Focus: Machine learning, neural network certification, wavelet analysis, Hessian-Schatten regularization, and sparse modeling. Scientific Recognition: Swiss National Science Foundation Postdoc Fellowship (2021) Best Student Paper Award at ICASSP (2019) Gold Medalist at Iranian National Mathematics Olympiad (2011) Academic Contributions: Authored 15+ publications in top-tier journals (SIAM, IEEE, etc.) and conferences (ICASSP, EUSIPCO), with a focus on Lipschitz-regularized models, spline-based optimization, and inverse problems. Advising Experience: Supervised 11+ students across master's theses, summer internships, and semester projects, including Eliana Renzo, Joaquim Campos, and Haojun Zhu. Email: shayan.aziznejad@gmail.com

Xuming Heمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

• Applied Analysis
• Numerical Analysis
• Data Science
• +۶ مورد دیگر

Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.

• Environmental Science
• Computational Science
• Biogeochemistry
• +۳ مورد دیگر

Jonathan Sauder is a Researcher and Doctoral Assistant at EPFL, affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Institute of Infrastructure and Environment (IIE). He is part of two laboratories: the Computational Science Laboratory for Environment and Earth Observation (ECEO) in Sion and the Biogeochemistry Laboratory (LGB) in Lausanne. His research focuses on environmental science, computational methods applied to coral reef ecosystems, deep learning, and signal processing. He is enrolled in the doctoral program in Civil and Environmental Engineering at EPFL. His work bridges environmental science and artificial intelligence, with notable contributions to coral reef monitoring using 3D mapping, semantic segmentation of benthic environments, and self-supervised learning techniques for underwater imaging. He has also explored structured sparse matrices in signal recovery and neural-augmented algorithms for iterative processes. His research locations include EPFL Valais Wallis (Sion) and the EPFL main campus in Lausanne, with labs located at ALP 2 011 (Sion) and GR C2 524 (Lausanne). He collaborates with interdisciplinary teams in environmental engineering and computational science. Professional activities include membership in the EDOC doctoral school (EDCE) and engagement in international conferences. His research themes emphasize scalable solutions for environmental monitoring and advancing deep learning applications in geoscience and marine biology.

Alin Achimمشاهده پروفایل

استاد

• Statistical Signal Processing
• Inverse Problems
• Computational Imaging
• +۷ مورد دیگر

Alin Achim is a Professor of Computational Imaging at the School of Computer Science, University of Bristol, with a focus on statistical signal processing and inverse problems. His research bridges model-based and data-driven approaches in machine learning, particularly for computational imaging applications. Education: B.Sc. and M.Sc. from the University of Bucharest, Ph.D. from the University of Patras. His work emphasizes sparse representations in signal/image processing, enabling non-linear algorithms for image denoising, fusion, segmentation, super-resolution , and novel penalty functions for deep learning architectures. Applications span Synthetic Aperture Radar (SAR) imaging for Earth Observation and biomedical imaging (e.g., lung ultrasound, OCT). Recent publications highlight trends in computational medical imaging (2023), SAR for ocean surface modeling (2022), and non-convex optimization methods (2020). His research integrates statistical sparsity with classical sparsity constraints. Scientific Awards: EUSIPCO Best Student Paper Award (2009) Senior Member IEEE (2009) He has supervised multiple students and leads active projects like Next Generation Quantitative Acoustic Microscopy (NIH-funded) and AssenSAR for naval SAR applications. He contributes to open-access datasets (e.g., OCT2Confocal, 3D OCT Images of Murine Uveitis) and serves on editorial boards for journals like IEEE Transactions on Image Processing .

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.

Quentin Denoyelleمشاهده پروفایل

دانشیار

• Sparse Inverse Problems
• Convex Optimization Algorithms
• Machine Learning
• +۲ مورد دیگر

Quentin Denoyelle is an Associate Professor at MAP5 (CNRS UMR 8145) at Université Paris Cité. Prior to this, he conducted postdoctoral research at the Biomedical Imaging Group (BIG) of Michael Unser at EPFL and earned his PhD in Applied Mathematics under Gabriel Peyré and Vincent Duval at CEREMADE, Université Paris-Dauphine. His research focuses on sparse inverse problems convex optimization algorithms machine learning applications signal and image processing . He is based at Université Paris Cité, working within the MAP5 laboratory, a CNRS joint research unit (UMR 8145).

Tatiana Latychevskaiaمشاهده پروفایل

دانشیار

• Coherent Diffraction Imaging
• Holography
• Electron Microscopy
• +۴ مورد دیگر

Dr. Tatiana Latychevskaia is a Principal Investigator at the Paul Scherrer Institute (PSI) and holds an academic position at the University of Zurich's Department of Physics. She leads the Laboratory for Multiscale Bioimaging, focusing on developing advanced imaging techniques. Her research integrates theoretical and experimental approaches to push the boundaries of nanoscale visualization. Research Interests: Her group innovates in coherent high-resolution 3D imaging, including holography, coherent diffraction imaging, and convergent beam electron diffraction. Key areas are algorithm development for wavefront propagation, iterative phase retrieval, low-energy electron imaging (20–300 eV), and wavefront modulation for beam shaping. Applications span 2D materials, biological specimens, and nanostructured systems. Publication Trends: Recent work emphasizes algorithmic advances in phase retrieval and holographic reconstruction (2025), 2D material characterization using electron diffraction (2023–2025), and novel lensless imaging techniques (2024). Her publications demonstrate consistent leadership in quantitative electron microscopy and computational imaging. Teaching & Mentoring: She lectures on Electron Microscopy (PHY427) and Modern Microscopy (PHY425) at the University of Zurich. Actively supervises a diverse team including Postdocs, PhD students, and undergraduate researchers. Current advisees include Dylan Brault (Postdoc), Piet Fang (PhD), and Zhiyong Zhuang (Master's).

Jan S. Hesthavenمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +۶ مورد دیگر

Jan S. Hesthaven is a Professor and Provost at EPFL, leading academic affairs. He holds a Master's from the Technical University of Denmark (DTU) and a PhD in Numerical Analysis, followed by an honorary dr.techn degree from DTU. His research focuses on high-order computational methods for wave problems, reduced order models, and machine learning integration. He has co-authored over 175 papers and 4 monographs. Previously, he served as Dean of the School of Basic Sciences at EPFL and held roles at Brown University, including Director of the Center for Computation and Visualization. Awards include the Alfred P. Sloan Fellowship and the Philip J. Bray Award. Education: Master of Science in Computational Physics, DTU (1991) PhD in Numerical Analysis, DTU (1995) dr.techn in Computational Mathematics, DTU (2009) Research Interests: Development of high-order numerical methods, computational wave propagation, geophysical flows, and machine learning applications in scientific computing. His work bridges traditional methods with AI-driven approaches for real-time modeling and structural health monitoring. Recent Work: His 2023–2025 publications emphasize machine learning-enhanced models, reduced order methods, and seismic data analysis for environmental applications. Key techniques include physics-informed neural networks and graph-based operator learning. Awards: Alfred P. Sloan Fellowship (2000) NSF Career Award (2002) Philip J. Bray Award (2004) Dr.techn from DTU (2009) Grants & Leadership: Led the Center for Computation and Visualization (CCV) at Brown (2006–2013) and co-directed the NSF Institute ICERM (2010–2013). Current roles include Provost at EPFL and leadership in MATHICSE. Collaborates with industry and applied scientists on computational challenges. Labs & Teams: Active in the MATHICSE lab, focusing on numerical methods and high-performance computing. Involved in interdisciplinary projects combining AI with traditional computational science.

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.

• Signal Processing
• Computer Science
• Communication Systems
• +۳ مورد دیگر

Amel Abdelraheem is a Doctoral Assistant at the Signal Processing Laboratory 4 (LTS4), part of the Institute of Electrical Engineering (IEM) within the School of Engineering (STI) at École Polytechnique Fédérale de Lausanne (EPFL). She is concurrently pursuing her doctoral studies in the Doctoral Program in Computer and Communication Sciences (EDIC), under the Education division (ETU) at EPFL. Her academic work is situated at the intersection of signal processing and computer science, with potential applications in data analysis, machine learning, and communication technologies. Given her affiliation with LTS4, a lab known for advanced research in statistical signal processing and data modeling, her research likely involves algorithmic development, optimization, and inference in complex systems. The trend in her academic environment emphasizes interdisciplinary research in information and communication technologies, particularly in areas such as sparse modeling, inverse problems, and AI-driven signal analysis. While no publications are listed, her involvement in a leading signal processing lab suggests active contribution to cutting-edge research. Scientific Awards: No scientific awards listed in the provided text. Advising and Grants: There is no explicit information about students she advises or grants she leads. As a Doctoral Assistant, she may support teaching and research supervision at the graduate or undergraduate level, but no advisees are mentioned. Labs and Teams: She is a member of the Signal Processing Laboratory 4 (LTS4) at EPFL, a research group focused on foundational and applied aspects of signal processing, machine learning, and data science. The lab is part of the broader STI and IEM ecosystem, which fosters collaboration across engineering disciplines.

Margie Olbinadoمشاهده پروفایل

پژوهشگر

• X-ray Imaging
• Phase-Contrast Imaging
• Tomography
• +۵ مورد دیگر

Dr. Margie Olbinado is an Industrial Liaison Scientist at the Paul Scherrer Institute (PSI) in Switzerland, affiliated with the PSI Center for Photon Science and its Laboratory for Macromolecules and Bioimaging . She specializes in advanced X-ray imaging techniques at the TOMCAT beamline. Education: B.S. and M.S. in Physics, University of the Philippines Ph.D. in Physics, University of Tokyo (2010-2013) Research Interests: Dr. Olbinado is a leading expert in X-ray phase-contrast imaging , tomography , and time-resolved X-ray techniques . Her work focuses on industrial applications, including material characterization, failure analysis, and virtual histology, with recent innovations in X-ray ghost imaging. Publications: Her research spans ultra-fast X-ray microscopy, grating interferometry, and novel imaging modalities like Ghost Tomography. Key collaborations include institutions such as ESRF, Tohoku University, and ANAXAM - Analytics for Advanced Manufacturing. Collaborations: She works closely with PSI Technology Transfer, SLS TT AG, and international research teams to bridge academic advancements with industrial needs.