2023 - Research.com Computer Science in United States Leader Award
2023 - Research.com Mathematics in United States Leader Award
2022 - Research.com Mathematics in United States Leader Award
2018 - Member of the National Academy of Engineering For contributions to imaging, computer vision, and graphics including level-set methods and efficient compressed sensing.
2013 - John von Neumann Lecturer
2013 - Fellow of the American Mathematical Society
2009 - Fellow of the American Academy of Arts and Sciences
2009 - SIAM Fellow For contributions to the numerical solution of partial differential equations, level set methods, and image processing.
2007 - THE J. TINSLEY ODEN MEDAL
2005 - Member of the National Academy of Sciences
1972 - Fellow of Alfred P. Sloan Foundation
Stanley Osher mainly focuses on Mathematical analysis, Algorithm, Level set method, Applied mathematics and Image processing. His Mathematical analysis study integrates concerns from other disciplines, such as Regularization and Nonlinear system. Stanley Osher combines subjects such as Mathematical optimization, Minification and Deblurring with his study of Algorithm.
His Level set method research incorporates elements of Level set, Level set, Curvature, Signed distance function and Vector field. His research investigates the connection between Applied mathematics and topics such as Scale space that intersect with issues in Inverse. The concepts of his Image processing study are interwoven with issues in Bounded variation and Pattern recognition.
Stanley Osher mostly deals with Mathematical analysis, Algorithm, Artificial intelligence, Applied mathematics and Computer vision. The study of Mathematical analysis is intertwined with the study of Level set method in a number of ways. Stanley Osher interconnects Level set and Level set in the investigation of issues within Level set method.
His Algorithm study combines topics in areas such as Mathematical optimization, Minification and Inverse problem. His studies link Pattern recognition with Artificial intelligence. His Applied mathematics study frequently draws parallels with other fields, such as Gradient descent.
His primary areas of study are Algorithm, Artificial neural network, Applied mathematics, Artificial intelligence and Gradient descent. Stanley Osher has included themes like Phase retrieval and Minification in his Algorithm study. His biological study spans a wide range of topics, including Function, Training set, Mathematical optimization and Robustness.
His Applied mathematics study incorporates themes from Dimension, Laplacian smoothing, Partial differential equation, Interpolation and Point. His studies in Artificial intelligence integrate themes in fields like Machine learning, Residual and Pattern recognition. His Regularization research is multidisciplinary, relying on both Smoothing and Image processing.
Stanley Osher spends much of his time researching Algorithm, Artificial neural network, Applied mathematics, Function and Optimal control. In the field of Algorithm, his study on Regularization overlaps with subjects such as Quantization. The study incorporates disciplines such as Saddle point, Structure, Training set and Mean field theory in addition to Artificial neural network.
The Applied mathematics study combines topics in areas such as Stability, Dimension, Metric and Laplace operator. His study in Optimal control is interdisciplinary in nature, drawing from both Curse of dimensionality, Hamilton–Jacobi equation, Differential game, Viscosity solution and Gaussian noise. His Mathematical optimization research is multidisciplinary, incorporating elements of Image processing and Type.
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Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Stanley Osher;James A. Sethian.
Journal of Computational Physics (1988)
Nonlinear total variation based noise removal algorithms
Leonid I. Rudin;Stanley Osher;Emad Fatemi.
Physica D: Nonlinear Phenomena (1992)
Level Set Methods and Dynamic Implicit Surfaces
Stanley Osher;Ronald Fedkiw.
Efficient implementation of essentially non-oscillatory shock-capturing schemes,II
Chi-Wang Shu;Stanley Osher.
Journal of Computational Physics (1989)
A level set approach for computing solutions to incompressible two-phase flow
Mark Sussman;Peter Smereka;Stanley Osher.
Journal of Computational Physics (1994)
The Split Bregman Method for L1-Regularized Problems
Tom Goldstein;Stanley Osher.
Siam Journal on Imaging Sciences (2009)
Weighted essentially non-oscillatory schemes
Xu-Dong Liu;Stanley Osher;Tony Chan.
Journal of Computational Physics (1994)
A Non-oscillatory Eulerian Approach to Interfaces in Multimaterial Flows (the Ghost Fluid Method)
Ronald P Fedkiw;Tariq Aslam;Barry Merriman;Stanley Osher.
Journal of Computational Physics (1999)
Level set methods: an overview and some recent results
Stanley Osher;Ronald P. Fedkiw.
Journal of Computational Physics (2001)
An Iterative Regularization Method for Total Variation-Based Image Restoration
Stanley J. Osher;Martin Burger;Donald Goldfarb;Jinjun Xu.
Multiscale Modeling & Simulation (2005)
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