2007 - Fellow of Alfred P. Sloan Foundation
The scientist’s investigation covers issues in Mathematical analysis, Algorithm, Computation, Fast multipole method and Discretization. His work carried out in the field of Mathematical analysis brings together such families of science as Kohn–Sham equations, Basis set and Atomic orbital. The various areas that Lexing Ying examines in his Algorithm study include Binary logarithm and Diagonal.
His Binary logarithm research incorporates themes from Computational complexity theory and Interpolation. His Interpolation research incorporates elements of Scale parameter, Theoretical computer science, Phase correlation and Cartesian coordinate system. His Fast Fourier transform research is multidisciplinary, relying on both Fourier transform and Curvelet.
His main research concerns Applied mathematics, Algorithm, Mathematical analysis, Preconditioner and Discretization. The study incorporates disciplines such as Artificial neural network, Stochastic gradient descent, Linear system, Factorization and Function in addition to Applied mathematics. His Algorithm research includes elements of Binary logarithm, Simple and Representation.
His Mathematical analysis research is multidisciplinary, incorporating perspectives in Scattering and Boundary. Lexing Ying has included themes like Helmholtz equation, Solver and Linear algebra in his Preconditioner study. His biological study deals with issues like Integral equation, which deal with fields such as Fast multipole method.
Lexing Ying mostly deals with Applied mathematics, Algorithm, Artificial neural network, Stochastic gradient descent and Discretization. His biological study spans a wide range of topics, including Factorization, Type, Preconditioner, Function and Relaxation. The Algorithm study combines topics in areas such as Energy and Wavelet, Wavelet transform.
His Artificial neural network research also works with subjects such as
His primary areas of investigation include Artificial neural network, Applied mathematics, Nonlinear system, Algorithm and Artificial intelligence. His study in Artificial neural network is interdisciplinary in nature, drawing from both Field, Time evolution, Mathematical optimization and Inverse problem. Lexing Ying has researched Field in several fields, including Mathematical analysis and Near and far field.
His research in Applied mathematics intersects with topics in Type, Partial differential equation, Regular polygon, Discretization and Function. Lexing Ying combines subjects such as Multiple time dimensions, Fractional Laplacian, Solver and Laplace operator with his study of Discretization. By researching both Algorithm and Operator, Lexing Ying produces research that crosses academic boundaries.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Fast Discrete Curvelet Transforms
Emmanuel J. Candès;Laurent Demanet;David L. Donoho;Lexing Ying.
Multiscale Modeling & Simulation (2006)
A kernel-independent adaptive fast multipole algorithm in two and three dimensions
Lexing Ying;George Biros;Denis Zorin.
Journal of Computational Physics (2004)
Seismic wave extrapolation using lowrank symbol approximation
Sergey B Fomel;Lexing Ying;Xiaolei Song.
Geophysical Prospecting (2013)
Wave atoms and sparsity of oscillatory patterns
Laurent Demanet;Lexing Ying.
Applied and Computational Harmonic Analysis (2007)
A massively parallel adaptive fast multipole method on heterogeneous architectures
Ilya Lashuk;Aparna Chandramowlishwaran;Harper Langston;Tuan-Anh Nguyen.
Communications of The ACM (2012)
Sweeping Preconditioner for the Helmholtz Equation: Moving Perfectly Matched Layers
Björn Engquist;Lexing Ying.
Multiscale Modeling & Simulation (2011)
Sweeping preconditioner for the Helmholtz equation: Hierarchical matrix representation
Björn Engquist;Lexing Ying.
Communications on Pure and Applied Mathematics (2011)
3D discrete curvelet transform
Lexing Ying;Laurent Demanet;Emmanuel Candes.
Proceedings of SPIE (2005)
Solving parametric PDE problems with artificial neural networks
Yuehaw Khoo;Jianfeng Lu;Lexing Ying.
European Journal of Applied Mathematics (2021)
Texture and Shape Synthesis on Surfaces
Lexing Ying;Aaron Hertzmann;Henning Biermann;Denis Zorin.
eurographics symposium on rendering techniques (2001)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: