2019 - German National Academy of Sciences Leopoldina - Deutsche Akademie der Naturforscher Leopoldina – Nationale Akademie der Wissenschaften Mathematics
2015 - Fellow, The World Academy of Sciences
2015 - Member of Academia Europaea
2015 - Member of the National Academy of Engineering For contributions to the mathematics and applications of wavelets.
2013 - Fellow of the American Mathematical Society
2012 - BBVA Foundation Frontiers of Knowledge Award
2011 - Benjamin Franklin Medal, Franklin Institute
2011 - Fellow of the American Association for the Advancement of Science (AAAS)
2011 - Jack S. Kilby Signal Processing Medal For pioneering contributions to the theory and applications of wavelets and filter banks.
2011 - John von Neumann Lecturer
2011 - Steele Prize for Seminal Contribution to Research
2010 - Fellow of John Simon Guggenheim Memorial Foundation
2009 - SIAM Fellow For contributions to the theory of wavelets and computational harmonic analysis.
1999 - Royal Netherlands Academy of Arts and Sciences
1998 - Member of the National Academy of Sciences
1994 - Steele Prize for Mathematical Exposition
1993 - Fellow of the American Academy of Arts and Sciences
1992 - Fellow of the MacArthur Foundation
Her primary areas of investigation include Wavelet, Algorithm, Mathematical analysis, Wavelet transform and Orthonormal basis. Her study in Wavelet is interdisciplinary in nature, drawing from both Classification of discontinuities and Applied mathematics. Her Algorithm research is multidisciplinary, incorporating perspectives in Continuous signal, Analog image processing, Multidimensional signal processing and Analog signal.
Ingrid Daubechies studies Discrete wavelet transform, a branch of Wavelet transform. Ingrid Daubechies has researched Orthonormal basis in several fields, including Penalty method, Quadratic equation, Exponential decay, Thresholding and Simple. Her Biorthogonal wavelet research includes elements of Pure mathematics and Legendre wavelet.
Wavelet, Algorithm, Artificial intelligence, Mathematical analysis and Wavelet transform are her primary areas of study. Her research integrates issues of Orthonormal basis and Mathematical optimization in her study of Wavelet. Her Algorithm study which covers Inverse problem that intersects with Norm.
Her work in Artificial intelligence tackles topics such as Painting which are related to areas like Image processing. The various areas that Ingrid Daubechies examines in her Mathematical analysis study include Refinable function, Function, Path integral formulation and Pure mathematics. Her studies in Second-generation wavelet transform and Harmonic wavelet transform are all subfields of Discrete wavelet transform research.
Her main research concerns Artificial intelligence, Algorithm, Pattern recognition, Wavelet and Painting. Her study on Source separation, Artificial neural network and Image is often connected to Separation process as part of broader study in Artificial intelligence. Ingrid Daubechies works in the field of Algorithm, focusing on Regularization in particular.
Her Wavelet research integrates issues from Orthonormal basis, Lambda, Reconstruction problem, Sign and Fourier analysis. Her work is dedicated to discovering how Orthonormal basis, Subspace topology are connected with Discrete mathematics and other disciplines. Her Painting research is multidisciplinary, incorporating elements of X ray image and Computer graphics.
Her primary scientific interests are in Artificial intelligence, Algorithm, Painting, Pattern recognition and Wavelet. Her work on Image restoration, Visualization and Source separation as part of general Artificial intelligence study is frequently linked to Component, bridging the gap between disciplines. As a member of one scientific family, she mostly works in the field of Algorithm, focusing on Mathematical analysis and, on occasion, Representation and Similarity.
Ingrid Daubechies interconnects SAINT, Salient, Computer graphics and Computer vision in the investigation of issues within Painting. Her work deals with themes such as Nonparametric statistics, Stochastic optimization, Bayes' theorem, Benchmark and Gibbs sampling, which intersect with Pattern recognition. She studies Wavelet, namely Wavelet transform.
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.
Ten Lectures on Wavelets
Orthonormal bases of compactly supported wavelets
Communications on Pure and Applied Mathematics (1988)
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory (1990)
Image coding using wavelet transform
M. Antonini;M. Barlaud;P. Mathieu;I. Daubechies.
IEEE Transactions on Image Processing (1992)
An Iterative Thresholding Algorithm for Linear Inverse Problems with a Sparsity Constraint
Ingrid Daubechies;Michel Defrise;Christine De Mol.
Communications on Pure and Applied Mathematics (2004)
Biorthogonal bases of compactly supported wavelets
A. Cohen;Ingrid Daubechies;J.-C. Feauveau.
Communications on Pure and Applied Mathematics (1992)
Factoring wavelet transforms into lifting steps
Ingrid Daubechies;Wim Sweldens.
Journal of Fourier Analysis and Applications (1998)
PAINLESS NONORTHOGONAL EXPANSIONS
Ingrid Daubechies;A. Grossmann;Y. Meyer.
Journal of Mathematical Physics (1986)
Wavelet Transforms That Map Integers to Integers
A.R. Calderbank;Ingrid Daubechies;Wim Sweldens;Boon-Lock Yeo.
Applied and Computational Harmonic Analysis (1998)
Wavelets on the Interval and Fast Wavelet Transforms
Albert Cohen;Ingrid Daubechies;Pierre Vial.
Applied and Computational Harmonic Analysis (1993)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
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: