What is she best known for?
The fields of study she is best known for:
- Statistics
- Mathematical analysis
- Artificial intelligence
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.
Her most cited work include:
- Ten Lectures on Wavelets (15760 citations)
- Orthonormal bases of compactly supported wavelets (7230 citations)
- The wavelet transform, time-frequency localization and signal analysis (5011 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Wavelet (25.75%)
- Algorithm (24.75%)
- Artificial intelligence (16.39%)
What were the highlights of her more recent work (between 2012-2021)?
- Artificial intelligence (16.39%)
- Algorithm (24.75%)
- Pattern recognition (7.36%)
In recent papers she was focusing on the following fields of study:
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.
Between 2012 and 2021, her most popular works were:
- A Nonlinear Squeezing of the Continuous Wavelet Transform Based on Auditory Nerve Models (120 citations)
- ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform (87 citations)
- A New Fully Automated Approach for Aligning and Comparing Shapes (73 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Mathematical analysis
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.
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