What is she best known for?
The fields of study she is best known for:
- Algorithm
- Statistics
- Artificial intelligence
Her primary scientific interests are in Algorithm, Sparse approximation, Representation, Polynomial and Compressed sensing.
Her Algorithm study often links to related topics such as Linear combination.
The concepts of her Sparse approximation study are interwoven with issues in Adjacency matrix, Matching pursuit, Sparse matrix and Greedy algorithm.
The Matching pursuit study which covers Mathematical optimization that intersects with Basis pursuit denoising.
Anna C. Gilbert has included themes like Discrete-time signal, Fourier transform and Signal in her Polynomial study.
Her research integrates issues of Data stream, Algorithm design and Signal reconstruction, Signal processing in her study of Compressed sensing.
Her most cited work include:
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit (7158 citations)
- Algorithms for simultaneous sparse approximation: part I: Greedy pursuit (1100 citations)
- Dynamics of IP traffic: a study of the role of variability and the impact of control (394 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Algorithm, Compressed sensing, Sparse approximation, Combinatorics and Theoretical computer science.
Her biological study focuses on Matching pursuit.
Her work in the fields of Matching pursuit, such as Basis pursuit, overlaps with other areas such as Convex optimization.
Her Compressed sensing study also includes fields such as
- Signal reconstruction which intersects with area such as Mathematical optimization,
- Group testing which is related to area like Decoding methods,
- Wireless sensor network that intertwine with fields like Structural health monitoring and Real-time computing.
Her Sparse approximation study necessitates a more in-depth grasp of Artificial intelligence.
Her Combinatorics study combines topics from a wide range of disciplines, such as Discrete mathematics, Upper and lower bounds and Distribution.
She most often published in these fields:
- Algorithm (30.57%)
- Compressed sensing (18.47%)
- Sparse approximation (14.65%)
What were the highlights of her more recent work (between 2017-2020)?
- Algorithm (30.57%)
- Discrete mathematics (10.83%)
- Metric (5.10%)
In recent papers she was focusing on the following fields of study:
The scientist’s investigation covers issues in Algorithm, Discrete mathematics, Metric, Feature and Differential privacy.
Her Algorithm research focuses on Synthetic data in particular.
The study incorporates disciplines such as Fast Fourier transform, Legendre polynomials, Discrete Fourier transform, Jacobi polynomials and Classical orthogonal polynomials in addition to Discrete mathematics.
Her research in Metric intersects with topics in Embedding, Tree structure, Key and Orders of magnitude.
Her Feature research includes elements of Ranking, Pairwise comparison and Information retrieval.
Her Differential privacy research incorporates elements of Property testing and Theoretical computer science.
Between 2017 and 2020, her most popular works were:
- But How Does It Work in Theory? Linear SVM with Random Features (27 citations)
- On the Approximation Properties of Random ReLU Features (15 citations)
- Property Testing for Differential Privacy (7 citations)
In her most recent research, the most cited papers focused on:
- Algorithm
- Statistics
- Artificial intelligence
Anna C. Gilbert mainly focuses on Reproducing kernel Hilbert space, Bounded function, Algorithm, Mutual fund separation theorem and Artificial neural network.
Her Reproducing kernel Hilbert space investigation overlaps with Fourier transform, Universality theorem, Of the form, Function and Feature.
Anna C. Gilbert integrates Universality theorem with Applied mathematics in her study.
Her research in the fields of Synthetic data overlaps with other disciplines such as Work.
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