What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Statistics
His scientific interests lie mostly in Artificial intelligence, Compressed sensing, Computer vision, Algorithm and Kalman filter.
Namrata Vaswani usually deals with Artificial intelligence and limits it to topics linked to Pattern recognition and Subspace topology.
His study in Compressed sensing is interdisciplinary in nature, drawing from both Sequence and Iterative reconstruction.
His research integrates issues of Image and Mathematical optimization in his study of Iterative reconstruction.
As part of the same scientific family, he usually focuses on Computer vision, concentrating on Hidden Markov model and intersecting with Histogram.
The various areas that he examines in his Algorithm study include Signal and Bounded function.
His most cited work include:
- Modified-CS: Modifying compressive sensing for problems with partially known support (468 citations)
- Kalman filtered Compressed Sensing (237 citations)
- Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours (155 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Algorithm, Artificial intelligence, Subspace topology, Compressed sensing and Principal component analysis.
His Algorithm research incorporates themes from Signal, Mathematical optimization and Bounded function.
His work deals with themes such as Computer vision and Pattern recognition, which intersect with Artificial intelligence.
His studies in Subspace topology integrate themes in fields like Matrix decomposition, Sparse matrix, Discrete mathematics and Outlier.
The study incorporates disciplines such as Kalman filter, Sequence, Residual and Iterative reconstruction in addition to Compressed sensing.
His work carried out in the field of Principal component analysis brings together such families of science as Singular value decomposition, Correctness and Robustness.
He most often published in these fields:
- Algorithm (50.62%)
- Artificial intelligence (38.12%)
- Subspace topology (36.87%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (50.62%)
- Subspace topology (36.87%)
- Outlier (20.00%)
In recent papers he was focusing on the following fields of study:
Namrata Vaswani spends much of his time researching Algorithm, Subspace topology, Outlier, Phase retrieval and Principal component analysis.
The Algorithm study combines topics in areas such as Upper and lower bounds, Missing data and Matrix completion.
His Subspace topology research includes elements of Tracking, Sparse matrix and Dimensionality reduction.
The subject of his Outlier research is within the realm of Artificial intelligence.
His research is interdisciplinary, bridging the disciplines of Computer vision and Artificial intelligence.
His Phase retrieval research is multidisciplinary, incorporating elements of Condition number, Signal and Combinatorics.
Between 2016 and 2021, his most popular works were:
- Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery (120 citations)
- Low-Rank Phase Retrieval (38 citations)
- Provable Dynamic Robust PCA or Robust Subspace Tracking (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Statistics
His primary areas of investigation include Algorithm, Subspace topology, Principal component analysis, Outlier and Artificial intelligence.
His work deals with themes such as Exploratory data analysis and Numerical linear algebra, which intersect with Algorithm.
Namrata Vaswani has researched Subspace topology in several fields, including Matrix decomposition, Sparse matrix, Matrix completion and Compressed sensing.
He works mostly in the field of Principal component analysis, limiting it down to topics relating to Robustness and, in certain cases, Singular value decomposition and Dimensionality reduction, as a part of the same area of interest.
Particularly relevant to Iterative reconstruction is his body of work in Artificial intelligence.
His studies deal with areas such as Range and Signal as well as Computer vision.
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