What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algebra
Angshul Majumdar mainly focuses on Artificial intelligence, Pattern recognition, Compressed sensing, Machine learning and Deep learning.
His Computer vision research extends to Artificial intelligence, which is thematically connected.
His biological study spans a wide range of topics, including Sparse matrix, Gaussian noise and Robustness.
His Compressed sensing research integrates issues from Optimization problem, Image, Iterative reconstruction and Signal processing.
His work in Machine learning tackles topics such as Biometrics which are related to areas like Dictionary learning.
The Deep learning study combines topics in areas such as Finite-state machine, Encoding, Hidden Markov model and Benchmark.
His most cited work include:
- Detecting Silicone Mask-Based Presentation Attack via Deep Dictionary Learning (105 citations)
- Hyperspectral Image Denoising Using Spatio-Spectral Total Variation (102 citations)
- An algorithm for sparse MRI reconstruction by Schatten p-norm minimization (88 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Compressed sensing, Machine learning and Computer vision are his primary areas of study.
Deep learning, Autoencoder, Dictionary learning, Deep belief network and Facial recognition system are the primary areas of interest in his Artificial intelligence study.
His research in Pattern recognition intersects with topics in Artificial neural network and Noise reduction.
The concepts of his Compressed sensing study are interwoven with issues in Optimization problem, Wavelet and Iterative reconstruction.
His Machine learning study combines topics in areas such as K-SVD and Biometrics.
His work on Image as part of his general Computer vision study is frequently connected to k-space, thereby bridging the divide between different branches of science.
He most often published in these fields:
- Artificial intelligence (67.26%)
- Pattern recognition (34.52%)
- Compressed sensing (26.79%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (67.26%)
- Pattern recognition (34.52%)
- Deep learning (17.26%)
In recent papers he was focusing on the following fields of study:
Angshul Majumdar mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Benchmark.
Artificial intelligence is represented through his Autoencoder, Dictionary learning, Feature learning, Convolutional neural network and Representation research.
His study explores the link between Autoencoder and topics such as Discriminative model that cross with problems in Hyperspectral imaging.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Basis, Cluster analysis and Compressed sensing.
His studies deal with areas such as Optimization problem and Sampling as well as Compressed sensing.
Angshul Majumdar interconnects Matrix decomposition, Regularization, Algorithm and Convolution in the investigation of issues within Deep learning.
Between 2018 and 2021, his most popular works were:
- Simultaneous Detection of Multiple Appliances From Smart-Meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning (40 citations)
- McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data. (23 citations)
- Blind Denoising Autoencoder (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Angshul Majumdar spends much of his time researching Artificial intelligence, Machine learning, Matrix completion, Benchmark and Pattern recognition.
Borrowing concepts from Consistency, Angshul Majumdar weaves in ideas under Artificial intelligence.
His work in the fields of Machine learning, such as Kernel method, overlaps with other areas such as Performance results, Set and Severe acute respiratory syndrome coronavirus 2.
His work deals with themes such as Matrix decomposition, Computational biology and Graph, which intersect with Matrix completion.
His Pattern recognition study combines topics from a wide range of disciplines, such as Image and Autoencoder.
His Autoencoder research is multidisciplinary, incorporating elements of Deep belief network and Feature learning.
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