What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Programming language
His main research concerns Parallel computing, Artificial intelligence, Compiler, Data mining and Set.
As part of his studies on Parallel computing, Alok Choudhary often connects relevant subjects like Fortran.
His Artificial intelligence research incorporates elements of Machine learning and Identification.
His Compiler study deals with Embedded system intersecting with Reduction.
His Data mining research is multidisciplinary, incorporating elements of Scalability and Data science.
The various areas that he examines in his Cache study include Multiprocessing, Locality, Server and Loop nest optimization.
His most cited work include:
- The International Exascale Software Project roadmap (580 citations)
- Terascale direct numerical simulations of turbulent combustion using S3D (420 citations)
- A two-phase algorithm for fast discovery of high utility itemsets (402 citations)
What are the main themes of his work throughout his whole career to date?
Alok Choudhary spends much of his time researching Parallel computing, Compiler, Data mining, Distributed computing and Artificial intelligence.
His studies deal with areas such as Input/output, Scalability and Locality as well as Parallel computing.
Alok Choudhary combines subjects such as Embedded system and Fortran with his study of Compiler.
A large part of his Data mining studies is devoted to Association rule learning.
Alok Choudhary has researched Distributed computing in several fields, including Scheduling, File system and Server.
His Artificial intelligence research includes themes of Machine learning and Pattern recognition.
He most often published in these fields:
- Parallel computing (30.82%)
- Compiler (13.07%)
- Data mining (12.78%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (12.22%)
- Deep learning (2.84%)
- Algorithm (8.38%)
In recent papers he was focusing on the following fields of study:
Alok Choudhary focuses on Artificial intelligence, Deep learning, Algorithm, Machine learning and Parallel computing.
His studies in Artificial intelligence integrate themes in fields like Scalability and Pattern recognition.
His biological study deals with issues like Big data, which deal with fields such as Analytics.
His Algorithm research is multidisciplinary, incorporating perspectives in Mean squared error and Cluster analysis, Fuzzy clustering.
The Machine learning study combines topics in areas such as Variety, Set, Domain knowledge and Identification.
The concepts of his Parallel computing study are interwoven with issues in Compiler and File system.
Between 2014 and 2021, his most popular works were:
- A general-purpose machine learning framework for predicting properties of inorganic materials (388 citations)
- Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science (317 citations)
- Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection (254 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Programming language
Alok Choudhary mainly focuses on Artificial intelligence, Deep learning, Machine learning, Convolutional neural network and Materials informatics.
His Artificial intelligence study incorporates themes from Set, Data mining and Pattern recognition.
His study in Deep learning is interdisciplinary in nature, drawing from both Transfer of learning, Data-driven, Random forest and Domain.
His Machine learning study combines topics from a wide range of disciplines, such as Alloy, Variety and Identification.
His Artificial neural network research is multidisciplinary, relying on both Circulant matrix and Algorithm.
His study in the field of Petascale computing is also linked to topics like Spark.
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