2009 - Fellow of the American Association for the Advancement of Science (AAAS)
2009 - ACM Fellow For contributions to HPC, storage and parallel I/O.
2005 - IEEE Fellow For contributions to high performance computing systems.
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.
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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The International Exascale Software Project roadmap
Jack Dongarra;Pete Beckman;Terry Moore;Patrick Aerts.
ieee international conference on high performance computing data and analytics (2011)
A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward;Ankit Agrawal;Alok Nidhi Choudhary;Christopher M Wolverton.
npj Computational Materials (2016)
A two-phase algorithm for fast discovery of high utility itemsets
Ying Liu;Wei-keng Liao;Alok Choudhary.
knowledge discovery and data mining (2005)
Terascale direct numerical simulations of turbulent combustion using S3D
J. H. Chen;A. Choudhary;B. De Supinski;M. Devries.
Computational Science & Discovery (2009)
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Ankit Agrawal;Alok Choudhary.
APL Materials (2016)
Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection
Kasthurirangan Gopalakrishnan;Siddhartha K. Khaitan;Alok Choudhary;Ankit Agrawal.
Construction and Building Materials (2017)
Combinatorial screening for new materials in unconstrained composition space with machine learning
Bryce Meredig;Amit K Agrawal;Scott Kirklin;James E. Saal.
Physical Review B (2014)
A fast high utility itemsets mining algorithm
Ying Liu;Wei-keng Liao;Alok Choudhary.
Proceedings of the 1st international workshop on Utility-based data mining (2005)
Firefly: illuminating future network-on-chip with nanophotonics
Yan Pan;Prabhat Kumar;John Kim;Gokhan Memik.
international symposium on computer architecture (2009)
Parallel netCDF: A High-Performance Scientific I/O Interface
Jianwei Li;Wei-keng Liao;Alok Choudhary;Robert Ross.
conference on high performance computing (supercomputing) (2003)
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