H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology D-index 30 Citations 5,680 149 World Ranking 7122 National Ranking 2415

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Artificial intelligence
  • Machine learning

His primary scientific interests are in Parallel computing, File system, Artificial intelligence, Computational science and Interface. The Parallel computing study combines topics in areas such as Distributed computing and Cluster analysis. His study in File system is interdisciplinary in nature, drawing from both Degree of parallelism, Server and Cache.

Wei-keng Liao has included themes like Social media and Identification in his Artificial intelligence study. His work carried out in the field of Computational science brings together such families of science as Supercomputer, CUDA and Parallel coordinates. His research integrates issues of NetCDF, Hierarchical Data Format, File format and Parallel processing in his study of Interface.

His most cited work include:

  • Terascale direct numerical simulations of turbulent combustion using S3D (420 citations)
  • A two-phase algorithm for fast discovery of high utility itemsets (402 citations)
  • Parallel netCDF: A High-Performance Scientific I/O Interface (324 citations)

What are the main themes of his work throughout his whole career to date?

Wei-keng Liao spends much of his time researching Parallel computing, Scalability, Input/output, Cluster analysis and Artificial intelligence. Wei-keng Liao has researched Parallel computing in several fields, including File system and Distributed computing. His Scalability research focuses on Software and how it relates to Visualization.

As a part of the same scientific family, he mostly works in the field of Input/output, focusing on Parallel processing and, on occasion, Interface. In his research on the topic of Cluster analysis, Set and Algorithm is strongly related with Data mining. His research in Supercomputer focuses on subjects like Computational science, which are connected to Hierarchical Data Format.

He most often published in these fields:

  • Parallel computing (30.24%)
  • Scalability (18.05%)
  • Input/output (14.15%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (13.17%)
  • Artificial neural network (3.90%)
  • Transfer of learning (2.93%)

In recent papers he was focusing on the following fields of study:

Wei-keng Liao mostly deals with Artificial intelligence, Artificial neural network, Transfer of learning, Deep learning and Machine learning. His Artificial intelligence research incorporates elements of Process and Diffraction. His Deep learning research integrates issues from Degree of parallelism, Parallel computing, Scalability and Convolutional neural network.

His Scalability research focuses on subjects like Volume, which are linked to Computer network. His work in the fields of Recurrent neural network overlaps with other areas such as Materials informatics. The study incorporates disciplines such as Random forest and Data mining in addition to Layer.

Between 2018 and 2021, his most popular works were:

  • Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches (46 citations)
  • Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning (40 citations)
  • Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation (21 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Artificial intelligence
  • Machine learning

Artificial intelligence, Artificial neural network, Machining, Deep learning and Set are his primary areas of study. His biological study spans a wide range of topics, including Process and Pattern recognition. His Deep learning research is multidisciplinary, relying on both Domain and Convolutional neural network.

His research in Domain intersects with topics in Layer, Data mining, Vanishing gradient problem, Knowledge integration and Random forest. He combines subjects such as Field, Identification and Big data with his study of Convolutional neural network. The concepts of his Set study are interwoven with issues in Tree and Molecular graph.

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.

Best Publications

A two-phase algorithm for fast discovery of high utility itemsets

Ying Liu;Wei-keng Liao;Alok Choudhary.
knowledge discovery and data mining (2005)

571 Citations

Terascale direct numerical simulations of turbulent combustion using S3D

J. H. Chen;A. Choudhary;B. De Supinski;M. Devries.
Computational Science & Discovery (2009)

537 Citations

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)

494 Citations

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)

409 Citations

A new scalable parallel DBSCAN algorithm using the disjoint-set data structure

Md. Mostofa Ali Patwary;Diana Palsetia;Ankit Agrawal;Wei-keng Liao.
ieee international conference on high performance computing data and analytics (2012)

191 Citations

HACC: Simulating Sky Surveys on State-of-the-Art Supercomputing Architectures

Salman Habib;Adrian Pope;Hal Finkel;Nicholas Frontiere;Nicholas Frontiere.
New Astronomy (2016)

181 Citations

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition.

Dipendra Jha;Logan Ward;Arindam Paul;Wei-Keng Liao.
Scientific Reports (2018)

148 Citations

Dynamically adapting file domain partitioning methods for collective I/O based on underlying parallel file system locking protocols

Wei-keng Liao;Alok Choudhary.
ieee international conference on high performance computing data and analytics (2008)

141 Citations

Social media evolution of the Egyptian revolution

Alok Choudhary;William Hendrix;Kathy Lee;Diana Palsetia.
Communications of The ACM (2012)

140 Citations

Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

Zijiang Yang;Yuksel C. Yabansu;Reda Al-Bahrani;Wei keng Liao.
Computational Materials Science (2018)

138 Citations

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Best Scientists Citing Wei-keng Liao

Jerry Chun-Wei Lin

Jerry Chun-Wei Lin

Western Norway University of Applied Sciences

Publications: 142

Tzung-Pei Hong

Tzung-Pei Hong

National University of Kaohsiung

Publications: 121

Philippe Fournier-Viger

Philippe Fournier-Viger

Harbin Institute of Technology

Publications: 111

Jacqueline H. Chen

Jacqueline H. Chen

Sandia National Laboratories

Publications: 91

Scott Klasky

Scott Klasky

Oak Ridge National Laboratory

Publications: 64

Alok Choudhary

Alok Choudhary

Northwestern University

Publications: 61

Robert Ross

Robert Ross

Argonne National Laboratory

Publications: 61

Vincent S. Tseng

Vincent S. Tseng

National Yang Ming Chiao Tung University

Publications: 56

Nilanjan Chakraborty

Nilanjan Chakraborty

Newcastle University

Publications: 54

Franck Cappello

Franck Cappello

Argonne National Laboratory

Publications: 46

Evatt R. Hawkes

Evatt R. Hawkes

UNSW Sydney

Publications: 46

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 38

Ankit Agrawal

Ankit Agrawal

Northwestern University

Publications: 34

Unil Yun

Unil Yun

Sejong University

Publications: 32

Manish Parashar

Manish Parashar

University of Utah

Publications: 31

Rajeev Thakur

Rajeev Thakur

Argonne National Laboratory

Publications: 28

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