World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8742
World Ranking
7920
National Ranking
3414

Overview

Wei-keng Liao is affiliated with Northwestern University in the United States. Their research primarily spans the fields of Engineering, Computer Science, and Materials Science, with a focus on subfields such as Materials Chemistry, Electrical and Electronic Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition, and Mechanical Engineering.

The scientist's work addresses topics including Machine Learning in Materials Science, X-ray Diffraction in Crystallography, Advanced Data Storage Technologies, Distributed and Parallel Computing Systems, Computational Drug Discovery Methods, Electron and X-Ray Spectroscopy Techniques, and Parallel Computing and Optimization Techniques.

Frequent publication venues for Wei-keng Liao include arXiv (Cornell University), Scientific Reports, Microscopy and Microanalysis, Journal of Physics Conference Series, and Nature Communications. The scientist has produced a considerable number of publications in these venues, with the highest counts in arXiv and Scientific Reports.

Among notable recent publications are:

  • "Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data" (2021, Nature Communications)
  • "Enabling deeper learning on big data for materials informatics applications" (2021, Scientific Reports)
  • "Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets" (2024, npj Computational Materials)
  • "Moving closer to experimental level materials property prediction using AI" (2022, Scientific Reports)
  • "A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures" (2022, Journal of Intelligent Manufacturing)

Wei-keng Liao frequently collaborates with several co-authors, including Ankit Agrawal, Alok Choudhary, Vishu Gupta, Kamal Choudhary, and Yuwei Mao. These collaborations reflect interdisciplinary work that integrates expertise in computational methods and materials science.

Best Publications

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

    Ying Liu;Wei-keng Liao;Alok Choudhary

  • Terascale direct numerical simulations of turbulent combustion using S3D

    J. H. Chen;A. Choudhary;B. De Supinski;M. Devries

  • A fast high utility itemsets mining algorithm

    Ying Liu;Wei-keng Liao;Alok Choudhary

  • Parallel netCDF: A High-Performance Scientific I/O Interface

    Jianwei Li;Wei-keng Liao;Alok Choudhary;Robert Ross

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

    Dipendra Jha;Logan Ward;Arindam Paul;Wei-Keng Liao

  • 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

  • Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

    Dipendra Jha;Kamal Choudhary;Francesca Tavazza;Wei keng Liao

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

    Salman Habib;Adrian Pope;Hal Finkel;Nicholas Frontiere;Nicholas Frontiere

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

    Md. Mostofa Ali Patwary;Diana Palsetia;Ankit Agrawal;Wei-keng Liao

  • Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches

    Zijiang Yang;Yuksel C. Yabansu;Dipendra Jha;Wei keng Liao

  • Social media evolution of the Egyptian revolution

    Alok Choudhary;William Hendrix;Kathy Lee;Diana Palsetia

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

    Wei-keng Liao;Alok Choudhary

  • Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data.

    Vishu Gupta;Kamal Choudhary;Francesca Tavazza;Carelyn Campbell

  • Scaling parallel I/O performance through I/O delegate and caching system

    Arifa Nisar;Wei-keng Liao;Alok Choudhary

  • Noncontiguous I/O through PVFS

    Avery Ching;A. Choudhary;Wei-keng Liao;R. Ross

  • Noncontiguous I/O accesses through MPI-IO

    A. Ching;A. Choudhary;K. Coloma;Wei-keng Liao

  • Fast Algorithms for the Maximum Clique Problem on Massive Sparse Graphs

    Bharath Pattabiraman;Md. Mostofa Ali Patwary;Assefaw Hadish Gebremedhin;Wei-keng Liao

  • A Parallel Monte Carlo Code for Simulating Collisional N-body Systems

    Bharath Pattabiraman;Stefan Umbreit;Wei-Keng Liao;Alok Nidhi Choudhary

  • Collective caching: application-aware client-side file caching

    Wei-keng Liao;K. Coloma;A. Choudhary;L. Ward

  • Mining millions of reviews: a technique to rank products based on importance of reviews

    Kunpeng Zhang;Yu Cheng;Wei-keng Liao;Alok Choudhary

  • Terascale direct numerical simulations of turbulent combustion using S3D.

    Ramanan Sankaran;J. Mellor-Crummy;M. DeVries;Chun Sang Yoo

Frequent Co-Authors

Alok Choudhary
Alok Choudhary Northwestern University
Ankit Agrawal
Ankit Agrawal Northwestern University
Robert Ross
Robert Ross Argonne National Laboratory
Zijiang Yang
Zijiang Yang Western Michigan University
Gokhan Memik
Gokhan Memik Northwestern University
Mahmut Kandemir
Mahmut Kandemir Pennsylvania State University
Rajeev Thakur
Rajeev Thakur Argonne National Laboratory
Pramod K. Varshney
Pramod K. Varshney Syracuse University
Yu Cheng
Yu Cheng Microsoft (United States)
William Gropp
William Gropp University of Illinois at Urbana-Champaign

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