World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
84
Citations
31297
World Ranking
845
National Ranking
461

Research.com Recognitions

  • 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.

Overview

Alok Choudhary is affiliated with Northwestern University in the United States. Their research spans multiple fields including materials science, computer science, and engineering, with a focus on subfields such as materials chemistry, mechanical engineering, computer networks and communications, surfaces, coatings and films, and computer vision and pattern recognition.

Their work covers several main topics, notably machine learning in materials science, X-ray diffraction in crystallography, electron and X-ray spectroscopy techniques, advanced data storage technologies, computational drug discovery methods, distributed and parallel computing systems, and systemic sclerosis and related diseases.

Frequent co-authors in their publications include Ankit Agrawal, Wei-keng Liao, Vishu Gupta, Yuwei Mao, and Kamal Choudhary.

Alok Choudhary's research has appeared in a variety of publication venues. These include Scientific Reports, arXiv (Cornell University), SSRN Electronic Journal, npj Computational Materials, and Integrating Materials and Manufacturing Innovation.

Some of their recent papers are:

  • Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets, 2024, npj Computational Materials
  • Recent advances and applications of deep learning methods in materials science, 2022, npj Computational Materials
  • Moving closer to experimental level materials property prediction using AI, 2022, Scientific Reports
  • 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

Alok Choudhary has published a book titled Artificial Intelligence for Science (2022) through World Scientific.

The scientist has been recognized with several awards including Fellow of the IEEE (2005) for contributions to high performance computing systems, ACM Fellow (2009) for contributions to HPC, storage and parallel I/O, and Fellow of the American Association for the Advancement of Science (AAAS) in 2009.

Best Publications

  • A general-purpose machine learning framework for predicting properties of inorganic materials

    Logan Ward;Ankit Agrawal;Alok Nidhi Choudhary;Christopher M Wolverton

  • Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

    Ankit Agrawal;Alok Choudhary

  • 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

  • Recent Advances and Applications of Deep Learning Methods in Materials Science

    Kamal Choudhary;Brian DeCost;Chi Chen;Anubhav Jain

  • The International Exascale Software Project roadmap

    Jack Dongarra;Pete Beckman;Terry Moore;Patrick Aerts

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

    Ying Liu;Wei-keng Liao;Alok Choudhary

  • Combinatorial screening for new materials in unconstrained composition space with machine learning

    Bryce Meredig;Amit K Agrawal;Scott Kirklin;James E. Saal

  • 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

  • Firefly: illuminating future network-on-chip with nanophotonics

    Yan Pan;Prabhat Kumar;John Kim;Gokhan Memik

  • 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

  • Twitter Trending Topic Classification

    Kathy Lee;Diana Palsetia;Ramanathan Narayanan;Md. Mostofa Ali Patwary

  • Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

    Logan Ward;Ruoqian Liu;Amar Krishna;Vinay I. Hegde

  • Improved parallel I/O via a two-phase run-time access strategy

    Juan Miguel del Rosario;Rajesh Bordawekar;Alok Choudhary

  • 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

  • An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections

    Yu Cheng;Yu Cheng;Felix X. Yu;Rogerio S. Feris;Sanjiv Kumar

  • Sentiment Analysis of Conditional Sentences

    Ramanathan Narayanan;Bing Liu;Alok Choudhary

  • Towards Online Spam Filtering in Social Networks

    Hongyu Gao;Yan Chen;Kathy Lee;Diana Palsetia

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

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

  • Terascale direct numerical simulations of turbulent combustion using S3D.

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

Frequent Co-Authors

Wei-keng Liao
Wei-keng Liao Northwestern University
Ankit Agrawal
Ankit Agrawal Northwestern University
Mahmut Kandemir
Mahmut Kandemir Pennsylvania State University
Gokhan Memik
Gokhan Memik Northwestern University
J. Ramanujam
J. Ramanujam Louisiana State University
Yu Cheng
Yu Cheng Microsoft (United States)
Rajeev Thakur
Rajeev Thakur Argonne National Laboratory
Prithviraj Banerjee
Prithviraj Banerjee Ansys (United States)
Geoffrey C. Fox
Geoffrey C. Fox University of Virginia
Rajesh Bordawekar
Rajesh Bordawekar IBM (United States)

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