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Ankit Agrawal

  • 2184 Citations
20052023
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Personal profile

Education/Academic qualification

Computer Science, minor in Computer Science, PhD, Iowa State University

Aug 2006 → Aug 2009

Computer Science and Engineering, BTech, Indian Institute of Technology Roorkee

Aug 2002 → May 2006

Research interests

  • Bioinformatics and Computational Biology
  • Data Mining
  • High Performance Computing

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Learning systems Engineering & Materials Science
Statistical Significance Mathematics
Data mining Engineering & Materials Science
Pairwise Mathematics
Clustering algorithms Engineering & Materials Science
Parallel algorithms Engineering & Materials Science
Neural networks Engineering & Materials Science
Materials science Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Grants 2011 2023

Ankit Agrawal Subproject for CHiMaD

Agrawal, A.

National Institute of Standards and Technology

1/1/19 → 12/31/23

Project: Research project

Industrial applications
Artificial intelligence
Learning systems
Costs
Genes

PROTEUS: Machine Learning Driven Resilience for Extreme-scale Systems

Agrawal, A., Choudhary, A. N. & Liao, W.

Department of Energy

9/1/18 → 8/31/21

Project: Research project

Learning systems
Data storage equipment
Costs
Scalability
Software engineering

BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, & Disseminate

Agrawal, A., Olson, G. B. & Voorhees, P. W.

National Science Foundation

2/15/17 → 1/31/21

Project: Research project

personnel
digital media
journalism
new media
assets

Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing

Agrawal, A., Choudhary, A. N. & Liao, W.

Department of Energy

8/1/15 → 7/31/20

Project: Research project

Natural sciences computing
Data storage equipment
Scalability
Statistics

SHF:Medium:Collaborative Research: Scalable Algorithms for Spatio-temporal Data Analysis

Agrawal, A., Choudhary, A. N. & Liao, W.

National Science Foundation

6/1/14 → 5/31/19

Project: Research project

Supercomputers
Colliding beam accelerators
Telescopes
Data mining
Sustainable development

Research Output 2005 2019

Communication-Efficient Parallelization Strategy for Deep Convolutional Neural Network Training

Lee, S., Agrawal, A., Balaprakash, P., Choudhary, A. N. & Liao, W-K., Feb 8 2019, Proceedings of MLHPC 2018: Machine Learning in HPC Environments, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis. Institute of Electrical and Electronics Engineers Inc., p. 47-56 10 p. 8638635. (Proceedings of MLHPC 2018: Machine Learning in HPC Environments, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Neural networks
Communication
Supercomputers
Synchronization
Costs

Deep learning based domain knowledge integration for small datasets: Illustrative applications in materials informatics

Yang, Z., Al-Bahrani, R., Reid, A. C. E., Papanikolaou, S., Kalidindi, S. R., Liao, W. K., Choudhary, A. & Agrawal, A., Jul 2019, 2019 International Joint Conference on Neural Networks, IJCNN 2019. Institute of Electrical and Electronics Engineers Inc., 8852162. (Proceedings of the International Joint Conference on Neural Networks; vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Learning systems
Materials science
Neural networks
Availability
Deep learning
1 Citation (Scopus)

Deep materials informatics: Applications of deep learning in materials science

Agrawal, A. & Choudhary, A., Sep 1 2019, In : MRS Communications. 9, 3, p. 779-792 14 p.

Research output: Contribution to journal › Review article

Open Access
Materials science
Railroad cars
Availability
Deep learning

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

Jha, D., Choudhary, K., Tavazza, F., Liao, W. K., Choudhary, A., Campbell, C. & Agrawal, A., Dec 1 2019, In : Nature communications. 10, 1, 5316.

Research output: Contribution to journal › Article

Open Access
learning
Density functional theory
Materials properties
density functional theory
predictions
9 Citations (Scopus)

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

Yang, Z., Yabansu, Y. C., Jha, D., Liao, W-K., Choudhary, A. N., Kalidindi, S. R. & Agrawal, A., Mar 1 2019, In : Acta Materialia. 166, p. 335-345 11 p.

Research output: Contribution to journal › Article

Elastic deformation
Composite materials
Microstructure
Materials science
Learning systems