Grants per year
Personal profile
Research Interests
Professor Alok Choudhary's research over the last decades focused on big data science, supercomputing, scalable data mining, Machine Learning, AI and their applications in sciences (e.g. climate understanding, astrophysics, designing materials etc.), medicine (e.g., understanding diseases causes, outcome predictions, drug design etc.) and business applications (e.g., business intelligence, marketing, advertising).
Education/Academic qualification
Computer Engineering (ECE Department), PhD, University of Illinois at Urbana-Champaign
… → 1989
Computer Engineering (ECE Department), MS, University of Massachusetts
… → 1986
Electrical and Electronics Engineering, BE, (Honors), Birla Institute of Technology and Science Pilani
… → 1982
Research interests keywords
- Computer architecture
- Data intensive computing
- High-performance I/O systems and software
- High-performance computing
- Materials and Medical Informatics
- Scalable data mining
- Social Media
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Collaborations and top research areas from the last five years
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Collaborative Research: AI-Driven Multi-Scale Design of Materials under Processing Constraints
Agrawal, A. & Choudhary, A. N.
9/1/21 → 8/31/24
Project: Research project
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RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence
Liao, W., Agrawal, A. & Choudhary, A. N.
9/23/20 → 9/22/25
Project: Research project
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High-performance I/O Research, Development, and Deployment for DataLib
UChicago Argonne, LLC, Argonne National Laboratory, Department of Energy
10/1/19 → 9/30/23
Project: Research project
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ANL joint appointment funding-Alok Choudhary-data libraries
UChicago Argonne, LLC, Argonne National Laboratory, Department of Energy
10/1/19 → 9/30/23
Project: Research project
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Alok Choudhary Subproject for CHiMaD
National Institute of Standards and Technology
1/1/19 → 12/31/23
Project: Research project
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A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures
Mao, Y., Lin, H., Yu, C. X., Frye, R., Beckett, D., Anderson, K., Jacquemetton, L., Carter, F., Gao, Z., Liao, W. K., Choudhary, A. N., Ehmann, K. & Agrawal, A., Jan 2023, In: Journal of Intelligent Manufacturing. 34, 1, p. 315-329 15 p.Research output: Contribution to journal › Article › peer-review
Open Access4 Scopus citations -
AI for science
Choudhary, A., Fox, G. & Hey, T., Mar 21 2023, Artificial Intelligence For Science: A Deep Learning Revolution. World Scientific Publishing Co. Pte Ltd, p. 3-11 9 p.Research output: Chapter in Book/Report/Conference proceeding › Chapter
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An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems
Mao, Y., Hasan, M., Paul, A., Gupta, V., Choudhary, K., Tavazza, F., Liao, W. K., Choudhary, A., Acar, P. & Agrawal, A., Dec 2023, In: npj Computational Materials. 9, 1, 111.Research output: Contribution to journal › Article › peer-review
Open Access1 Scopus citations -
Artificial intelligence for accelerating materials discovery
Agrawal, A. & Choudhary, A., Mar 21 2023, Artificial Intelligence For Science: A Deep Learning Revolution. World Scientific Publishing Co. Pte Ltd, p. 431-443 13 p.Research output: Chapter in Book/Report/Conference proceeding › Chapter
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Artificial intelligence for science: A deep learning revolution
Choudhary, A., Fox, G. & Hey, T., Mar 21 2023, World Scientific Publishing Co. Pte Ltd. 790 p.Research output: Book/Report › Book
Datasets
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Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
Gupta, V. (Contributor), Choudhary, K. (Contributor), Tavazza, F. (Contributor), Campbell, C. (Contributor), Liao, W. (Contributor), Choudhary, A. N. (Contributor) & Agrawal, A. (Contributor), ZENODO, Sep 28 2021
DOI: 10.5281/zenodo.5533023, https://zenodo.org/record/5533023
Dataset
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ALIGNN TL Models
Gupta, V. (Creator), Choudhary, K. (Creator), DeCost, B. (Creator), Tavazza, F. (Creator), Campbell, C. (Creator), Liao, W. (Creator), Choudhary, A. N. (Creator) & Agrawal, A. (Creator), ZENODO, 2022
DOI: 10.5281/zenodo.6816246, https://zenodo.org/record/6816246
Dataset