Ankit Agrawal

Research Professor

Department of Electrical and Computer Engineering
McCormick School of Engineering and Applied Science
Northwestern University

Office: M384, TECH
Technological Institute
2145 Sheridan Road
Evanston, Illinois 60208-3118

Ph: 847-491-8163
Fax: 847-491-4455
Email: ankit-agrawal AT northwestern.edu
[CUCIS] [Google Scholar] [DBLP] [Scopus] [ORCID] [NCBI]

Research Interests

Artificial Intelligence, High Performance Data Mining, Materials Informatics, Healthcare Informatics, Social Media Analytics, Bioinformatics

Recent Highlights

  • 09/2024: Featured in Stanford/Elsevier’s list of top 2% scientists worldwide
  • 09/2024: Gave a keynote talk at Global AI Summit 2024: AI+HPC for Accelerating Science and Engineering
  • 07/2024: Named a Top Scholar by ScholarGPS for being in top 0.5% of scholars worldwide in the fields of machine learning, deep learning, and informatics
  • 02/2024: Awarded Nanocombinatorics grant: AI-Driven Nanocombinatorics for Accelerated Structural Characterization: Automated High-Throughput Nanoparticle Library Screening and Analytics
  • 10/2023: Co-awarded NSF grant: EAGER: XAISE: Explainable Artificial Intelligence for Science and Engineering
  • See more here

Education

Appointments

  • Research Professor, Northwestern University, 2020 - present
  • Honorary Professor, Amity University, India, 2020 - present
  • Research Associate Professor, Northwestern University, 2013 - 2020
  • Research Assistant Professor, Northwestern University, 2010 - 2013
  • Postdoctoral Fellow, Northwestern University, 2009 - 2010
  • Research Assistant, Iowa State University, 2007 - 2009
  • Graduate Assistant, Iowa State University, 2006 - 2007

Research Grants

  1. PI, “AI-Driven Nanocombinatorics for Accelerated Structural Characterization: Automated High-Throughput Nanoparticle Library Screening and Analytics (Renewal)”, Center for Nanocombinatorics, Northwestern University, $100,000, 2024-2025.

  2. Co-PI, “EAGER: XAISE: Explainable Artificial Intelligence for Science and Engineering”, National Science Foundation (NSF), $300,000, 2023-2025. (PI: Alok Choudhary) [OAC-2331329]

  3. PI, “AI-Driven Nanocombinatorics for Accelerated Structural Characterization: Automated High-Throughput Nanoparticle Library Screening and Analytics (Renewal)”, Center for Nanocombinatorics, Northwestern University, $100,000, 2023-2024.

  4. Co-PI, “Nitrogen Activation at Catalyst Surfaces to Catalyze Net-Zero: An AI-Driven Approach”, Center for Nanocombinatorics, Northwestern University, $100,000, 2023-2024.

  5. PI, “AI-Driven Nanocombinatorics for Accelerated Structural Characterization: Automated High-Throughput Nanoparticle Library Screening and Analytics”, Center for Nanocombinatorics, Northwestern University, $100,000, 2021-2022.

  6. PI, “AI-Driven Nanocombinatorics for Functional Characterization and Optimization: Predictive Modeling and Active Learning of Catalysis in Megalibraries”, Center for Nanocombinatorics, Northwestern University, $90,000, 2021-2022.

  7. PI, “Collaborative Research: AI-Driven Multi-Scale Design of Materials under Processing Constraints”, National Science Foundation (NSF), $379,022 (Total $651,462), 2021-2025. (Lead PI: Pinar Acar) [CMMI-2053929]

  8. Co-PI, “RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence”, Department of Energy (DOE), $650,000 (Total $28,750,000), 2020-2025. (PI: Wei-keng Liao; Lead PI: Rob Ross) [DE-SC0021399]

  9. PI, “Center for Hierarchical Materials Design (CHiMaD): Phase II” (Agrawal Subproject), National Institute of Standards and Technology (NIST), $953,221 (Total $25,000,000), 2019-2023. (Lead PI: Peter Voorhees) [70NANB19H005]

  10. PI, “Data-driven Analytics for Understanding Materials Properties”, Toyota Motor Corporation, $300,000, 2019-2020.

  11. PI, “Digital Innovation Design (DID)”, Defense Logistics Agency via Steel Founders Society of America, $200,000, 2019-2020. (Original PI: Greg Olson)

  12. Co-PI, “PROTEUS: Machine Learning Driven Resilience for Extreme-scale Systems”, Department of Energy (DOE), $1,248,115, 2018-2023. (PI: Alok Choudhary) [DE-SC0019358]

  13. PI, “The investigation of machine learning for material development”, Toyota Motor Corporation, $200,000, 2017-2018.

  14. Senior Personnel, “BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, & Disseminate”, National Science Foundation (NSF), $123,847 (Total $989,700), 2017-2022. (PI: Peter Voorhees; Lead PI: Ian Foster) [IIS-1636909]

  15. Co-PI, “Scalable, In-situ Clustering and Data Analysis for Extreme Scale Scientific Computing”, Department of Energy (DOE), $1,219,899, 2015-2021. (PI: Alok Choudhary) [DE-SC0014330]

  16. Co-PI, “SHF:Medium:Collaborative Research: Scalable Algorithms for Spatio-temporal Data Analysis”, National Science Foundation (NSF), $709,342 (Total $934,342), 2014-2019. (PI: Alok Choudhary) [CCF-1409601]

  17. PI, “Advanced Materials Center for Excellence: Center for Hierarchical Materials Design (CHiMaD)” (Agrawal Subproject), National Institute of Standards and Technology (NIST), $505,358 (Total $25,000,000), 2014-2018. (Lead PI: Peter Voorhees) [70NANB14H012]

  18. PI, “Social Media mining of caregiver experiences: Opportunity for preventing caregiver burnout”, Northwestern Data Science Initiative, $25,000, 2017-2017.

  19. PI, “Data-driven analytics for understanding processing-structure-property-performance relationships in steel alloys”, Northwestern Data Science Initiative, $45,000, 2016-2017.

  20. Co-PI, “Scaling up the screening of molecular networks in the rational design of optically active materials”, Northwestern Data Science Initiative, $9,000 (Total $45,000), 2016-2017. (PI: Kevin Kohlstedt)

  21. PI, “Analyzing caregiving experience on Twitter”, Feinberg School of Medicine, $14,246, 2015-2016.

  22. Co-PI, “SIMPLEX: Data-driven Discovery of Novel Thermoelectric Materials”, Defense Advanced Research Projects Agency (DARPA), $601,250 (Total $1,559,999), 2015-2018. (PI: Alok Choudhary; Lead PI: Greg Olson) [N66001-15-C-4036]

  23. Co-PI, “EAGER: Scalable Big Data Analytics”, National Science Foundation (NSF), $300,000, 2013-2016. (PI: Alok Choudhary) [IIS-1343639]

  24. Co-PI, “MURI: MANAGING THE MOSAIC OF MICROSTRUCTURE: Image analysis, data structures, mathematical theory of microstructure, and hardware for the structure-property relationship”, Air Force Office of Scientific Research (AFOSR), Department of Defense (DOD), $750,000 (Total $5,658,616), 2012-2018. (PI: Alok Choudhary; Lead PI: Marc De Graef) [FA9550-12-1-0458]

  25. Co-PI, “Scalable Data Management, Analysis, and Visualization (SDAV) Institute”, Department of Energy (DOE), $750,000 (Total $25,000,000), 2012-2019. (PI: Alok Choudhary; Lead PI: Arie Shoshani) [DE-SC0007456]

  26. Senior Researcher, “Expeditions in Computing: Understanding Climate Change: A Data Driven Approach”, National Science Foundation (NSF), $900,000 (Total $10,000,000), 2010-2016. (PI: Alok Choudhary; Lead PI: Vipin Kumar) [CCF-1029166]

  27. Co-PI, “EAGER: Discovering Knowledge from Scientific Research Networks”, National Science Foundation (NSF), $256,000, 2011-2014. (PI: Alok Choudhary) [ACI-1144061]

  28. Research Participant, “Scalable and Power Efficient Data Analytics for Hybrid Exascale Systems”, Department of Energy (DOE), $705,000, 2010-2014. (PI: Alok Choudhary) [DE-SC0005340]

Selected Publications

  1. Y. Li, V. Gupta, M. N. T. Kilic, K. Choudhary, D. Wines, W. Liao, A. Choudhary, and A. Agrawal, “Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction,” Digital Discovery, vol. 4, pp. 376–383, 2025. [url] [bib]

    @article{LGK25,
  title = {Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction},
  author = {Li, Youjia and Gupta, Vishu and Kilic, Muhammed Nur Talha and Choudhary, Kamal and Wines, Daniel and Liao, W. and Choudhary, Alok and Agrawal, Ankit},
  journal = {Digital Discovery},
  volume = {4},
  pages = {376-383},
  year = {2025},
  publisher = {RSC}
}

  2. V. Gupta, K. Choudhary, B. DeCost, F. Tavazza, C. Campbell, W. Liao, A. Choudhary, and A. Agrawal, “Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets,” npj Computational Materials, vol. 10, p. 1, 2024. [url] [bib]

    @article{GCD24,
  author = {Gupta, Vishu and Choudhary, Kamal and DeCost, Brian and Tavazza, Francesca and Campbell, Carelyn and Liao, W. and Choudhary, Alok and Agrawal, Ankit},
  title = {Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets},
  journal = {npj Computational Materials},
  year = {2024},
  volume = {10},
  pages = {1}
}

  3. V. Gupta, K. Choudhary, Y. Mao, K. Wang, F. Tavazza, C. Campbell, W. Liao, A. Choudhary, and A. Agrawal, “MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction,” Journal of Chemical Information and Modeling, vol. 63, no. 7, pp. 1865–1871, 2023. [url] [bib]

    @article{GCM23,
  author = {Gupta, Vishu and Choudhary, Kamal and Mao, Yuwei and Wang, Kewei and Tavazza, Francesca and Campbell, Carelyn and Liao, W. and Choudhary, Alok and Agrawal, Ankit},
  title = {MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction},
  journal = {Journal of Chemical Information and Modeling},
  year = {2023},
  volume = {63},
  number = {7},
  pages = {1865-1871}
}

  4. V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Evolution of artificial intelligence for application in contemporary materials science,” MRS Communications, pp. 1–10, 2023. [url] [bib]

    @article{GLC23b,
  author = {Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit},
  title = {Evolution of artificial intelligence for application in contemporary materials science},
  journal = {MRS Communications},
  year = {2023},
  pages = {1-10}
}

  5. Y. Mao, M. Hasan, A. Paul, V. Gupta, K. Choudhary, F. Tavazza, W. Liao, A. Choudhary, P. Acar, and A. Agrawal, “An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems,” npj Computational Materials, vol. 9, p. 111, 2023. [url] [bib]

    @article{MHP23,
  author = {Mao, Yuwei and Hasan, Mahmudul and Paul, Arindam and Gupta, Vishu and Choudhary, Kamal and Tavazza, Francesca and Liao, W. and Choudhary, Alok and Acar, Pinar and Agrawal, Ankit},
  title = {An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems},
  journal = {npj Computational Materials},
  year = {2023},
  volume = {9},
  pages = {111}
}

  6. Y. Mao, H. Lin, C. X. Yu, R. Frye, D. Beckett, K. Anderson, L. Jacquemetton, F. Carter, Z. Gao, W. Liao, A. N. Choudhary, K. Ehmann, and A. Agrawal, “A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures,” Journal of Intelligent Manufacturing, vol. 24, pp. 315–329, 2023. [url] [bib]

    @article{MLY23,
  author = {Mao, Yuwei and Lin, Hui and Yu, Christina Xuan and Frye, Roger and Beckett, Darren and Anderson, Kevin and Jacquemetton, Lars and Carter, Fred and Gao, Zhangyuan and Liao, W. and Choudhary, Alok N. and Ehmann, Kornel and Agrawal, Ankit},
  title = {A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures},
  journal = {Journal of Intelligent Manufacturing},
  year = {2023},
  volume = {24},
  pages = {315-329}
}

  7. V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “BRNet: Branched Residual Network for Fast and Accurate Predictive Modeling of Materials Properties,” in Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), 2022, pp. 343–351. [url] [bib]

    @inproceedings{GLC22,
  author = {Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit},
  title = {BRNet: Branched Residual Network for Fast and Accurate Predictive Modeling of Materials Properties},
  booktitle = {Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)},
  pages = {343-351},
  year = {2022}
}

  8. D. Jha, V. Gupta, W. Liao, A. Choudhary, and A. Agrawal, “Moving closer to experimental level materials property prediction using AI,” Scientific Reports, vol. 12, p. 11953, 2022. [url] [bib]

    @article{JGL22,
  author = {Jha, Dipendra and Gupta, Vishu and Liao, W. and Choudhary, Alok and Agrawal, Ankit},
  title = {Moving closer to experimental level materials property prediction using AI},
  journal = {Scientific Reports},
  year = {2022},
  volume = {12},
  pages = {11953}
}

  9. Y. Mao, Z. Yang, D. Jha, A. Paul, W. Liao, A. Choudhary, and A. Agrawal, “Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design,” Integrating Materials and Manufacturing Innovation, vol. 11, pp. 637–647, 2022. [url] [bib]

    @article{MYJ22,
  author = {Mao, Yuwei and Yang, Zijiang and Jha, Dipendra and Paul, Arindam and Liao, W. and Choudhary, Alok and Agrawal, Ankit},
  title = {Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design},
  journal = {Integrating Materials and Manufacturing Innovation},
  year = {2022},
  volume = {11},
  pages = {637-647}
}

  10. V. Gupta, K. Choudhary, F. Tavazza, C. Campbell, W. Liao, A. Choudhary, and A. Agrawal, “Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data,” Nature Communications, vol. 12, no. 6595, 2021. [url] [bib]

    @article{GCT21,
  author = {Gupta, Vishu and Choudhary, Kamal and Tavazza, Francesca and Campbell, Carelyn and Liao, W. and Choudhary, Alok and Agrawal, Ankit},
  title = {Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data},
  journal = {Nature Communications},
  year = {2021},
  volume = {12},
  number = {6595}
}

  11. Z. Yang, S. Papanikolaou, A. C. E. Reid, W. Liao, A. N. Choudhary, C. Campbell, and A. Agrawal, “Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations,” Scientific Reports, vol. 10, no. 8262, 2020. [url] [bib]

    @article{YPR20,
  author = {Yang, Zijiang and Papanikolaou, Stefanos and Reid, Andrew C. E. and Liao, W. and Choudhary, Alok N. and Campbell, Carelyn and Agrawal, Ankit},
  title = {Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations},
  journal = {Scientific Reports},
  year = {2020},
  volume = {10},
  number = {8262}
}

  12. A. Agrawal and A. Choudhary, “Deep materials informatics: Applications of deep learning in materials science,” MRS Communications, vol. 9, no. 3, pp. 779–792, 2019. [url] [bib]

    @article{AC19,
  author = {Agrawal, Ankit and Choudhary, Alok},
  title = {Deep materials informatics: Applications of deep learning in materials science},
  journal = {MRS Communications},
  year = {2019},
  volume = {9},
  number = {3},
  pages = {779-792}
}

  13. D. Jha, L. Ward, Z. Yang, C. Wolverton, I. Foster, W. Liao, A. Choudhary, and A. Agrawal, “IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery,” in Proceedings of 25th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2019, pp. 2385–2393. [url] [bib]

    @inproceedings{JWY19,
  author = {Jha, Dipendra and Ward, Logan and Yang, Zijiang and Wolverton, Christopher and Foster, Ian and Liao, W. and Choudhary, Alok and Agrawal, Ankit},
  title = {IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery},
  booktitle = {Proceedings of 25th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD)},
  pages = {2385-2393},
  year = {2019}
}

  14. Z. Yang, Y. C. Yabansu, D. Jha, W. Liao, A. N. Choudhary, S. R. Kalidindi, and A. Agrawal, “Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches,” Acta Materialia, vol. 166, pp. 335–345, 2019. [url] [bib]

    @article{YYJ19,
  author = {Yang, Zijiang and Yabansu, Yuksel C. and Jha, Dipendra and Liao, W. and Choudhary, Alok N. and Kalidindi, Surya R. and Agrawal, Ankit},
  title = {Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches},
  journal = {Acta Materialia},
  year = {2019},
  volume = {166},
  pages = {335-345}
}

  15. M. K. Danilovich, J. Tsay, R. Al-Bahrani, A. Choudhary, and A. Agrawal, “#Alzheimer’s and Dementia: Expressions of Memory Loss on Twitter,” Topics in Geriatric Rehabilitation, vol. 34, pp. 48–53, 2018. [url] [bib]

    @article{DTA18,
  author = {Danilovich, Margaret K. and Tsay, Jonathan and Al-Bahrani, Reda and Choudhary, Alok and Agrawal, Ankit},
  title = {#Alzheimer's and Dementia: Expressions of Memory Loss on Twitter},
  journal = {Topics in Geriatric Rehabilitation},
  year = {2018},
  volume = {34},
  pages = {48-53}
}

  16. D. Jha, L. Ward, A. Paul, W. Liao, A. Choudhary, C. Wolverton, and A. Agrawal, “ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition,” Scientific Reports, vol. 8, no. 17593, 2018. [url] [bib]

    @article{JWP18,
  author = {Jha, Dipendra and Ward, Logan and Paul, Arindam and Liao, W. and Choudhary, Alok and Wolverton, Chris and Agrawal, Ankit},
  title = {ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition},
  journal = {Scientific Reports},
  year = {2018},
  volume = {8},
  number = {17593}
}

  17. K. Gopalakrishnan, S. K. Khaitan, A. Choudhary, and A. Agrawal, “Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection,” Construction and Building Materials, vol. 157, pp. 322–330, 2017. [url] [bib]

    @article{GKC17,
  author = {Gopalakrishnan, Kasthurirangan and Khaitan, Siddhartha K. and Choudhary, Alok and Agrawal, Ankit},
  title = {Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection},
  journal = {Construction and Building Materials},
  year = {2017},
  volume = {157},
  pages = {322-330}
}

  18. A. Agrawal and A. Choudhary, “Perspective: Materials informatics and big data: Realization of the ‘fourth paradigm’ of science in materials science,” APL Materials, vol. 4, no. 053208, pp. 1–10, 2016. [url] [bib]

    @article{AC16APLMat,
  author = {Agrawal, Ankit and Choudhary, Alok},
  title = {Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science},
  journal = {APL Materials},
  year = {2016},
  pages = {1-10},
  volume = {4},
  number = {053208}
}

  19. A. Agrawal, M. Patwary, W. Hendrix, W. Liao, and A. Choudhary, “High performance big data clustering,” in Advances in Parallel Computing, Volume 23: Cloud Computing and Big Data, L. Grandinetti, Ed. IOS Press, 2013, pp. 192–211. [url] [bib]

    @inbook{APH13,
  author = {Agrawal, Ankit and Patwary, Mostofa and Hendrix, William and Liao, W. and Choudhary, Alok},
  title = {High performance big data clustering},
  editor = {Grandinetti, Lucio},
  booktitle = {Advances in Parallel Computing, Volume 23: Cloud Computing and Big Data},
  publisher = {IOS Press},
  year = {2013},
  pages = {192-211}
}

  20. J. S. Mathias, A. Agrawal, J. Feinglass, A. J. Cooper, D. W. Baker, and A. Choudhary, “Development of a 5 year life expectancy index in older adults using predictive mining of electronic health record data,” Journal of the American Medical Informatics Association, vol. 20, pp. e118–e124, 2013. JSM and AA are co-first authors. [url] [bib]

    @article{MAF13,
  title = {Development of a 5 year life expectancy index in older adults using predictive mining of electronic health record data},
  author = {Mathias, Jason Scott and Agrawal, Ankit and Feinglass, Joe and Cooper, Andrew J and Baker, David William and Choudhary, Alok},
  journal = {Journal of the American Medical Informatics Association},
  year = {2013},
  volume = {20},
  pages = {e118-e124},
  publisher = {BMJ Publishing Group Ltd},
  note = {**JSM and AA are co-first authors**}
}

  21. A. Agrawal and X. Huang, “Pairwise Statistical Significance of Local Sequence Alignment Using Sequence-Specific and Position-Specific Substitution Matrices,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 1, pp. 194–205, 2011. [url] [bib]

    @article{AH11,
  author = {Agrawal, Ankit and Huang, Xiaoqiu},
  title = {Pairwise Statistical Significance of Local Sequence Alignment Using Sequence-Specific and Position-Specific Substitution Matrices},
  journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  publisher = {IEEE},
  volume = {8},
  number = {1},
  pages = {194--205},
  year = {2011}
}

Acknowledgement

My research is supported by grants from NSF, DOE, DOD (AFOSR), NIST, DARPA, DLA, NIH, Toyota Motor Corporation, Northwestern Data Science Initiative, and Center for Nanocombinatorics at Northwestern University.