Faculty Directory
Ankit Agrawal

Education

Ph.D. Computer Science, Iowa State University, Ames, IA

B.Tech. Computer Science and Engineering, Indian Institute of Technology, Roorkee, India


Research Interests

Prof. Ankit Agrawal specializes in interdisciplinary artificial intelligence (AI) and big data analytics via high performance data mining, based on a coherent integration of high performance computing and data mining to develop customized AI solutions for big data problems with real-world impact. His research has contributed to large-scale data-driven discoveries in various scientific and engineering disciplines, such as materials science, healthcare, social media, and bioinformatics.

He has co-authorized 150+ peer-reviewed publications, co-developed and released 15+ softwares, delivered 50+ invited/keynote talks at major conferences, universities, and companies all over the world, been on program committees of 40+ conferences/workshops, and served as a PI/Co-PI on 15+ sponsored projects funded by various US federal agencies (e.g., NSF, DOE, AFOSR, NIST, DARPA, DLA) as well as industry (e.g., Toyota Motor Coporation Japan).

In particular, he is one of the few computer scientists who are actively introducing AI and advanced data science techniques in the field of materials science, and has co-authored 50+ publications and led 10+ funded projects in the field of materials informatics (AI for materials). As an example, he is co-leading the AI group at the Center for Hierarchical Materials Design (CHiMaD), which is a $60 million NIST-sponsored center of excellence. He is also serving as the Editor-in-Chief of Computers, Materials & Continua.



Selected Publications

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.