Ankit Agrawal

Research Associate Professor

Department of Electrical Engineering and Computer Science
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: ankitag AT eecs.northwestern.edu
[CUCIS] [Google Scholar] [DBLP]

Recent Highlights

Research Interests

Data Mining, High Performance Computing, Materials Informatics, Healthcare Informatics, Social Media Analytics.

Education

Professional Experience

  • Research Associate Professor, Northwestern University, Mar. 2013 - present.
  • Research Assistant Professor, Northwestern University, Sep. 2010 - Feb. 2013.
  • Postdoctoral Fellow, Northwestern University, Sep. 2009 - Aug. 2010.
  • Research Assistant, Iowa State University, Jan. 2007 - Aug. 2009.

Research Grants

  1. Co-PI, “PROTEUS: Machine Learning Driven Resilience for Extreme-scale Systems”, Department of Energy (DOE), $1,248,115, 2018-2021.

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

  3. Senior Personnel, “BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, & Disseminate”, National Science Foundation (NSF), $123,847 (Total $989,700), 2017-2020. [IIS-1636909]

  4. PI, “Advanced Materials Center for Excellence: Center for Hierarchical Materials Design (CHiMaD)”, National Institute of Standards and Technology (NIST), $474,899 (Total $25,000,000), 2014-2018. [70NANB14H012]

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

  6. Co-PI, “Scalable, In-situ Clustering and Data Analysis for Extreme Scale Scientific Computing”, Department of Energy (DOE), $1,219,899, 2015-2019. [DE-SC0014330]

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

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

  9. 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.

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

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

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

  13. 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. [FA9550-12-1-0458]

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

  15. Senior Researcher, “Expeditions in Computing: Understanding Climate Change: A Data Driven Approach”, National Science Foundation (NSF), $900,000 (Total $10,000,000), 2010-2016. [CCF-1029166]

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

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

Selected Publications

  1. A. Agrawal and A. Choudhary, “An online tool for predicting fatigue strength of steel alloys based on ensemble data mining,” International Journal of Fatigue, vol. 113, pp. 389–400, 2018. [url] [bib]

    @article{AC18,
      author = {Agrawal, Ankit and Choudhary, Alok},
      title = {An online tool for predicting fatigue strength of steel alloys based on ensemble data mining},
      journal = {International Journal of Fatigue},
      year = {2018},
      volume = {113},
      pages = {389-400}
    }
    
  2. 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}
    }
    
  3. D. Jha, S. Singh, R. Al-Bahrani, W.-keng Liao, A. N. Choudhary, M. D. Graef, and A. Agrawal, “Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials using Convolutional Neural Networks,” Microscopy and Microanalysis, vol. 24, no. 5, pp. 497–502, 2018. [url] [bib]

    @article{JSA18,
      author = {Jha, Dipendra and Singh, Saransh and Al-Bahrani, Reda and Liao, Wei-keng and Choudhary, Alok N. and Graef, Marc De and Agrawal, Ankit},
      title = {Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials using Convolutional Neural Networks},
      journal = {Microscopy and Microanalysis},
      year = {2018},
      volume = {24},
      number = {5},
      pages = {497-502}
    }
    
  4. Z. Yang, X. Li, L. C. Brinson, A. Choudhary, W. Chen, and A. Agrawal, “Microstructural Materials Design via Deep Adversarial Learning Methodology,” Journal of Mechanical Design, vol. 140, no. 11, p. 10, 2018. [url] [bib]

    @article{YLB18,
      author = {Yang, Zijiang and Li, Xiaolin and Brinson, L Catherine and Choudhary, Alok and Chen, Wei and Agrawal, Ankit},
      title = {Microstructural Materials Design via Deep Adversarial Learning Methodology},
      journal = {Journal of Mechanical Design},
      year = {2018},
      volume = {140},
      number = {11},
      pages = {10}
    }
    
  5. Z. Yang, Y. C. Yabansu, R. Al-Bahrani, W.-keng Liao, A. N. Choudhary, S. R. Kalidindi, and A. Agrawal, “Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets,” Computational Materials Science, vol. 151, pp. 278–287, 2018. [url] [bib]

    @article{YYB18,
      author = {Yang, Zijiang and Yabansu, Yuksel C. and Al-Bahrani, Reda and Liao, Wei-keng and Choudhary, Alok N. and Kalidindi, Surya R. and Agrawal, Ankit},
      title = {Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets},
      journal = {Computational Materials Science},
      year = {2018},
      volume = {151},
      pages = {278-287}
    }
    
  6. D. Jha, L. Ward, A. Paul, W.-keng Liao, A. Choudhary, C. Wolverton, and A. Agrawal, “ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition,” Nature Scientific Reports, 2018. To appear. [bib]

    @article{JWP18,
      author = {Jha, Dipendra and Ward, Logan and Paul, Arindam and Liao, Wei-keng and Choudhary, Alok and Wolverton, Chris and Agrawal, Ankit},
      title = {ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition},
      journal = {Nature Scientific Reports},
      year = {2018},
      note = {To appear},
      volume = {},
      pages = {}
    }
    
  7. 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}
    }
    
  8. R. Liu, A. Kumar, Z. Chen, A. Agrawal, V. Sundararaghavan, and A. Choudhary, “A Predictive Machine Learning Approach for Microstructure Optimization and Materials Design,” Nature Scientific Reports, vol. 5, no. 11551, 2015. [url] [bib]

    @article{LKC15,
      author = {Liu, Ruoqian and Kumar, Abhishek and Chen, Zhengzhang and Agrawal, Ankit and Sundararaghavan, Veera and Choudhary, Alok},
      title = {A Predictive Machine Learning Approach for Microstructure Optimization and Materials Design},
      journal = {Nature Scientific Reports},
      year = {2015},
      volume = {5},
      number = {11551}
    }
    
  9. A. Agrawal, P. D. Deshpande, A. Cecen, G. P. Basavarsu, A. N. Choudhary, and S. R. Kalidindi, “Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters,” Integrating Materials and Manufacturing Innovation, vol. 3, no. 8, pp. 1–19, 2014. [url] [bib]

    @article{ADC14,
      author = {Agrawal, Ankit and Deshpande, Parijat D and Cecen, Ahmet and Basavarsu, Gautham P and Choudhary, Alok N and Kalidindi, Surya R},
      title = {Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters},
      journal = {Integrating Materials and Manufacturing Innovation},
      year = {2014},
      pages = {1-19},
      volume = {3},
      number = {8}
    }
    
  10. B. Meredig, A. Agrawal, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton, “Combinatorial screening for new materials in unconstrained composition space with machine learning,” Physical Review B, vol. 89, no. 094104, pp. 1–7, 2014. BM and AA are co-first authors. [url] [bib]

    @article{MAK14,
      author = {Meredig, Bryce and Agrawal, Ankit and Kirklin, S and Saal, J E and Doak, J W and Thompson, A and Zhang, Kunpeng and Choudhary, Alok and Wolverton, Christopher},
      title = {Combinatorial screening for new materials in unconstrained composition space with machine learning},
      journal = {Physical Review B},
      year = {2014},
      number = {094104},
      volume = {89},
      pages = {1-7},
      note = {**BM and AA are co-first authors**}
    }
    
  11. 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**}
    }
    
  12. M. Patwary, D. Palsetia, A. Agrawal, W.-keng Liao, F. Manne, and A. Choudhary, “A new scalable parallel DBSCAN algorithm using the disjoint-set data structure,” in ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2012, pp. 1–11. Article No. 62. [url] [bib]

    @inproceedings{PPA12,
      title = {A new scalable parallel DBSCAN algorithm using the disjoint-set data structure},
      author = {Patwary, Mostofa and Palsetia, Diana and Agrawal, Ankit and Liao, Wei-keng and Manne, Fredrik and Choudhary, Alok},
      booktitle = {ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC)},
      pages = {1--11},
      year = {2012},
      note = {Article No. 62},
      organization = {IEEE}
    }
    
  13. 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}
    }
    
  14. A. Agrawal and X. Huang, “PSIBLAST_PairwiseStatSig: Reordering PSI-BLAST hits using pairwise statistical significance,” Bioinformatics, vol. 25, no. 8, pp. 1082–1083, 2009. [url] [bib]

    @article{AH09b,
      author = {Agrawal, Ankit and Huang, Xiaoqiu},
      title = {PSIBLAST_PairwiseStatSig: Reordering PSI-BLAST hits using pairwise statistical significance},
      journal = {Bioinformatics},
      volume = {25},
      number = {8},
      pages = {1082-1083},
      year = {2009},
      publisher = {Oxford Univ Press}
    }