World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
14074
World Ranking
6687
National Ranking
2953

Ankit Agrawal publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Ankit Agrawal sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 215 publications — 52nd percentile

52% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Ankit Agrawal D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Ankit Agrawal sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 46 D-Index — 53rd percentile

53% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Ankit Agrawal is affiliated with Northwestern University in the United States. Their research spans the domains of medicine and materials science, with a particular focus on cardiology, materials chemistry, and related interdisciplinary fields.

The scientist's main fields of study include Medicine, with 178 publications. Subfields cover Cardiology and Cardiovascular Medicine, Materials Chemistry, Pulmonary and Respiratory Medicine, Epidemiology, and Surgery. This multidisciplinary range reflects an integration of clinical and materials science approaches.

Their research topics emphasize intersections between machine learning and materials science, as well as clinical cardiology. Key topics include:

  • Machine Learning in Materials Science
  • Cardiac Valve Diseases and Treatments
  • X-ray Diffraction in Crystallography
  • Pericarditis and Cardiac Tamponade
  • Cardiac Imaging and Diagnostics
  • Infective Endocarditis Diagnosis and Management
  • Electron and X-Ray Spectroscopy Techniques

Research output includes papers published in venues such as the Journal of the American College of Cardiology, arXiv, Scientific Reports, bioRxiv, and Circulation, reflecting contributions to both clinical research and computational materials science.

Selected recent papers demonstrate involvement in advanced computational methods applied to materials science and informatics:

  • "Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets," 2024, npj Computational Materials
  • "JARVIS-Leaderboard: a large scale benchmark of materials design methods," 2024, npj Computational Materials
  • "Recent advances and applications of deep learning methods in materials science," 2022, npj Computational Materials
  • "Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data," 2021, Nature Communications
  • "Enabling deeper learning on big data for materials informatics applications," 2021, Scientific Reports

Frequent co-authors collaborating with Ankit Agrawal include Alok Choudhary, Wei-keng Liao, Vishu Gupta, Allan L. Klein, and Alec Peltekian, indicating strong collaborative ties across multiple research groups and institutions.

Best Publications

  • A general-purpose machine learning framework for predicting properties of inorganic materials

    Logan Ward;Ankit Agrawal;Alok Nidhi Choudhary;Christopher M Wolverton

  • Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

    Ankit Agrawal;Alok Choudhary

  • Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

    Kasthurirangan Gopalakrishnan;Siddhartha K. Khaitan;Alok Choudhary;Ankit Agrawal

  • Recent Advances and Applications of Deep Learning Methods in Materials Science

    Kamal Choudhary;Brian DeCost;Chi Chen;Anubhav Jain

  • Classification of sentiment reviews using n-gram machine learning approach

    Abinash Tripathy;Ankit Agrawal;Santanu Kumar Rath

  • ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition.

    Dipendra Jha;Logan Ward;Arindam Paul;Wei-Keng Liao

  • Twitter Trending Topic Classification

    Kathy Lee;Diana Palsetia;Ramanathan Narayanan;Md. Mostofa Ali Patwary

  • Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

    Logan Ward;Ruoqian Liu;Amar Krishna;Vinay I. Hegde

  • JARVIS: An Integrated Infrastructure for Data-driven Materials Design

    Kamal Choudhary;Kevin F. Garrity;Andrew C. E. Reid;Brian DeCost

  • Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

    Zijiang Yang;Yuksel C. Yabansu;Reda Al-Bahrani;Wei keng Liao

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

    Dipendra Jha;Kamal Choudhary;Francesca Tavazza;Wei keng Liao

  • Deep materials informatics: Applications of deep learning in materials science

    Ankit Agrawal;Alok Choudhary

  • Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters

    Ankit Agrawal;Parijat D Deshpande;Ahmet Cecen;Gautham P Basavarsu

  • Classification of Sentimental Reviews Using Machine Learning Techniques

    Abinash Tripathy;Ankit Agrawal;Santanu Kumar Rath

  • A predictive machine learning approach for microstructure optimization and materials design

    Ruoqian Liu;Abhishek Kumar;Zhengzhang Chen;Zhengzhang Chen;Ankit Agrawal

  • Real-time disease surveillance using Twitter data: demonstration on flu and cancer

    Kathy Lee;Ankit Agrawal;Alok Choudhary

  • Microstructural Materials Design Via Deep Adversarial Learning Methodology

    Zijiang Yang;Xiaolin Li;L. Catherine Brinson;Alok N. Choudhary

  • The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

    Kamal Choudhary;Kamal Choudhary;Kevin F. Garrity;Andrew C.E. Reid;Brian DeCost

  • A new scalable parallel DBSCAN algorithm using the disjoint-set data structure

    Md. Mostofa Ali Patwary;Diana Palsetia;Ankit Agrawal;Wei-keng Liao

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

    Zijiang Yang;Yuksel C. Yabansu;Dipendra Jha;Wei keng Liao

  • Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks

    Mojtaba Mozaffar;Arindam Paul;Reda Al-Bahrani;Sarah Wolff

Frequent Co-Authors

Alok Choudhary
Alok Choudhary Northwestern University
Wei-keng Liao
Wei-keng Liao Northwestern University
Yu Cheng
Yu Cheng Microsoft (United States)
Zijiang Yang
Zijiang Yang Western Michigan University
Jane Cleland-Huang
Jane Cleland-Huang University of Notre Dame
Chris Wolverton
Chris Wolverton Northwestern University
Surya R. Kalidindi
Surya R. Kalidindi Georgia Institute of Technology
Ian Foster
Ian Foster University of Chicago
Arindam Banerjee
Arindam Banerjee University of Illinois at Urbana-Champaign
Walter J. Scheirer
Walter J. Scheirer University of Notre Dame

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