World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
14074
World Ranking
6686
National Ranking
2952

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

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science education in the USA opens many flexible learning options. Many students begin their journey with 2 year online degrees, which offer foundational knowledge and a fast path into entry-level tech jobs or further study.

For those prioritizing cost, there are also numerous affordable online degree programs. These programs allow students to earn a respected credential without accumulating excessive debt, making higher education more accessible.

Academic requirements don’t have to be a barrier. Prospective students with lower GPAs can still pursue their goals at online colleges that accept 2.0 gpa. These institutions often provide additional academic support, helping more students succeed.

A computer science degree leads to diverse careers, from software development to data analytics. If you’re considering a different discipline, it’s worth asking: what can i do with an environmental science degree? Understanding various career pathways will help you choose a degree that matches your interests.

Best Scientists Citing Ankit Agrawal

Trending Scientists