Surya R. Kalidindi

1.0k total citations
40 papers, 762 citations indexed

About

Surya R. Kalidindi is a scholar working on Materials Chemistry, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Surya R. Kalidindi has authored 40 papers receiving a total of 762 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Materials Chemistry, 19 papers in Mechanical Engineering and 10 papers in Mechanics of Materials. Recurrent topics in Surya R. Kalidindi's work include Machine Learning in Materials Science (16 papers), Titanium Alloys Microstructure and Properties (6 papers) and Manufacturing Process and Optimization (6 papers). Surya R. Kalidindi is often cited by papers focused on Machine Learning in Materials Science (16 papers), Titanium Alloys Microstructure and Properties (6 papers) and Manufacturing Process and Optimization (6 papers). Surya R. Kalidindi collaborates with scholars based in United States, France and India. Surya R. Kalidindi's co-authors include Daniel Wheeler, Quan Qian, Jonathan Sze Choong Low, Wencong Lu, Daren Zong Loong Tan, Stéphane Bressan, Seeram Ramakrishna, Stefano Sanvito, Tong‐Yi Zhang and Brendan P. Croom and has published in prestigious journals such as Acta Materialia, Advanced Science and IEEE Signal Processing Magazine.

In The Last Decade

Surya R. Kalidindi

39 papers receiving 739 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Surya R. Kalidindi United States 14 417 321 169 120 86 40 762
Yajun Yin China 17 277 0.7× 525 1.6× 134 0.8× 81 0.7× 59 0.7× 72 841
Yiwei Dong China 15 184 0.4× 356 1.1× 94 0.6× 77 0.6× 103 1.2× 58 763
Chanwook Park South Korea 16 231 0.6× 156 0.5× 179 1.1× 51 0.4× 117 1.4× 41 743
Xiaojun Gu China 17 284 0.7× 257 0.8× 209 1.2× 110 0.9× 73 0.8× 51 802
Subhas Ganguly India 17 215 0.5× 454 1.4× 126 0.7× 19 0.2× 61 0.7× 57 727
Ahmet Cecen United States 13 700 1.7× 530 1.7× 342 2.0× 166 1.4× 112 1.3× 23 1.3k
Noah H. Paulson United States 14 262 0.6× 256 0.8× 129 0.8× 270 2.3× 60 0.7× 27 723
Ram Mohan United States 17 143 0.3× 357 1.1× 280 1.7× 84 0.7× 144 1.7× 97 837
Fenglin Guo China 19 449 1.1× 323 1.0× 712 4.2× 34 0.3× 184 2.1× 60 1.2k
Tianle Wang China 16 347 0.8× 533 1.7× 119 0.7× 36 0.3× 98 1.1× 77 1.2k

Countries citing papers authored by Surya R. Kalidindi

Since Specialization
Citations

This map shows the geographic impact of Surya R. Kalidindi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Surya R. Kalidindi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Surya R. Kalidindi more than expected).

Fields of papers citing papers by Surya R. Kalidindi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Surya R. Kalidindi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Surya R. Kalidindi. The network helps show where Surya R. Kalidindi may publish in the future.

Co-authorship network of co-authors of Surya R. Kalidindi

This figure shows the co-authorship network connecting the top 25 collaborators of Surya R. Kalidindi. A scholar is included among the top collaborators of Surya R. Kalidindi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Surya R. Kalidindi. Surya R. Kalidindi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Olsen, Daniel H., et al.. (2025). Batch active learning for microstructure–property relations in energetic materials. Mechanics of Materials. 205. 105308–105308. 1 indexed citations
2.
Choudhary, Kamal, et al.. (2025). Lean CNNs for Mapping Electron Charge Density Fields to Material Properties. Integrating materials and manufacturing innovation. 14(1). 1–13.
3.
Kalidindi, Surya R., et al.. (2024). MICRO2D: A Large, Statistically Diverse, Heterogeneous Microstructure Dataset. Integrating materials and manufacturing innovation. 13(1). 120–154. 6 indexed citations
4.
Kemp, James W., Brendan P. Croom, Zlatomir D. Apostolov, et al.. (2021). Direct ink writing of ZrB2-SiC chopped fiber ceramic composites. Additive manufacturing. 44. 102049–102049. 78 indexed citations
5.
Kacher, Josh, et al.. (2021). In Situ Transmission Electron Microscopy: Signal processing challenges and examples. IEEE Signal Processing Magazine. 39(1). 89–103. 2 indexed citations
6.
Hardin, James O., et al.. (2021). A generalizable artificial intelligence tool for identification and correction of self-supporting structures in additive manufacturing processes. Additive manufacturing. 46. 102191–102191. 24 indexed citations
7.
Kalidindi, Surya R., et al.. (2020). A Framework for the Systematic Design of Segmentation Workflows. Integrating materials and manufacturing innovation. 9(1). 70–88. 15 indexed citations
8.
9.
Yabansu, Yuksel C., et al.. (2020). High-Throughput Exploration of the Process Space in 18% Ni (350) Maraging Steels via Spherical Indentation Stress–Strain Protocols and Gaussian Process Models. Integrating materials and manufacturing innovation. 9(3). 199–212. 12 indexed citations
10.
Yabansu, Yuksel C. & Surya R. Kalidindi. (2019). Microscale volume elements and their effective/homogenized stiffness parameter for high contrast 3-D elastic composite. 1 indexed citations
11.
Mandal, Sourav, et al.. (2019). Probing Local Mechanical Properties in Polymer-Ceramic Hybrid Acetabular Sockets Using Spherical Indentation Stress-Strain Protocols. Integrating materials and manufacturing innovation. 8(3). 257–272. 7 indexed citations
12.
Yang, Zijiang, Reda Al-Bahrani, Andrew Reid, et al.. (2019). Deep learning based domain knowledge integration for small datasets: Illustrative applications in materials informatics. 1–8. 11 indexed citations
13.
Ramakrishna, Seeram, Tong‐Yi Zhang, Wencong Lu, et al.. (2018). Materials informatics. Journal of Intelligent Manufacturing. 30(6). 2307–2326. 117 indexed citations
14.
Zapiain, David Montes de Oca, et al.. (2018). Reduced-Order Microstructure-Sensitive Models for Damage Initiation in Two-Phase Composites. Integrating materials and manufacturing innovation. 7(3). 97–115. 18 indexed citations
15.
Wheeler, Daniel, et al.. (2017). Materials Knowledge Systems in Python - A Data Science Framework for Accelerated Development of Hierarchical Materials | NIST. JOM. 1 indexed citations
16.
Wheeler, Daniel, et al.. (2017). Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials. Integrating materials and manufacturing innovation. 6(1). 36–53. 83 indexed citations
17.
Wheeler, Daniel, et al.. (2017). Microstructure-based knowledge systems for capturing process-structure evolution linkages.. Acta Materialia. 21. 2 indexed citations
18.
Kalidindi, Surya R., et al.. (2016). Role of e-Collaborations in Scaling-Up Materials Innovation | NIST. MRS Bulletin. 1 indexed citations
19.
Satko, Daniel P., Joshua Shaffer, J. Tiley, et al.. (2016). Effect of microstructure on oxygen rich layer evolution and its impact on fatigue life during high-temperature application of α/β titanium. Acta Materialia. 107. 377–389. 60 indexed citations
20.
Poole, Warren J., et al.. (2016). Proceedings of the 3rd World Congress on Integrated Computational Materials Engineering (ICME 2015). Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 9 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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