Deepak Nathani

840 total citations · 1 hit paper
6 papers, 399 citations indexed

About

Deepak Nathani is a scholar working on Artificial Intelligence, Health Information Management and Computer Vision and Pattern Recognition. According to data from OpenAlex, Deepak Nathani has authored 6 papers receiving a total of 399 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 1 paper in Health Information Management and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Deepak Nathani's work include Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers) and Advanced Graph Neural Networks (2 papers). Deepak Nathani is often cited by papers focused on Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers) and Advanced Graph Neural Networks (2 papers). Deepak Nathani collaborates with scholars based in United States and India. Deepak Nathani's co-authors include Manohar Kaul, Jatin Chauhan, Charu Sharma, Liangming Pan, William Yang Wang, Wenda Xu, Xinyi Wang, Kalpesh Krishna, Xavier García and Partha Talukdar and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, arXiv (Cornell University) and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

In The Last Decade

Deepak Nathani

6 papers receiving 386 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Learning Attention-based Embeddings for Relation Prediction in \nKnowledge Graphs

2019 351 citations Deepak Nathani, Jatin Chauhan et al. Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Deepak Nathani United States	5	360	70	53	50	44	6	399
Charu Sharma India	4	328 0.9×	67 1.0×	54 1.0×	50 1.0×	40 0.9×	9	369
Jatin Chauhan India	3	344 1.0×	70 1.0×	52 1.0×	50 1.0×	38 0.9×	10	376
Arvind Neelakantan United States	8	607 1.7×	57 0.8×	68 1.3×	30 0.6×	44 1.0×	11	645
Alberto García-Durán Switzerland	8	397 1.1×	125 1.8×	83 1.6×	25 0.5×	54 1.2×	16	448
Zhanqiu Zhang China	4	300 0.8×	71 1.0×	47 0.9×	43 0.9×	42 1.0×	5	324
Sebastijan Dumančić Belgium	6	393 1.1×	97 1.4×	97 1.8×	16 0.3×	53 1.2×	12	428
Ben London United States	9	210 0.6×	24 0.3×	36 0.7×	67 1.3×	52 1.2×	18	263
Siwei Rao China	3	343 1.0×	104 1.5×	36 0.7×	30 0.6×	43 1.0×	6	364
Mojtaba Nayyeri Germany	10	279 0.8×	55 0.8×	55 1.0×	29 0.6×	38 0.9×	25	305

Countries citing papers authored by Deepak Nathani

Since Specialization

Citations

This map shows the geographic impact of Deepak Nathani'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 Deepak Nathani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepak Nathani more than expected).

Fields of papers citing papers by Deepak Nathani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Deepak Nathani. 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 Deepak Nathani. The network helps show where Deepak Nathani may publish in the future.

Co-authorship network of co-authors of Deepak Nathani

This figure shows the co-authorship network connecting the top 25 collaborators of Deepak Nathani. A scholar is included among the top collaborators of Deepak Nathani 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 Deepak Nathani. Deepak Nathani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Pan, Liangming, et al.. (2024). Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies. Transactions of the Association for Computational Linguistics. 12. 484–506. 24 indexed citations

2.

Vardhan, Madhurima, et al.. (2024). Large language models as synthetic electronic health record data generators. 804–810. 1 indexed citations

3.

Nathani, Deepak, et al.. (2023). MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models. 6591–6616. 4 indexed citations

4.

Krishna, Kalpesh, et al.. (2022). Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 7439–7468. 8 indexed citations

5.

Chauhan, Jatin, Deepak Nathani, & Manohar Kaul. (2020). Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures. arXiv (Cornell University). 11 indexed citations

6.

Nathani, Deepak, Jatin Chauhan, Charu Sharma, & Manohar Kaul. (2019). Learning Attention-based Embeddings for Relation Prediction in \nKnowledge Graphs. Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad). 351 indexed citations breakdown →

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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