Sumit Sanghai

1.6k total citations · 1 hit paper
10 papers, 921 citations indexed

About

Sumit Sanghai is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computational Theory and Mathematics. According to data from OpenAlex, Sumit Sanghai has authored 10 papers receiving a total of 921 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 3 papers in Computational Theory and Mathematics. Recurrent topics in Sumit Sanghai's work include Bayesian Modeling and Causal Inference (3 papers), Topic Modeling (3 papers) and Rough Sets and Fuzzy Logic (3 papers). Sumit Sanghai is often cited by papers focused on Bayesian Modeling and Causal Inference (3 papers), Topic Modeling (3 papers) and Rough Sets and Fuzzy Logic (3 papers). Sumit Sanghai collaborates with scholars based in United States, India and United Kingdom. Sumit Sanghai's co-authors include Pedro Domingos, Nilesh Dalvi, Mausam Mausam, Deepak Kumar Verma, Qifan Wang, Yang Li, Prasan Roy, S. Sudarshan, Joshua Ainslie and Chris Alberti and has published in prestigious journals such as Journal of Computer and System Sciences, Journal of Artificial Intelligence Research and arXiv (Cornell University).

In The Last Decade

Sumit Sanghai

10 papers receiving 868 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.

Adversarial classification

2004 519 citations Nilesh Dalvi, Pedro Domingos et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sumit Sanghai United States	9	691	282	258	207	117	10	921
Mauro Sozio France	14	499 0.7×	369 1.3×	185 0.7×	204 1.0×	158 1.4×	33	928
Marco Barreno United States	6	936 1.4×	558 2.0×	505 2.0×	227 1.1×	80 0.7×	7	1.3k
Parag Singla India	16	893 1.3×	183 0.6×	156 0.6×	269 1.3×	146 1.2×	53	1.2k
Kannan Achan United States	13	542 0.8×	438 1.6×	242 0.9×	475 2.3×	58 0.5×	44	853
Andy Seaborne United States	6	627 0.9×	394 1.4×	134 0.5×	349 1.7×	147 1.3×	13	843
Ashish Gehani United States	15	251 0.4×	427 1.5×	238 0.9×	329 1.6×	82 0.7×	70	804
Yun Shen China	15	382 0.6×	205 0.7×	134 0.5×	173 0.8×	91 0.8×	75	764
Alex Thomo Canada	14	424 0.6×	275 1.0×	153 0.6×	167 0.8×	178 1.5×	88	729
Kemafor Anyanwu United States	12	503 0.7×	429 1.5×	186 0.7×	494 2.4×	127 1.1×	36	880
Yahiko Kambayashi Japan	14	394 0.6×	366 1.3×	228 0.9×	148 0.7×	69 0.6×	105	764

Countries citing papers authored by Sumit Sanghai

Since Specialization

Citations

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

Fields of papers citing papers by Sumit Sanghai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sumit Sanghai

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Li, Yang, et al.. (2022). MAVE. 1256–1265. 28 indexed citations

2.

Ainslie, Joshua, Santiago Ontañón, Chris Alberti, et al.. (2020). ETC: Encoding Long and Structured Inputs in Transformers. 268–284. 148 indexed citations

3.

Wang, Qifan, et al.. (2020). Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach. 47–55. 46 indexed citations

4.

Dani, Varsha, et al.. (2012). An Empirical Comparison of Algorithms for Aggregating Expert Predictions. arXiv (Cornell University). 106–113. 12 indexed citations

5.

Sanghai, Sumit, Pedro Domingos, & Daniel S. Weld. (2005). Relational Dynamic Bayesian Networks. Journal of Artificial Intelligence Research. 24. 759–797. 34 indexed citations

6.

Dalvi, Nilesh, Pedro Domingos, Mausam Mausam, Sumit Sanghai, & Deepak Kumar Verma. (2004). Adversarial classification. 99–108. 519 indexed citations breakdown →

7.

Sanghai, Sumit, Pedro Domingos, & Daniel S. Weld. (2003). Dynamic probabilistic relational models. International Joint Conference on Artificial Intelligence. 992–997. 46 indexed citations

8.

Dalvi, Nilesh, Sumit Sanghai, Prasan Roy, & S. Sudarshan. (2003). Pipelining in multi-query optimization. Journal of Computer and System Sciences. 66(4). 728–762. 43 indexed citations

9.

Domingos, Pedro, Yeuhi Abe, Corin R. Anderson, et al.. (2003). Research on Statistical Relational Learning at the University of Washington. 1 indexed citations

10.

Dalvi, Nilesh, Sumit Sanghai, Prasan Roy, & S. Sudarshan. (2001). Pipelining in multi-query optimization. 59–70. 44 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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