Sidharth Mudgal

1.4k total citations · 1 hit paper
7 papers, 351 citations indexed

About

Sidharth Mudgal is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Networks and Communications. According to data from OpenAlex, Sidharth Mudgal has authored 7 papers receiving a total of 351 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 5 papers in Management Science and Operations Research and 2 papers in Computer Networks and Communications. Recurrent topics in Sidharth Mudgal's work include Topic Modeling (5 papers), Data Quality and Management (5 papers) and Semantic Web and Ontologies (3 papers). Sidharth Mudgal is often cited by papers focused on Topic Modeling (5 papers), Data Quality and Management (5 papers) and Semantic Web and Ontologies (3 papers). Sidharth Mudgal collaborates with scholars based in United States. Sidharth Mudgal's co-authors include AnHai Doan, Youngchoon Park, Theodoros Rekatsinas, Esteban Arcaute, Han Li, Ganesh Krishnan, Yingyu Liang, Sanjib Das, Adel Ardalan and Erik K. Paulson and has published in prestigious journals such as Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, IEEE Data(base) Engineering Bulletin and Journal of Computer Science and Cybernetics.

In The Last Decade

Sidharth Mudgal

7 papers receiving 322 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.

Deep Learning for Entity Matching

2018 282 citations Sidharth Mudgal, Han Li et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sidharth Mudgal United States	5	295	255	98	42	23	7	351
Pradap Konda United States	7	221 0.7×	234 0.9×	94 1.0×	52 1.2×	10 0.4×	10	282
Shishir Prasad United States	5	180 0.6×	180 0.7×	77 0.8×	34 0.8×	9 0.4×	10	240
Zhaoqi Chen United States	7	183 0.6×	213 0.8×	147 1.5×	49 1.2×	29 1.3×	10	316
Kedar Bellare United States	10	305 1.0×	148 0.6×	70 0.7×	34 0.8×	33 1.4×	17	351
Byung-Won On United States	6	212 0.7×	237 0.9×	99 1.0×	32 0.8×	6 0.3×	7	291
Dominique Ritze Germany	12	341 1.2×	260 1.0×	225 2.3×	73 1.7×	20 0.9×	20	439
Ian Millard United Kingdom	9	189 0.6×	86 0.3×	108 1.1×	72 1.7×	16 0.7×	20	227
Alban Galland France	5	148 0.5×	51 0.2×	103 1.1×	62 1.5×	12 0.5×	8	260
Patricia C. Arocena Canada	6	155 0.5×	157 0.6×	77 0.8×	102 2.4×	15 0.7×	11	254
Girija Limaye India	2	174 0.6×	147 0.6×	117 1.2×	51 1.2×	14 0.6×	3	230

Countries citing papers authored by Sidharth Mudgal

Since Specialization

Citations

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

Fields of papers citing papers by Sidharth Mudgal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sidharth Mudgal

This figure shows the co-authorship network connecting the top 25 collaborators of Sidharth Mudgal. A scholar is included among the top collaborators of Sidharth Mudgal 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 Sidharth Mudgal. Sidharth Mudgal 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:

7 of 7 papers shown

1.

Han, Li, et al.. (2021). DEEP LEARNING FOR SEMANTIC MATCHING: A SURVEY. Journal of Computer Science and Cybernetics. 37(4). 365–402. 1 indexed citations

2.

Park, Sunghyun, Sidharth Mudgal, Sung‐Jin Lee, et al.. (2021). A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 6054–6063. 10 indexed citations

3.

Konda, Pradap, Paul Suganthan, N. Palaniappan, et al.. (2019). Entity Matching Meets Data Science. 389–403. 12 indexed citations

4.

Doan, AnHai, Pradap Konda, Adel Ardalan, et al.. (2018). Toward a System Building Agenda for Data Integration (and Data Science).. IEEE Data(base) Engineering Bulletin. 41. 35–46. 3 indexed citations

5.

Mudgal, Sidharth, et al.. (2018). Generalizing Word Embeddings using Bag of Subwords. 601–606. 23 indexed citations

6.

Mudgal, Sidharth, Han Li, Theodoros Rekatsinas, et al.. (2018). Deep Learning for Entity Matching. 19–34. 282 indexed citations breakdown →

7.

Doan, AnHai, Adel Ardalan, Sanjib Das, et al.. (2017). Human-in-the-Loop Challenges for Entity Matching. 1–6. 20 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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