Jay Pujara

1.7k total citations
65 papers, 716 citations indexed

About

Jay Pujara is a scholar working on Artificial Intelligence, Management Science and Operations Research and Information Systems. According to data from OpenAlex, Jay Pujara has authored 65 papers receiving a total of 716 indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Artificial Intelligence, 16 papers in Management Science and Operations Research and 14 papers in Information Systems. Recurrent topics in Jay Pujara's work include Topic Modeling (25 papers), Natural Language Processing Techniques (14 papers) and Data Quality and Management (14 papers). Jay Pujara is often cited by papers focused on Topic Modeling (25 papers), Natural Language Processing Techniques (14 papers) and Data Quality and Management (14 papers). Jay Pujara collaborates with scholars based in United States, India and Germany. Jay Pujara's co-authors include Lise Getoor, Pigi Kouki, James Schaffer, John O’Donovan, Xiang Ren, Fred Morstatter, Pedro Szekely, Dong‐Ho Lee, William W. Cohen and Pei Zhou and has published in prestigious journals such as Proceedings of the National Academy of Sciences, IEEE Transactions on Wireless Communications and Knowledge and Information Systems.

In The Last Decade

Jay Pujara

60 papers receiving 688 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jay Pujara United States	15	488	184	140	98	42	65	716
Ming-Che Lee Taiwan	15	338 0.7×	247 1.3×	91 0.7×	64 0.7×	44 1.0×	53	709
Alexiei Dingli Malta	13	216 0.4×	140 0.8×	90 0.6×	66 0.7×	33 0.8×	69	487
László Kovács Hungary	10	288 0.6×	219 1.2×	46 0.3×	72 0.7×	98 2.3×	120	654
Elior Sulem Israel	8	611 1.3×	120 0.7×	43 0.3×	85 0.9×	35 0.8×	12	870
Chengyu Wang China	15	570 1.2×	103 0.6×	70 0.5×	140 1.4×	29 0.7×	101	771
Amir Pouran Ben Veyseh United States	10	616 1.3×	160 0.9×	40 0.3×	75 0.8×	38 0.9×	23	936
Oscar Sainz Spain	4	432 0.9×	114 0.6×	42 0.3×	66 0.7×	31 0.7×	8	686
Stefano Ferilli Italy	12	289 0.6×	149 0.8×	47 0.3×	90 0.9×	66 1.6×	98	511
Mauro Dragoni Italy	15	622 1.3×	181 1.0×	60 0.4×	46 0.5×	25 0.6×	77	825
James Michaelis United States	10	268 0.5×	155 0.8×	135 1.0×	46 0.5×	202 4.8×	37	561

Countries citing papers authored by Jay Pujara

Since Specialization

Citations

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

Fields of papers citing papers by Jay Pujara

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jay Pujara

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

All Works

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20 of 20 papers shown

1.

Pujara, Jay, et al.. (2024). Faithful Persona-based Conversational Dataset Generation with Large Language Models. 15245–15270. 2 indexed citations

2.

Jin, Woojeong, et al.. (2024). Improving Covert Toxicity Detection by Retrieving and Generating References. 266–274. 2 indexed citations

3.

Pujara, Jay, et al.. (2024). Faithful Persona-based Conversational Dataset Generation with Large Language Models. 114–139. 1 indexed citations

4.

Lee, Dong‐Ho, Woojeong Jin, Minwoo Kim, et al.. (2023). Analyzing Norm Violations in Live-Stream Chat. 852–868.

5.

Lee, Dong‐Ho, et al.. (2023). XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models. 264–273. 2 indexed citations

6.

Zhou, Pei, Jennifer J. Hu, Jay Pujara, et al.. (2023). I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons. 4 indexed citations

7.

Zhu, Xiaoyuan, et al.. (2023). Learn Your Tokens: Word-Pooled Tokenization for Language Modeling. 9883–9893. 2 indexed citations

8.

Lee, Dong‐Ho, et al.. (2023). Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning. 544–557. 13 indexed citations

9.

Zhou, Pei, et al.. (2022). Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality. 10450–10468. 8 indexed citations

10.

Ramachandran, Deepak, et al.. (2022). FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue. 10936–10953. 1 indexed citations

11.

Lee, Dong‐Ho, et al.. (2022). Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2687–2700. 47 indexed citations

12.

Mehrabi, Ninareh, Pei Zhou, Fred Morstatter, et al.. (2021). Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 5016–5033. 19 indexed citations

13.

Zhou, Pei, Karthik Gopalakrishnan, Behnam Hedayatnia, et al.. (2021). Commonsense-Focused Dialogues for Response Generation: An Empirical Study. 121–132. 15 indexed citations

14.

Pham, Tam Minh, et al.. (2021). SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables. 3543–3551. 5 indexed citations

15.

Pujara, Jay, et al.. (2019). D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF. 189–196. 4 indexed citations

16.

Garijo, Daniel, Kelly M. Cobourn, Ewa Deelman, et al.. (2018). Integrating Models Through Knowledge-Powered Data and Process Composition. AGU Fall Meeting Abstracts. 2018. 1 indexed citations

17.

Pujara, Jay, et al.. (2017). Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short. 1751–1756. 62 indexed citations

18.

Weikum, Gerhard, et al.. (2015). RELLY: Inferring Hypernym Relationships Between Relational Phrases. 971–981. 8 indexed citations

19.

Pujara, Jay, Ben London, & Lise Getoor. (2015). Budgeted online collective inference. Uncertainty in Artificial Intelligence. 712–721. 3 indexed citations

20.

Pujara, Jay, et al.. (2013). Large-Scale Knowledge Graph Identification Using PSL.. National Conference on Artificial Intelligence. 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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