Aman Chadha

878 total citations
33 papers, 184 citations indexed

About

Aman Chadha is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Aman Chadha has authored 33 papers receiving a total of 184 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 5 papers in Signal Processing. Recurrent topics in Aman Chadha's work include Topic Modeling (13 papers), Natural Language Processing Techniques (11 papers) and Speech Recognition and Synthesis (6 papers). Aman Chadha is often cited by papers focused on Topic Modeling (13 papers), Natural Language Processing Techniques (11 papers) and Speech Recognition and Synthesis (6 papers). Aman Chadha collaborates with scholars based in United States, India and Bangladesh. Aman Chadha's co-authors include Amitava Das, Amit Sheth, Swagata Chakraborty, Vipula Rawte, Sriparna Saha, Vinija Jain, Arijit Chowdhury, A. Seetharama Acharya, Parth Patwa and Yixin Wan and has published in prestigious journals such as IEEE Access, International Journal of Advanced Computer Science and Applications and arXiv (Cornell University).

In The Last Decade

Aman Chadha

28 papers receiving 161 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aman Chadha United States 8 83 49 22 15 13 33 184
Annajiat Alim Rasel Bangladesh 7 73 0.9× 21 0.4× 13 0.6× 32 2.1× 15 1.2× 70 164
Yeganeh Kordi United States 1 191 2.3× 43 0.9× 11 0.5× 39 2.6× 6 0.5× 2 269
Anandhavalli Muniasamy Saudi Arabia 5 80 1.0× 18 0.4× 4 0.2× 33 2.2× 8 0.6× 17 211
Brian Dolhansky United States 6 73 0.9× 150 3.1× 28 1.3× 5 0.3× 6 0.5× 8 211
Zhourong Chen Hong Kong 7 87 1.0× 82 1.7× 18 0.8× 31 2.1× 8 0.6× 9 164
Yunlong Liang China 6 187 2.3× 32 0.7× 5 0.2× 27 1.8× 5 0.4× 17 229
Prachi Jain India 7 156 1.9× 34 0.7× 59 2.7× 17 1.1× 6 0.5× 28 220
Arjun Akula United States 8 121 1.5× 48 1.0× 4 0.2× 5 0.3× 3 0.2× 14 159
Karol Kurach United States 6 88 1.1× 51 1.0× 11 0.5× 36 2.4× 16 1.2× 8 181

Countries citing papers authored by Aman Chadha

Since Specialization
Citations

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

Fields of papers citing papers by Aman Chadha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aman Chadha

This figure shows the co-authorship network connecting the top 25 collaborators of Aman Chadha. A scholar is included among the top collaborators of Aman Chadha 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 Aman Chadha. Aman Chadha 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
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Rawte, Vipula, et al.. (2025). FACTOID: FACtual enTailment fOr hallucInation Detection. 599–617.
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Patwa, Parth, et al.. (2024). Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs. 91–98. 7 indexed citations
5.
Saha, Sriparna, et al.. (2024). A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models. 11709–11724. 9 indexed citations
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Chadha, Aman, et al.. (2024). MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention. 8084–8104. 1 indexed citations
8.
Acharya, A. Seetharama, et al.. (2024). CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare. Proceedings of the AAAI Conference on Artificial Intelligence. 38(20). 22031–22039. 12 indexed citations
9.
Chowdhury, Arijit, et al.. (2024). Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges. 411–421. 5 indexed citations
10.
Saha, Sriparna, et al.. (2024). Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development. 11214–11226. 1 indexed citations
11.
Moraffah, Raha, et al.. (2024). Causality Guided Disentanglement for Cross-Platform Hate Speech Detection. 626–635. 7 indexed citations
12.
Roosta, Tanya, et al.. (2024). AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approach. 5174–5179. 3 indexed citations
13.
Desplanques, Brecht, et al.. (2024). Post-Training Embedding Alignment for Decoupling Enrollment and Runtime Speaker Recognition Models. 10836–10840. 1 indexed citations
14.
Jamshidi, Fatemeh, Vinija Jain, Aman Chadha, et al.. (2024). OffensiveLang: A Community Based Implicit Offensive Language Dataset. IEEE Access. 1–1. 1 indexed citations
15.
Samragh, Mohammad, et al.. (2023). I See What You Hear: A Vision-Inspired Method to Localize Words. 1–5. 1 indexed citations
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Chadha, Aman, et al.. (2023). FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering. 10421–10440. 6 indexed citations
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Dave, Harshit K., et al.. (2023). FACTIFY3M: A benchmark for multimodal fact verification with explainability through 5W Question-Answering. 15282–15322. 1 indexed citations
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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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