Xiaochuang Han
- Artificial Intelligence top 5%
- Topic Modeling 13
- Natural Language Processing Techniques 10
- Explainable Artificial Intelligence (XAI) 3
- Adversarial Robustness in Machine Learning 3
- Sentiment Analysis and Opinion Mining 1
- Language and Linguistics top 10%
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- Human-Animal Interaction Studies 2
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- Computational and Text Analysis Methods 1
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- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Jacob EisensteinYulia TsvetkovByron WallaceUmashanthi PavalanathanScott F. KieslingLuke ZettlemoyerWeijia ShiMichael Lewis
- Journals
- Computational Linguistics (1 paper)Proceedings of the ACM on Human-Computer Interaction (1 paper)Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (1 paper)
- Partner nations
- United StatesChinaMexico
In The Last Decade
Xiaochuang Han
17 papers receiving 303 citations
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 246
- Health Informatics 5
- Human-Computer Interaction 20
- Communication 23
- Language and Linguistics 22
Countries citing papers authored by Xiaochuang Han
This map shows the geographic impact of Xiaochuang Han'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 Xiaochuang Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaochuang Han more than expected).
Fields of papers citing papers by Xiaochuang Han
This network shows the impact of papers produced by Xiaochuang Han. 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 Xiaochuang Han. The network helps show where Xiaochuang Han may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xiaochuang Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 22 | |
| 4 | 2024 | 3 | |
| 5 | 2023 | 8 | |
| 6 | 2023 | 2 | |
| 7 | 2023 | 13 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 5 | |
| 10 | 2021 | 9 | |
| 11 | 2020 | 51 | |
| 12 | 2020 | 41 | |
| 13 | Unsupervised Domain Adaptation of Contextualized Embeddings: A Case Study in Early Modern English. | 2019 | 5 |
| 14 | 2019 | 85 | |
| 15 | 2019 | 3 | |
| 16 | 2018 | 41 | |
| 17 | 2018 | 16 | |
| 18 | 2018 | 12 | |
| 19 | 2018 | 7 |
About Xiaochuang Han
Xiaochuang Han is a scholar working on Artificial Intelligence, Human-Computer Interaction and General Social Sciences, having authored 19 papers that have together received 324 indexed citations. Recurring topics across this work include Topic Modeling (13 papers), Natural Language Processing Techniques (10 papers), Explainable Artificial Intelligence (XAI) (3 papers), Adversarial Robustness in Machine Learning (3 papers), Human-Animal Interaction Studies (2 papers), Computational and Text Analysis Methods (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Sentiment Analysis and Opinion Mining (1 paper). The work is most often cited by research in Artificial Intelligence (246 citations), Health Informatics (5 citations) and Human-Computer Interaction (20 citations). Xiaochuang Han has collaborated with scholars based in United States, China and Mexico. Frequent co-authors include Jacob Eisenstein, Yulia Tsvetkov, Byron Wallace, Umashanthi Pavalanathan, Scott F. Kiesling, Luke Zettlemoyer, Weijia Shi, Michael Lewis, Sachin Kumar and Wen-tau Yih. Their work appears in journals such as Computational Linguistics, Proceedings of the ACM on Human-Computer Interaction and Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies.
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.