Shijian Deng: h-index, Total Citations, and Citation Map
Shijian Deng's h-index is 7 (5 i10-index, 184+ total citations across 15+ publications) according to Google Scholar as of May 2026. Shijian Deng is affiliated with The University of Texas at Dallas.
Shijian Deng is a researcher affiliated with The University of Texas at Dallas, specializing in various fields. Their work has been cited 184 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Shijian Deng's Citation Metrics
Bibliometric impact based on 15 indexed publications. Of these, 14 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 7
- i10-Index
- 5
- Total Citations
- 184
- Citing Countries
- 6
As of May 2026.
Shijian Deng has an h-index of 7 and 184 total citations across 15 publications, with research cited by institutions in 6 countries.
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Top Cited Works
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Cross modality bias in visual question answering: A causal view with possible worlds vqa
202464
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Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
143 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered audio-visual behavior recognition for autism, establishing a multimodal framework that subsequent independent studies have adopted to advance diagnostic and behavioral analysis tools.
The researcher introduced a causal framework using possible worlds to analyze and mitigate cross-modality bias in visual question answering systems.
The researcher advanced continual audio-visual sound separation, establishing a foundational framework for integrating visual cues into ongoing audio processing tasks.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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