Tianyu Yang: h-index, Total Citations, and Citation Map
Tianyu Yang's h-index is 5 (4 i10-index, 172+ total citations across 23+ publications) according to Google Scholar as of May 2026. Tianyu Yang is affiliated with University of Notre Dame.
Tianyu Yang is a researcher affiliated with University of Notre Dame, specializing in Multi-Modal Learning. Their work has been cited 172 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Tianyu Yang's Citation Metrics
Bibliometric impact based on 23 indexed publications. Of these, 18 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 5
- i10-Index
- 4
- Total Citations
- 172
- Citing Countries
- 19
As of May 2026.
Tianyu Yang has an h-index of 5 and 172 total citations across 23 publications, with research cited by institutions in 19 countries.
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Global Impact Map
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Top Cited Works
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Unimath: A foundational and multimodal mathematical reasoner
202345
Top Citing Countries
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.
40 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational multimodal mathematical reasoning framework, subsequently synthesizing the field's evolution from perception to alignment in a comprehensive survey.
The researcher established a multimodal benchmark for scientific college entrance exams and subsequently surveyed self-improvement mechanisms in multimodal large language models.
The researcher established a benchmarking framework for evaluating foundation models on university-level physics problem solving, providing a critical standard for assessing AI capabilities in complex scientific reasoning.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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