Tien-Ju Yang: h-index, Total Citations, and Citation Map
Tien-Ju Yang's h-index is 19 (21 i10-index, 12,471+ total citations across 44+ publications) according to Google Scholar as of June 2026. Tien-Ju Yang is affiliated with Massachusetts Institute of Technology.
Tien-Ju Yang is a researcher affiliated with Massachusetts Institute of Technology, specializing in Artificial Intelligence, Computer Vision, Neural Network. Their work has been cited 12,471 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Tien-Ju Yang's Citation Metrics
Bibliometric impact based on 44 indexed publications. Of these, 19 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 19
- i10-Index
- 21
- Total Citations
- 12,471
- Citing Countries
- 76
As of June 2026.
Tien-Ju Yang has an h-index of 19 and 12,471 total citations across 44 publications, with research cited by institutions in 76 countries.
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We've mapped 5,000 of 12,471 citations for Tien-Ju Yang
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Global Impact Map
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Efficient processing of deep neural networks: A tutorial and survey
20176,021
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.
355 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for efficient deep neural network processing, subsequently advancing mobile-specific hardware accelerators and platform-aware adaptation techniques.
The researcher established critical design and evaluation frameworks for efficient deep neural networks on processing-in-memory accelerators, challenging standard metrics and optimizing hardware efficiency.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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