Junyuan (Jason) Hong: h-index, Total Citations, and Citation Map
Junyuan (Jason) Hong's h-index is 21 (30 i10-index, 2,823+ total citations across 59+ publications) according to Google Scholar as of May 2026. Junyuan (Jason) Hong is affiliated with Massachusetts General Hospital & Harvard Med; National University of Singapore.
Junyuan (Jason) Hong is a researcher affiliated with Massachusetts General Hospital & Harvard Med; National University of Singapore, specializing in Privacy, Responsible AI, AI4Health. Their work has been cited 2,823 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Junyuan (Jason) Hong's Citation Metrics
Bibliometric impact based on 59 indexed publications.
- H-Index
- 21
- i10-Index
- 30
- Total Citations
- 2,823
- Citing Countries
- 51
As of May 2026.
Junyuan (Jason) Hong has an h-index of 21 and 2,823 total citations across 59 publications, with research cited by institutions in 51 countries.
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Global Impact Map
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Top Cited Works
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Data-free knowledge distillation for heterogeneous federated learning
20211,284
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.
874 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered data-free knowledge distillation for heterogeneous federated learning, establishing a foundational framework for privacy-preserving model aggregation that subsequent work extended to robustness and efficiency.
The researcher pioneered privacy-preserving prompt engineering for large language models, establishing a foundational framework that subsequent independent studies have widely adopted to assess and mitigate generative privacy risks.
The researcher advanced resilient and communication-efficient federated learning, subsequently extending this framework to address robustness in knowledge distillation and secure model watermarking.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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