Hailun Ding: h-index, Total Citations, and Citation Map
Hailun Ding's h-index is 6 (6 i10-index, 317+ total citations across 13+ publications) according to Google Scholar as of May 2026. Hailun Ding is affiliated with IBM Research.
Hailun Ding is a researcher affiliated with IBM Research, specializing in LLM, AI, Intrusion detection. Their work has been cited 317 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Hailun Ding's Citation Metrics
Bibliometric impact based on 13 indexed publications.
- H-Index
- 6
- i10-Index
- 6
- Total Citations
- 317
- Citing Countries
- 20
As of May 2026.
Hailun Ding has an h-index of 6 and 317 total citations across 13 publications, with research cited by institutions in 20 countries.
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Global Impact Map
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Top Cited Works
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Rethinking the reverse-engineering of trojan triggers
202280
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.
107 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered storage-efficient logging systems using representation learning, establishing a foundation for automated attack investigation and learned provenance graph storage.
The researcher developed training-time mitigation strategies to address both injected and natural backdoors, establishing a foundational approach for enhancing model robustness against diverse adversarial threats.
The researcher advanced the field of adversarial machine learning by critically re-evaluating and refining methodologies for reverse-engineering trojan triggers in neural networks.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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