Abigail Z. Jacobs: h-index, Total Citations, and Citation Map
Abigail Z. Jacobs's h-index is 19 (22 i10-index, 2,075+ total citations across 5+ publications) according to Google Scholar as of May 2026. Abigail Z. Jacobs is affiliated with University of Michigan.
Abigail Z. Jacobs is a researcher affiliated with University of Michigan, specializing in AI Governance, Responsible AI, Computational Social Science. Their work has been cited 2,075 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Abigail Z. Jacobs's Citation Metrics
Bibliometric impact based on 5 indexed publications. Of these, 4 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 19
- i10-Index
- 22
- Total Citations
- 2,075
- Citing Countries
- 17
As of May 2026.
Abigail Z. Jacobs has an h-index of 19 and 2,075 total citations across 5 publications, with research cited by institutions in 17 countries.
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Global Impact Map
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Top Cited Works
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Measurement and Fairness
2019660
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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.
The researcher established a foundational framework for integrating measurement theory with algorithmic fairness, as evidenced by the seminal 2021 FAccT paper.
The researcher developed a method for learning latent block structure in weighted networks, establishing a foundational approach for analyzing complex network data.
The researcher developed efficient methods for inferring community structure in bipartite networks, a foundational contribution to network science evidenced by high independent citation rates.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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