Benjamin Hillmann: h-index, Total Citations, and Citation Map
Benjamin Hillmann's h-index is 21 (23 i10-index, 29,730+ total citations across 3+ publications) according to Google Scholar as of May 2026. Benjamin Hillmann is affiliated with Graduate Student, University of Minnesota - Twin Cities.
Benjamin Hillmann is a researcher affiliated with Graduate Student, University of Minnesota - Twin Cities, specializing in Artificial Intelligence, Machine Learning, Metagenomics. Their work has been cited 29,730 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Benjamin Hillmann's Citation Metrics
Bibliometric impact based on 3 indexed publications.
- H-Index
- 21
- i10-Index
- 23
- Total Citations
- 29,730
- Citing Countries
- 26
As of May 2026.
Benjamin Hillmann has an h-index of 21 and 29,730 total citations across 3 publications, with research cited by institutions in 26 countries.
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We've mapped 5,000 of 29,730 citations for Benjamin Hillmann
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Top Cited Works
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Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
201925,340
Top Citing Countries
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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 demonstrated that US immigration significantly alters the human gut microbiome, providing foundational evidence on how environmental shifts impact microbial diversity.
The researcher developed QIIME 2, a highly cited framework enabling reproducible, interactive, scalable, and extensible microbiome data science, establishing a standard for computational analysis in the field.
The researcher established a framework for identifying personalized diet-microbiome associations through daily sampling, as demonstrated in a highly cited 2019 Cell Host and Microbe paper.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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