Wesley Tansey: h-index, Total Citations, and Citation Map
Wesley Tansey's h-index is 16 (21 i10-index, 850+ total citations across 67+ publications) according to Google Scholar as of May 2026. Wesley Tansey is affiliated with Memorial Sloan Kettering Cancer Center.
Wesley Tansey is a researcher affiliated with Memorial Sloan Kettering Cancer Center, specializing in Machine Learning, Bayesian Statistics, Deep Learning. Their work has been cited 850 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Wesley Tansey's Citation Metrics
Bibliometric impact based on 67 indexed publications. Of these, 19 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 16
- i10-Index
- 21
- Total Citations
- 850
- Citing Countries
- 12
As of May 2026.
Wesley Tansey has an h-index of 16 and 850 total citations across 67 publications, with research cited by institutions in 12 countries.
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Global Impact Map
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Top Cited Works
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The Holdout Randomization Test for Feature Selection in Black Box Models
2021106
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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 developed the Holdout Randomization Test, a novel statistical framework for rigorous feature selection in black-box machine learning models.
The researcher developed a method for inferring upgrade transformations to refactor legacy application annotations, addressing critical software maintenance challenges.
The researcher developed a method for smoothing false discovery rates, establishing a foundational approach to statistical error control that has been widely adopted by independent scholars.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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