MJ Kearns Google Scholar: h-index, Total Citations, and Citation Map
MJ Kearns's h-index is 63 (144 i10-index, 37,470+ total citations across 322+ publications) according to Google Scholar as of November 2026. MJ Kearns is affiliated with University of Pennsylvania.
MJ Kearns is a researcher affiliated with University of Pennsylvania, specializing in AI & Machine Learning. Their work has been cited 37,470 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
MJ Kearns's Citation Metrics
Bibliometric impact based on 322 indexed publications.
- H-Index
- 63
- i10-Index
- 144
- Total Citations
- 37,470
- Citing Countries
- 71
As of November 2026.
MJ Kearns has an h-index of 63 and 37,470 total citations across 322 publications, with research cited by institutions in 71 countries.
Global Impact Map
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An Introduction to Computational Learning Theory
19942,546
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.
1295 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established foundational theoretical limits on the learnability of Boolean formulae, extending this analysis to cryptographic constraints and agnostic learning settings.
The researcher established near-optimal reinforcement learning algorithms in polynomial time, extending this framework to address censored exploration and strategic trading.
The researcher established foundational methods for efficient, noise-tolerant learning from statistical queries, a framework subsequently extended to address privacy-preserving data synthesis and microdata reconstruction.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About MJ Kearns's research
MJ Kearns is a researcher in AI & Machine Learning at University of Pennsylvania. Their work has been cited 37,470 times across 322 publications (h-index 63), according to Google Scholar.
Their most-cited work, “An Introduction to Computational Learning Theory” (1994), has accumulated 2,546 citations. Other influential works include “Algorithmic Game Theory” (2007) with 2,248 citations and “Fairness in Criminal Justice Risk Assessments: The State of the Art” (2018) with 979 citations.
Citations of MJ Kearns's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
