Edward McFowland III: h-index, Total Citations, and Citation Map
Edward McFowland III's h-index is 14 (18 i10-index, 2,495+ total citations across 5+ publications) according to Google Scholar as of May 2026. Edward McFowland III is affiliated with Assistant Professor of Technology and Operations Management.
Edward McFowland III is a researcher affiliated with Assistant Professor of Technology and Operations Management, specializing in Machine Learning, Information Systems, Anomaly Detection. Their work has been cited 2,495 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Edward McFowland III's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 14
- i10-Index
- 18
- Total Citations
- 2,495
- Citing Countries
- 12
As of May 2026.
Edward McFowland III has an h-index of 14 and 2,495 total citations across 5 publications, with research cited by institutions in 12 countries.
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Top Cited Works
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Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality
20261,761
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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.
The researcher provided seminal field experimental evidence on how artificial intelligence impacts knowledge worker productivity and quality, establishing a foundational framework for understanding AI's role in organizational settings.
The researcher developed a fast generalized subset scan algorithm for anomalous pattern detection, establishing a foundational method for efficient statistical surveillance in machine learning.
The researcher developed a fast subset scan algorithm for multivariate event detection, establishing a computationally efficient method for identifying anomalies in complex statistical data.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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