Michael J. Tumminia: h-index, Total Citations, and Citation Map
Michael J. Tumminia's h-index is 11 (12 i10-index, 983+ total citations across 4+ publications) according to Google Scholar as of May 2026. Michael J. Tumminia is affiliated with Carnegie Mellon University.
Michael J. Tumminia is a researcher affiliated with Carnegie Mellon University, specializing in Developmental Psychology, Contemplative Science, Performance Psychology. Their work has been cited 983 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Michael J. Tumminia's Citation Metrics
Bibliometric impact based on 4 indexed publications.
- H-Index
- 11
- i10-Index
- 12
- Total Citations
- 983
- Citing Countries
- 12
As of May 2026.
Michael J. Tumminia has an h-index of 11 and 983 total citations across 4 publications, with research cited by institutions in 12 countries.
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Top Cited Works
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Identifying behavioral phenotypes of loneliness and social isolation with passive sensing: statistical analysis, data mining and machine learning of smartphone and fitbit data
2019190
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Visa Evidence Package
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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 pioneered the use of passive smartphone and Fitbit sensing to identify behavioral phenotypes of loneliness and social isolation through advanced statistical and machine learning analysis.
The researcher developed a framework for detecting depression in college students by leveraging routine behavior and contextually-filtered features, establishing a foundational approach in digital mental health assessment.
The researcher developed a machine learning framework with robust feature selection to detect depression and predict its onset using longitudinal passive sensing data.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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