Sumit Asthana: h-index, Total Citations, and Citation Map
Sumit Asthana's h-index is 10 (10 i10-index, 493+ total citations across 16+ publications) according to Google Scholar as of June 2026. Sumit Asthana is affiliated with PhD student, University of Michigan.
Sumit Asthana is a researcher affiliated with PhD student, University of Michigan, specializing in AI, HCI, machine learning. Their work has been cited 493 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Sumit Asthana's Citation Metrics
Bibliometric impact based on 16 indexed publications. Of these, 15 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 10
- i10-Index
- 10
- Total Citations
- 493
- Citing Countries
- 41
As of June 2026.
Sumit Asthana has an h-index of 10 and 493 total citations across 16 publications, with research cited by institutions in 41 countries.
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Global Impact Map
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Top Cited Works
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Understanding uncertainty: how lay decision-makers perceive and interpret uncertainty in human-AI decision making
2023124
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.
17 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for detecting synthetic media deception, subsequently extending this methodology to evaluate large language models for domain-specific concept simplification.
The researcher established a foundational framework for understanding how lay decision-makers perceive and interpret uncertainty in human-AI interactions, addressing a critical gap in trust and transparency.
The researcher developed Rex, a framework for preventing bugs and misconfigurations in large services through correlated change analysis, establishing a foundational approach to automated service reliability.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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