Aranyak Maity: h-index, Total Citations, and Citation Map
Aranyak Maity's h-index is 8 (7 i10-index, 370+ total citations across 21+ publications) according to Google Scholar as of May 2026. Aranyak Maity is affiliated with Amazon.
Aranyak Maity is a researcher affiliated with Amazon, specializing in Artificial Intelligence, Language Models, Agentic AI. Their work has been cited 370 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Aranyak Maity's Citation Metrics
Bibliometric impact based on 21 indexed publications. Of these, 18 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 8
- i10-Index
- 7
- Total Citations
- 370
- Citing Countries
- 48
As of May 2026.
Aranyak Maity has an h-index of 8 and 370 total citations across 21 publications, with research cited by institutions in 48 countries.
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Global Impact Map
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Top Cited Works
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Power consumption analysis, measurement, management, and issues: A state-of-the-art review of smartphone battery and energy usage
2019256
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.
84 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for smartphone energy management and extended it to safety-critical cyber-physical systems, evidenced by a highly cited review and subsequent independent adoption.
The researcher developed high-fidelity fast simulation methods for human-in-the-loop systems, establishing a foundation for certified safe personalization and addressing unknown-unknowns in human-centric AI.
The researcher developed physics-guided models to detect unknown-unknowns in human-in-the-loop systems, extending this framework to cooperative game-theoretic certification for AI-enabled cyber-physical systems.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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