Syed Bahauddin Alam (NOT "Syed Alam"): h-index, Total Citations, and Citation Map
Syed Bahauddin Alam (NOT "Syed Alam")'s h-index is 24 (53 i10-index, 1,910+ total citations across 230+ publications) according to Google Scholar as of May 2026. Syed Bahauddin Alam (NOT "Syed Alam") is affiliated with Assistant Professor, University of Illinois Urbana-Champaign.
Syed Bahauddin Alam (NOT "Syed Alam") is a researcher affiliated with Assistant Professor, University of Illinois Urbana-Champaign, specializing in AI4Sci, AI for Energy, Digital Twins. Their work has been cited 1,910 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Syed Bahauddin Alam (NOT "Syed Alam")'s Citation Metrics
Bibliometric impact based on 230 indexed publications.
- H-Index
- 24
- i10-Index
- 53
- Total Citations
- 1,910
- Citing Countries
- 53
As of May 2026.
Syed Bahauddin Alam (NOT "Syed Alam") has an h-index of 24 and 1,910 total citations across 230 publications, with research cited by institutions in 53 countries.
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Top Cited Works
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Explainable, interpretable, and trustworthy AI for an intelligent digital twin: A case study on remaining useful life
2024187
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.
821 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced signal processing and digital twin technologies by developing non-linear down-sampling methods and extending them to explainable, generalizable AI for engineering systems.
The researcher developed a deep neural operator-driven digital twin framework for real-time nuclear energy system inference, establishing a foundational approach for virtual sensing and hybrid power prediction.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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