Carlos Hernández Ulloa: h-index, Total Citations, and Citation Map
Carlos Hernández Ulloa's h-index is 20 (31 i10-index, 1,159+ total citations across 98+ publications) according to Google Scholar as of May 2026. Carlos Hernández Ulloa is affiliated with Universidad San Sebastián.
Carlos Hernández Ulloa is a researcher affiliated with Universidad San Sebastián, specializing in Artificial Intelligence, Heuristic Search, Automated Planning. Their work has been cited 1,159 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Carlos Hernández Ulloa's Citation Metrics
Bibliometric impact based on 98 indexed publications. Of these, 19 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 20
- i10-Index
- 31
- Total Citations
- 1,159
- Citing Countries
- 18
As of May 2026.
Carlos Hernández Ulloa has an h-index of 20 and 1,159 total citations across 98 publications, with research cited by institutions in 18 countries.
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Top Cited Works
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Subgoal graphs for optimal pathfinding in eight-neighbor grids
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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.
599 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed the LRTA*(k) algorithm and extended real-time heuristic search methods to address pathfinding challenges in complex grid environments.
The researcher developed a foundational framework for efficient bi-objective search algorithms, establishing a lineage of work that significantly advanced computational methods for multi-objective optimization.
The researcher developed anytime focal search methods for multi-objective pathfinding, establishing a foundational framework that subsequent independent studies have adopted and extended.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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