Xingran Huang
University of California, Riverside · United States
UC Riverside researcher bridging AI evaluation methodology and local-first AI for equitable language learning
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-15) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Output aggregates for this profile are not yet available: OpenAlex records no reliable publication volume beyond 3 works in the recent three-year window, so total output and authorship-role trends cannot be scored yet.
Impact—
Impact data is insufficient: h-index and i10-index are recorded as 0 with no citation history, so citation influence cannot be measured yet; this axis would track citations, uptake, and scholarly reach over time.
Focus0/100
Focus scores 0, reflecting a highly dispersed early profile: field distribution splits evenly (0.5 AI-for-education, 0.5 AI evaluation methodology), and OpenAlex assigns five distinct topics with one work each — NLP techniques, low-power VLSI, human-automation interaction safety, AI ethics, and embedded systems. Concentration may emerge as output accumulates.
Collaboration39/100
Collaboration scores 39, consistent with a small early-career network: 6 distinct coauthors in the recent window, including recurring collaborators Jinwen Tang, Hung-Yu Lin, Qiming Guo, Yafu Zhong, and Xiatian Zhuang. On both reviewed 2026 papers the researcher holds middle authorship without corresponding-author role, indicating contribution within team-led projects rather than independent leadership so far.
Activity40/100
Activity scores 40, driven entirely by a recent burst: OpenAlex countsByYear shows 3 works in 2026 and no recorded output in earlier years, with 3 works over the recent three-year window. Both catalog-reviewed papers are 2026 arXiv preprints, so visible activity concentrates in a single, very recent year — a pattern typical of a researcher just entering the bibliographic record.
Reproducibility38/100
Reproducibility scores 38, a mean taken over 2 reproducibility-assessed papers. Evolve rates the measurement-validity position paper 'low' readiness — it argues about evaluation practice rather than releasing runnable artifacts — while the LLMersion framework rates 'medium', proposing a concrete local-first system on small open models whose feasibility is argued but efficacy remains untested.
Research summary
Xingran Huang is an early-career researcher at the University of California, Riverside whose visible record, name-resolved from OpenAlex bibliographic data, concentrates in 2026. Two arXiv preprints reviewed in the Evolve catalog define the profile's two halves. The first, in AI evaluation methodology, argues that compliance data — such as automated driving disengagement reports — masquerades as evaluation and cannot alone support comparative safety claims for deployed AI systems, proposing an 'evaluation contract' framing for measurement validity. The second, LLMersion, is an AI-for-education contribution: a local-first agent framework built on small open language models, intended as a free four-skill home tutor toward language-learning equity. OpenAlex field distribution splits evenly between these two areas (0.5 each), and five topics each carry a single work — NLP techniques, low-power VLSI, human-automation interaction safety, AI ethics, and embedded systems — a broad, unfocused early footprint. Bibliometric aggregates are thin: h-index and i10-index are recorded as 0, the output and impact dimensions are marked unavailable, and activity rests on 3 works within the recent three-year window, all in 2026. Collaboration involves 6 distinct coauthors, with both reviewed papers at middle authorship and no corresponding role. Evolve rates reproducibility readiness low-to-medium across the two reviewed papers (mean 38). This is an early-stage profile to revisit as the record grows.
Collaboration pattern
The collaboration footprint is small and early: 6 distinct coauthors in the recent window (collaboration score 39). Recurring collaborators include Jinwen Tang, Hung-Yu Lin, Qiming Guo, Yafu Zhong, and Xiatian Zhuang. On both reviewed 2026 arXiv papers Xingran Huang is a middle author and not the corresponding author, consistent with contributing inside team-led projects while an independent leadership record is still forming.
Research keywords
Highlights
Compliance data is not evaluation: a measurement-validity critique for deployed AI
LLMersion: local-first AI tutoring toward language-learning equity
Timeline
- 2026First catalog-reviewed work appears: a measurement-validity position paper arguing that compliance data cannot support comparative safety claims for deployed AI systems.
- 2026Publishes LLMersion, a local-first AI agent framework for low-cost home language learning toward educational equity.
- 2026OpenAlex records 3 works in this single year — the only year with recorded output so far — alongside 6 distinct coauthors.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometric figures come from OpenAlex only; with no ORCID linked, this profile is name-resolved from public bibliographic records and may merge or miss same-name works.
- Citation-based output and impact cannot be scored: h-index and i10-index are recorded as 0 with no citation history.
- Only 2 works are reviewed in the Evolve catalog, while OpenAlex records 3 works in the recent three-year window; the unreviewed work is not characterized here.
- Both reviewed papers are 2026 arXiv preprints; peer-review status and venue acceptance are unknown.