Chenyang Xu — machine learning researcher bridging egocentric perception and clinical ML for cardiac arrest care
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-19) 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—
This axis measures total publication volume and productivity over time. The current snapshot contains no output metrics for this researcher — only two cataloged papers are visible — so the dimension is left unscored rather than estimated.
Impact—
This axis measures citation-based influence, such as citation counts and h-index. No citation data is available in the current bibliometric snapshot, so impact is not scored rather than estimated; the two 2026 preprints have no recorded citation record here.
Focus0/100
The focus axis scores 0 on the 0–100 scale, derived from a field distribution that is split exactly evenly — 50% computer vision and robotics (egocentric perception) and 50% clinical machine learning (critical care / cardiology). With only two cataloged papers, one in each field, the portfolio reads as fully dual-track rather than concentrated; the score reflects that even split and may shift as more publications are indexed.
Collaboration44/100
Collaboration scores 44/100, computed from 8 distinct coauthors across the two cataloged preprints. On both papers Xu sits in a middle author position without corresponding-author status, consistent with a contributing team member rather than a project lead. The listed frequent co-authors — Yunji Feng, Junyi Ma, Guanzhong Sun, Hesheng Wang, Jiaju Gao, Yi Zhao, Yuxi Zhou and Hao Wang — aggregate across both projects, suggesting a compact, likely lab-centered network.
Activity—
This axis measures publication cadence and research velocity across years. Only two 2026 preprints are visible, with no multi-year publication history in the snapshot, so the underlying data is insufficient to score activity; further indexing would enable this axis.
Reproducibility55/100
Reproducibility averages 55/100 across the two papers carrying a reproducibility assessment, and both are individually rated medium readiness. NeuroECG's fine-tuning pipeline (ECGFounder with gradual unfreezing) and HAP's framework with credible ablation studies support partial replication, but code and data availability are not documented in the current snapshot. The moderate score reflects methods that are describable yet not verifiable end-to-end from available materials.
Research summary
Chenyang Xu is an early-stage researcher whose visible work spans two deliberately different domains: egocentric computer vision and clinical machine learning for critical care. The cataloged record consists of two 2026 arXiv preprints, on both of which Xu appears as a middle author without corresponding-author status. The first, HAP, proposes a hand-driven active perception framework that predicts egocentric 6-DoF head motion from hand-inferred intent and occlusion graphs; Evolve's review credits credible ablations while judging gains over a constant-velocity prior thin. The second, NeuroECG, fine-tunes the ECGFounder foundation model on bedside ECG signals for EEG-free neurological prognostication after cardiac arrest, reporting an AUROC of 0.8077, though the improvement over static covariates is statistically inconclusive. Bibliometrically, the portfolio splits exactly evenly between the two fields (50% each), producing a focus score of 0 on the 0–100 scale; collaboration scores 44/100 over 8 distinct coauthors; reproducibility averages 55/100 across two medium-readiness papers. Output, impact and activity lack underlying data and are left unscored. Institution, country and ORCID are unverified; this profile is name-resolved from public bibliographic data, and Chenyang Xu is a common name.
Collaboration pattern
Chenyang Xu appears as a middle author without corresponding-author status on both cataloged papers, consistent with a contributing team member rather than a project lead. The network is compact — 8 distinct coauthors, including Yunji Feng, Junyi Ma, Guanzhong Sun, Hesheng Wang, Jiaju Gao, Yi Zhao, Yuxi Zhou and Hao Wang — aggregated across the two preprint projects. A collaboration score of 44/100 reflects the small circle; whether the same collaborators span both fields cannot be confirmed from current data.
Research keywords
Highlights
NeuroECG: EEG-free neurological prognostication after cardiac arrest
HAP: hand-driven active perception for egocentric head motion
Timeline
- 2026Posted HAP to arXiv: a hand-driven active perception framework predicting egocentric 6-DoF head motion from hand-inferred intent and occlusion reasoning.
- 2026Posted NeuroECG to arXiv: ECGFounder-based deep ECG representations for EEG-free neurological prognostication after cardiac arrest, reporting AUROC 0.8077.
- 2026Closed the year with a dual-track preprint portfolio split 50/50 between egocentric computer vision and clinical machine learning, with 8 distinct coauthors across the two projects.
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 scores derive from a narrow snapshot covering only the two cataloged 2026 arXiv preprints; other publications by this author may not be captured.
- Institution, country and ORCID are absent and unverified; the profile is name-resolved from public bibliographic data, and 'Chenyang Xu' is a common name.
- Output, impact and activity dimensions lack underlying data and are intentionally left unscored.
- Both papers are arXiv preprints; peer-review status and final venues are not reflected in the current data.