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Wenwei Wang

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Unclaimed · AI-structured profileOpenAlex

Early-career ML researcher of tabular foundation models, in-context learning, and glucose representation transfer

3

papers here

0

reviewed by Evolve

0

first-author

0

corresponding

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works · OpenAlex

0

h-index · OpenAlex

Bibliometric figures come from OpenAlex (2026-09-28) 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: insufficient dataOutputImpact: insufficient dataImpactFocus: insufficient dataFocusCollaboration: 48/100CollaborationActivity: 40/100ActivityReproducibility: insufficient dataReproducibility

Output—

Output cannot be scored: the snapshot shows an h-index and i10-index of zero with no full publication count beyond three recent works. This axis would measure cumulative publication volume, venue tier, and first or corresponding-author share.

Impact—

Impact is not measurable: with an h-index and i10-index of zero and no citation counts in the snapshot, influence on the literature cannot be quantified. This axis would measure citations, citing works, and downstream uptake of the three 2026 preprints.

Focus—

Focus cannot be scored: OpenAlex assigns five topics — neural networks, information retrieval, diabetes management, graph networks, few-shot learning — each with a count of one, too sparse for a concentration score. This axis would measure topical concentration across the record.

Collaboration48/100

With a collaboration score of 48, Wang sits mid-range: the snapshot records 10 distinct coauthors across a three-work recent window, and all three catalog papers carry a middle-author position rather than first or corresponding authorship. Frequent partners include Tian Zhou, Bingqing Peng, Linxiao Yang, and Xue Wang, indicating steady team-based work within one or two groups rather than a broad independent network.

Activity40/100

Activity scores 40 on a narrow base: countsByYear shows three works in 2026 and nothing earlier in the snapshot, and recent3yWorks is 3, so the record begins effectively this year. The cadence suggests an emerging researcher whose sustained annual output cannot yet be judged from a single publication year.

Reproducibility—

Reproducibility is unscored because no Evolve review verdicts, repro-readiness ratings, code links, or artifact statements exist for the three arXiv preprints. This axis would measure whether released code, data, and evaluation protocols allow independent replication of results.

Research summary

Wenwei Wang is an early-stage machine-learning researcher whose visible output centers on tabular foundation models and their transfer to physiological data. The Evolve catalog lists three arXiv preprints, all from 2026, in each of which Wang holds a middle-author position rather than first or corresponding authorship. Two probe what tabular foundation models compute internally: Support-Compiled Feature Folding pursues more evidence at lower memory across tabular foundation models, while the in-context study characterizes in-situ representation refinement through attention-gated updates. The third, Transferable Evidence Reconstruction for Longitudinal Glucose Representations, extends this representation-learning agenda toward diabetes management. OpenAlex topic assignments mirror that spread — advanced neural network applications, graph neural networks, domain adaptation and few-shot learning, information retrieval, and diabetes management each appear exactly once — indicating breadth rather than an established specialization. Bibliometric signals remain thin: the snapshot reports an h-index and i10-index of zero, three works in the recent three-year window, and ten distinct coauthors, producing collaboration (48) and activity (40) scores in the mid-range while output, impact, focus, and reproducibility axes are left unscored for insufficient data.

Collaboration pattern

Wang collaborates within a compact team: 10 distinct coauthors appear across the three 2026 works, and every catalog paper lists Wang as a middle author with no corresponding role. Recurring names include Linxiao Yang (listed twice among frequent partners), Tian Zhou, Bingqing Peng, Mengni Ye, Beverly Jin, Jinjie Gu, Liang Sun, Xue Wang, and Zuyi Zhu. The 48 collaboration score reflects a moderate-sized, consistent co-author circle rather than an expansive network.

Research keywords

Tabular foundation modelsIn-context learningRepresentation learningAttention mechanismsGraph neural networksDomain adaptationFew-shot learningTransfer learningInformation retrievalLongitudinal glucose modelingDiabetes managementMemory-efficient inference

Highlights

  • Support-Compiled Feature Folding: More Evidence at Lower Memory Across Tabular Foundation Models

    A 2026 arXiv preprint introducing a support-compiled feature folding approach that pursues more evidence at lower memory across tabular foundation models; Wang appears as a middle author in the Evolve catalog.

  • What Do Tabular Foundation Models Compute In Context? In-Situ Representation Refinement through Attention-Gated Updates

    The second 2026 preprint analyzes what tabular foundation models compute in context, characterizing in-situ representation refinement driven by attention-gated updates; it shares the tabular foundation-model line with the folding work.

  • Transferable Evidence Reconstruction for Longitudinal Glucose Representations

    A 2026 preprint extending the representation-learning agenda to physiological data by reconstructing transferable evidence for longitudinal glucose representations; the OpenAlex topic list also assigns diabetes management to Wang.

Timeline

  1. 2026Co-authored 'Support-Compiled Feature Folding', targeting more evidence at lower memory across tabular foundation models (arXiv, middle author).
  2. 2026Co-authored 'What Do Tabular Foundation Models Compute In Context?', characterizing in-situ representation refinement through attention-gated updates.
  3. 2026Co-authored 'Transferable Evidence Reconstruction for Longitudinal Glucose Representations', extending the agenda toward glucose and diabetes-related signals.

Papers in the Evolve catalog

Frequent co-authors

Tian Zhou×3Bingqing Peng×3Mengni Ye×3Beverly Jin×3Jinjie Gu×3Liang Sun×3Linxiao Yang×2Xue Wang×2Zuyi Zhu×1Linxiao Yang×1

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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.

  • All bibliometrics come from a single OpenAlex snapshot; h-index and i10-index are reported as zero and only 2026 appears in countsByYear.
  • Institution, country, and ORCID are unverified; the profile is name-resolved from public bibliographic data, and 'Wenwei Wang' is a common name, so identity confidence is limited.
  • The three catalog papers are arXiv preprints with no Evolve review verdicts, repro-readiness ratings, keywords, or persistent slugs yet.
  • Output, impact, focus, and reproducibility axes are unscored in the radar because the underlying data is insufficient; no estimates are offered.
Wenwei Wang — Researcher Profile | Evolve.Pub