Gu
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RL-based industrial control and predictive-maintenance researcher; two 2026 arXiv papers; name-resolved profile
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 (2026-10-13) 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 is not scored yet: no publication-count or venue-volume data is available in the current snapshot. It would measure total research output, paper counts per year, and venue spread once a fuller bibliographic record is linked to this name.
Impact—
No citation or influence data is available for this name-resolved profile, so impact is left unscored. Once linked, this axis would measure citation counts, h-index-type influence, and uptake of the two cataloged 2026 papers by other researchers.
Focus0/100
Evolve computes a focus score of 0 from the raw field distribution, which splits exactly 0.5/0.5 between machine learning for industrial AI / predictive maintenance and reinforcement learning for industrial control. This is an even split rather than noise: one 2026 cataloged paper sits in each field. The profile therefore reads as a generalist bridging two adjacent industrial-AI areas instead of a specialist concentrated in a single line of work.
Collaboration46/100
Collaboration scores 46 on Evolve's 0-100 scale, based on 9 distinct co-authors recorded in the bibliometric snapshot. The recurring roster includes Zhengyang, Joseph E. Hernandez, Thomas Cook, John Burtenshaw, Sean Scott, and Chris Couch, suggesting team-written papers rather than solo work. Both 2026 cataloged papers list this researcher in a middle author position and not as corresponding author, consistent with a contributor role inside a small collaborative group.
Activity—
Activity is unscored because the snapshot lacks a multi-year publication timeline; only two 2026 papers are on record. With a longer record it would measure annual publishing cadence, recency of output, and sustained engagement over time.
Reproducibility38/100
Reproducibility scores 38, the mean across 2 reviewed papers (reproPapers: 2, reproMean: 38). Evolve's verdicts flag concrete gaps: the PID–TD3 hybrid paper rests on single-episode evaluation with no significance tests and an unproven MIMO premise (reproReadiness: low), while the LSTM–Transformer predictive-maintenance benchmark, despite 700+ runs, is weakened by synthetic linear targets and missing linear baselines (reproReadiness: medium). Releasing code, seeds, and statistical tests would raise this axis.
Research summary
Gu is an early-stage researcher whose documented work, name-resolved from public bibliographic data, sits at the intersection of two industrial-AI areas: machine learning for predictive maintenance in Industry 4.0 (50% of the recorded field distribution) and reinforcement learning for industrial control (the remaining 50%). The Evolve catalog contains two 2026 arXiv papers with this researcher in a middle author position, neither as corresponding author. The first proposes a hybrid controller that augments classical PID with deep reinforcement learning, using TD3-discovered Gain/Shift parameters on the Industrial Benchmark; Evolve reports the hybrid matches the best DRL agent (-203.46 vs -204.92) while noting single-episode evaluation, no significance tests, and an unproven MIMO premise. The second benchmarks hybrid deep learning architectures — LSTM combined with Transformer — for predictive maintenance, claiming noise-robustness advantages across 700+ runs; Evolve cautions that synthetic linear targets, absent significance testing, and missing linear baselines weaken the evidence. Collaboration data records 9 distinct co-authors (collaboration score 46), including Zhengyang, Joseph E. Hernandez, Thomas Cook, John Burtenshaw, Sean Scott, and Chris Couch. Reproducibility scores 38, the mean across the 2 reviewed papers. Output, impact, and activity axes remain unscored because no citation or multi-year volume data is linked to this name, making this an early-stage, narrow-scope profile.
Collaboration pattern
Collaboration is team-based: the snapshot records 9 distinct co-authors (score 46 on Evolve's 0-100 scale), with recurring names Zhengyang, Joseph E. Hernandez, Thomas Cook, John Burtenshaw, Sean Scott, and Chris Couch. On both 2026 cataloged papers this researcher appears in a middle author position and is not the corresponding author, consistent with a contributing member of a small, stable group rather than a solo or leading investigator.
Research keywords
Highlights
Hybrid PID + TD3 controller matches the best DRL agent on Industrial Benchmark
LSTM-Transformer benchmark for predictive maintenance, built on 700+ runs
Timeline
- 2026Co-authored (middle author position) an arXiv paper augmenting PID control with deep reinforcement learning on the Industrial Benchmark.
- 2026Co-authored (middle author position) an arXiv benchmark of hybrid deep learning architectures for Industry 4.0 predictive maintenance.
- 2026Both papers entered the Evolve review catalog, receiving verdicts and reproducibility-readiness ratings (low and medium).
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.
- Name is recorded only as 'Gu'; the profile is name-resolved from public bibliographic data, with no ORCID or institution linked, so same-name researchers may be merged or missed.
- Bibliometrics come from a limited snapshot: no citation counts, publication volumes, or multi-year history, which is why output, impact, and activity axes are unscored.
- Only 2 papers (both 2026 arXiv preprints) reviewed by Evolve underpin the qualitative assessments; the reproducibility score covers just these 2 papers.
- The frequent-coauthor list contains apparent name variants (e.g. 'Zhengyang' and 'Gu' recur), so collaboration counts may be inflated or distorted by name matching.