Machine learning researcher in deep RL for industrial control and hybrid deep learning for predictive maintenance.
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—
Output metrics are not yet available. This axis would measure publication volume and productivity over time — total papers, publication venues, and output trends — none of which the current Evolve snapshot covers for this profile.
Impact—
Impact metrics are not yet available. This axis would measure citation-based influence — citation counts, h-index-style indicators, and uptake by other researchers — which the current snapshot does not provide for this researcher.
Focus0/100
Focus scores 0 because the reviewed output is split evenly, at 0.5 and 0.5, between reinforcement learning for industrial control and machine learning for predictive maintenance — no single field yet dominates. The two strands are nonetheless tightly coupled around industrial AI: one paper builds a hybrid PID-plus-TD3 controller for the Industrial Benchmark, the other benchmarks LSTM-Transformer architectures for Industry 4.0 maintenance.
Collaboration46/100
Collaboration scores 46, based on 9 distinct coauthors across the two reviewed papers. Recurring partners include Zhengyang Gu and Thomas Cook, alongside Joseph E. Hernandez, John Burtenshaw, and Chris Couch. On both papers Scott sits in a middle authorship position and is not the corresponding author, suggesting contribution within a team rather than standalone or leading work.
Activity—
Activity metrics are not yet available. This axis would measure publishing cadence and recent engagement — papers per year and continuity of output over time — which cannot be computed from the two reviewed 2026 preprints alone.
Reproducibility38/100
Reproducibility scores 38, the mean readiness rating across 2 assessed papers — one rated low, one medium. Both share weaknesses flagged in Evolve's verdicts: single-episode evaluation and no significance tests on the PID-DRL paper, and synthetic linear targets plus missing linear baselines in the predictive-maintenance benchmark despite its 700+ runs.
Research summary
Sean Scott is a machine learning researcher whose visible record in Evolve's catalog rests on two 2026 arXiv preprints, split evenly at 0.5 and 0.5 between reinforcement learning for industrial control and machine learning for predictive maintenance. The first augments PID control with TD3-discovered Gain/Shift parameters, matching the best deep reinforcement learning agent on the Industrial Benchmark (-203.46 vs -204.92); Evolve notes the comparison rests on single-episode evaluation with no significance tests and an unproven MIMO premise. The second benchmarks LSTM-Transformer and other hybrid architectures for Industry 4.0 predictive maintenance across 700+ runs, claiming a noise-robustness win that is weakened by synthetic linear targets and missing linear baselines. On both papers Scott is a middle author, not corresponding author, within a network of 9 distinct coauthors. Evolve's reproducibility ratings average 38 across the two papers (one low, one medium). Output, impact, and activity metrics are not yet available, making this an early-stage profile.
Collaboration pattern
Scott's reviewed work involves 9 distinct coauthors, with Zhengyang Gu and Thomas Cook recurring in the frequent-collaborator list, alongside Joseph E. Hernandez, John Burtenshaw, and Chris Couch. Across both 2026 preprints Scott is a middle author and never the corresponding author, consistent with contributing specialist work inside small teams rather than leading group output.
Research keywords
Highlights
Hybrid PID + TD3 controller matches deep-RL agent on Industrial Benchmark
700+ run benchmark of hybrid architectures for predictive maintenance
A recurring rigor gap across both 2026 papers
Independently of topic, Evolve's verdicts on both papers cite absent statistical significance testing and incomplete baselines — a pattern that, together with the 38/100 mean reproducibility score, defines the main open question in this early-stage profile.
Timeline
- 2026Released an arXiv preprint on augmenting PID control with deep reinforcement learning (hybrid TD3 approach on the Industrial Benchmark).
- 2026Released an arXiv preprint benchmarking hybrid deep learning architectures for predictive maintenance in Industry 4.0.
- 2026Published both works as a middle author within the same year, splitting output evenly between reinforcement learning for industrial control and predictive maintenance machine learning.
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.
- Profile is name-resolved from public bibliographic data; ORCID and affiliation are absent, and 'Sean Scott' is a common name, so identity is not independently verified.
- Output, impact, and activity scores are missing because the bibliometric snapshot lacks sufficient coverage for this researcher.
- Only the two 2026 arXiv preprints reviewed by Evolve are covered; other publications, if any, are not reflected here.
- Both sources are arXiv preprints, which may not correspond to peer-reviewed published versions.