ML researcher spanning open time-series foundation models and differentiable electricity-market optimization
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 (2026-10-16) 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 cannot be scored yet: the evidence base is only two 2026 arXiv preprints, with no aggregate publication count, venue history, or yearly output series in the snapshot. This axis would measure total publications and cadence over time.
Impact—
Impact cannot be scored yet: no citation counts, h-index, or downstream-usage signals are provided in the snapshot. This axis would measure citations and the influence of the work across the wider literature.
Focus0/100
Focus scores 0 because the reviewed output splits evenly across two distinct fields: machine learning (time series forecasting) and energy systems and machine learning (differentiable optimization), each at 0.5 in the field distribution. The two catalog papers sit on either side of this split — one on time-series foundation models, one on differentiable electricity-market clearing — so the profile currently reads as deliberately two-sided rather than concentrated.
Collaboration46/100
Collaboration scores 46, built on nine distinct co-authors in the snapshot. Both reviewed 2026 papers list Scholl as a middle author rather than first or corresponding, consistent with team-produced work. The frequent-coauthor list includes Arnau Quera-Bofarull, Lucas Meyer, Claudio Sole and Luca Mungo among nine named individuals, but the moderate score leaves unresolved whether these ties are deep repeats or broad one-off teams.
Activity—
Activity cannot be scored yet: both catalog entries carry the same year (2026), so no multi-year publication rhythm or recent-output trend can be established. This axis would measure publishing consistency and recency across successive years.
Reproducibility78/100
Reproducibility scores 78, the mean across exactly two reviewed papers, both rated for reproducibility readiness: the $t_0$ time-series foundation model report at high readiness (open weights, public benchmarks such as GIFT-Eval) and the differentiable electricity-market clearing paper at medium readiness (validated gradients, but only two 12-bus toy grids). A strong signal for open practice, though the base is narrow.
Research summary
Maarten P. Scholl is profiled from public bibliographic data resolved to ORCID 0000-0002-4561-401X; no institution or country is recorded in the available snapshot. The Evolve catalog currently holds two 2026 arXiv preprints on which Scholl appears as a middle-listed, non-corresponding author, and they span two adjacent but distinct areas. The first, $t_0$, reports open-weights time-series foundation models t0-alpha and t0-beta; t0-beta ranks 3rd on the GIFT-Eval and fev-bench leaderboards, with thorough evaluations but deep analysis limited to the weaker model. The second treats electricity-market clearing as a differentiable layer for gradient-based planning aimed at data-center siting; gradients are validated and results are near-optimal on two 12-bus toy grids, though scale remains unproven. Bibliometric radar signals: a reproducibility mean of 78 across the two reviewed papers, a collaboration score of 46 built on nine distinct co-authors, and a focus score of 0 reflecting an even 0.5/0.5 split across two fields. Output, impact, and activity lack sufficient data and are unscored. This is an early-stage, full-tier profile: conclusions rest on two reviewed preprints plus a thin bibliometric snapshot and should be read as provisional.
Collaboration pattern
Scholl's reviewed work is team-based: both 2026 papers list him as a middle, non-corresponding author, and the snapshot records nine distinct co-authors (collaboration score 46). The frequent-coauthor list — Arnau Quera-Bofarull, Lucas Meyer, Claudio Sole, Huikan Xiang, Nicolas Li, Lucas Franceschino, Joachim Fainberg, Geoffrey Négiar, Luca Mungo — spans both lines of work, though the input does not map individuals to specific papers.
Research keywords
Highlights
Open-weights time-series foundation model lands a top-3 leaderboard rank
Evaluation breadth outruns deep analysis in the t0 report
Electricity-market clearing made differentiable for gradient-based planning
Timeline
- 2026Appears as a middle, non-corresponding author on the arXiv preprint $t_0$, an open-weights time-series foundation model report; t0-beta ranks 3rd on GIFT-Eval/fev-bench.
- 2026Co-authors the differentiable electricity-market clearing paper for gradient-based planning, with gradients validated on two 12-bus toy grids.
- 2026Both preprints enter the Evolve review catalog with reproducibility-readiness ratings of high and medium; the bibliometric radar records nine distinct co-authors and a reproducibility mean of 78.
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 and co-author counts come from a public bibliometric snapshot only; no institution or country affiliation is recorded.
- The evidence base is two 2026 arXiv preprints reviewed by Evolve; no peer-reviewed venue record is included in the input.
- Output, impact, and activity dimensions lack sufficient underlying data and are left unscored rather than estimated.
- The profile is name-resolved from public bibliographic data via ORCID 0000-0002-4561-401X; identity beyond this identifier is not independently verified.