Theory-first researcher linking minimax statistics, online learning, and RL for supply chain decision-making
4
papers here
3
reviewed by Evolve
0
first-author
0
corresponding
—
works · OpenAlex
69
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-17) 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—
Total publication counts and full productivity aggregates are not yet available for this profile, so output is unscored. This axis would measure cumulative paper volume and how output volume evolves across the career record.
Impact—
Citation totals, field-normalized citation impact, and venue-level metrics are not yet available, so impact is unscored. This axis would measure how widely the researcher's work is cited relative to field benchmarks.
Focus0/100
The focus score of 0 reflects breadth rather than concentration: the raw topic table spreads output across five clusters — supply chain and inventory management (230), advanced bandit algorithms (108), optimization and search problems (91), graph complexity (66), and sustainable supply chain management (64) — and the recent field distribution splits evenly, one third each, across three distinct areas, spanning operations, statistics, and algorithmic economics.
Collaboration50/100
The collaboration score of 50 sits alongside 11 distinct coauthors in the recent window. The recurring roster — Haichen Hu, Shuze Daniel Liu, Claire Chen, Chutong Gao, Shangtong Zhang, Nan Lu, Ethan Lee, James M. Robins, Junwei Lu, Rui Ai, and Han Zhong — spans the machine learning theory, semiparametric statistics, and operations strands of the reviewed work. Across the four 2026 preprints the researcher holds middle or last authorship and is corresponding author on none.
Activity100/100
Activity scores 100, the maximum. The raw countsByYear series runs from 1987 through 2026 and shows a sustained recent climb: 28 works in 2019, 25 in 2020, 37 in 2025, and 52 already recorded for 2026 — the highest annual figure in the record. OpenAlex attributes 110 works to the last three years, indicating an exceptionally high and still accelerating publication tempo.
Reproducibility43/100
Reproducibility scores 43 (reproMean 43), computed from three scored papers. Evolve's readiness ratings for the reviewed preprints are medium for the contract-design, RL-inference, and OR-Transformer papers and low for the partial-linear-models lower bound, consistent with the verdicts: the theory papers report proofs with few or no experiments, and the OR-Transformer's 96% win rate is measured against a timed-out no-order MILP baseline.
Research summary
David Simchi-Levi (Purdue University West Lafayette; ORCID 0000-0002-4650-1519) is profiled here from OpenAlex bibliographic data and four arXiv preprints reviewed by Evolve. The bibliometric record spans 1987–2026, with an h-index of 69 and an i10-index of 199; 110 works fall in the last three years, and annual counts peak at 29 (2013), 28 (2019), 37 (2025), and 52 already recorded for 2026, driving an activity score of 100. Topic clustering is broad: supply chain and inventory management (230 works) leads, followed by advanced bandit algorithms (108), optimization and search problems (91), graph complexity (66), and sustainable supply chain management (64), while the recent field distribution splits evenly across theoretical statistics/ML theory, semiparametric inference for reinforcement learning, and online learning and contract design. The four reviewed 2026 preprints are theory-heavy: a sharp structure-agnostic minimax lower bound for partial linear models resolving Gu (2025)'s open problem; a sharp Θ̃(T^{m/(m+1)}) minimax regret for online contract design; Neyman-orthogonal optimal value inference for reinforcement learning; and an OR-Transformer scaling joint replenishment to 1,024 items. Reproducibility scores 43 from three scored papers; most reviewed work delivers proofs rather than experiments, and the OR-Transformer's 96% MILP win is measured against a timed-out no-order baseline.
Collaboration pattern
The collaboration score of 50 aligns with 11 distinct coauthors in the recent window. Recurring collaborators include Haichen Hu, Shuze Daniel Liu, Claire Chen, Chutong Gao, Shangtong Zhang, Nan Lu, Ethan Lee, James M. Robins, Junwei Lu, Rui Ai, and Han Zhong — a roster spanning the machine learning theory, semiparametric statistics, and operations strands of the reviewed work. Across all four 2026 preprints the researcher holds middle or last authorship and is corresponding author on none.
Research keywords
Highlights
Sharp lower bound resolves the partial-linear-models minimax problem
Minimax-optimal online contract design
Optimal value inference for reinforcement learning
OR-Transformer scales replenishment decisions to 1,024 items
Timeline
- 1987Earliest year in the OpenAlex countsByYear record (1 work); the tracked publication history begins.
- 1997An early peak with 22 works recorded in a single year.
- 2013Highest annual count before the recent surge, at 29 works.
- 201928 works recorded, beginning a sustained high-output run (25 followed in 2020).
- 202537 works recorded — the highest full-year count in the record to date.
- 202652 works recorded so far, the record's highest figure, including four arXiv preprints reviewed by Evolve across contract design, RL inference, minimax theory, and inventory RL.
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.
- Bibliometrics (h-index 69, i10-index 199, countsByYear, topics) come from OpenAlex only; identity is name-resolved from public bibliographic data rather than manually verified.
- Output and impact dimensions are unscored because citation totals and productivity aggregates were not available in the input data.
- All four reviewed papers are 2026 arXiv preprints assessed from excerpts; they may not yet be peer-reviewed, and Evolve's verdicts (including reproducibility, based on 3 scored papers) are editorial judgments.
- The focus score of 0 reflects an even three-way field split and five topic clusters in the data; it indicates breadth, not absence of research direction.