TUM control theorist uniting operator learning, stability of uncertain systems, and networked multi-robot control
3
papers here
1
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
50
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—
Insufficient data: raw counts (h-index 50, i10-index 222) exist in the snapshot, but no normalized output-volume score is computed. This axis would measure publication volume and productivity relative to field baselines; no estimate is offered.
Impact—
Insufficient data: no normalized citation or field-percentile metrics are available in this snapshot, beyond the raw h-index of 50 and i10-index of 222. This axis would measure citation influence relative to field and career-stage cohorts; no score is estimated.
Focus100/100
The focus score of 100 reflects a highly concentrated profile: the snapshot assigns exactly one field — systems and control theory / machine learning for dynamics — a field-distribution weight of 1.0. Topic clusters reinforce this: stability and control of uncertain systems (170 works), teleoperation and haptic systems (146), robot manipulation and learning (110), advanced control systems optimization (104), and distributed control of multi-agent systems (99).
Collaboration48/100
The collaboration axis scores 48, mid-range. The recent window records 10 distinct coauthors, and the frequent-coauthor list — Xiaobing Dai, Zewen Yang, Armin Lederer, Sihua Zhang, Lu Wan, Yang Tang, Max Beier, Nicolas Hoischen, Petar Bevanda, Wei Ren — suggests a stable core group plus a few external ties. Set against an activity score of 100, this indicates concentrated team-based collaboration rather than broadly distributed co-authorship.
Activity100/100
Activity scores 100, the axis maximum. The snapshot records 75 works in the most recent three-year window and continuous year-by-year output from 2002 through 2026, including 34 works in 2023, 32 in 2024, 23 in 2025, and 20 already in 2026. All three Evolve-catalog papers are dated 2026, consistent with an active, ongoing publishing cadence.
Reproducibility20/100
Reproducibility scores 20, computed from only 1 assessed paper (reproMean 20). The single reviewed item, the linear operator learning framework, is rated low for reproducibility readiness, and Evolve's verdict notes that it offers no experiments. Because the axis rests on one data point, treat it as provisional and early-stage rather than a verdict on the wider catalog.
Research summary
Sandra Hirche is based at the Technical University of Munich, and the OpenAlex snapshot behind this profile places her squarely in systems and control theory with machine learning for dynamics as the single assigned field (field-distribution weight 1.0). The bibliometric record is deep: an h-index of 50 and an i10-index of 222, with year-by-year counts from 2002 through 2026 that sum to 588 indexed works. Topic clusters concentrate on stability and control of uncertain systems (170 works), teleoperation and haptic systems (146), robot manipulation and learning (110), advanced control systems optimization (104), and distributed control of multi-agent systems (99). Recent activity is high — 75 works in the last three years, scoring 100 on the activity axis — and 2026 already shows three arXiv preprints in the Evolve catalog: a semigroup and inverse-problems framework for learning linear control operators (reviewed as mathematically clean but proof-light, with no rates or experiments), plus work on event-triggered control and online learning for networked systems and on asynchronous cooperative learning for multi-robot control, both under computational delays and both last-authored. Collaboration is mid-range (48) with 10 distinct recent coauthors, while the output and impact axes remain unscored for lack of normalized data, and the reproducibility score of 20 rests on a single low-readiness assessment.
Collaboration pattern
The snapshot records 10 distinct coauthors in the recent window (collaboration score 48, mid-range). The frequent-coauthor list is dominated by a core group — Xiaobing Dai, Zewen Yang, Armin Lederer, Sihua Zhang, Lu Wan, Max Beier, Nicolas Hoischen, Petar Bevanda — plus external ties to Yang Tang and Wei Ren. The 2026 catalog papers show last-author positions, consistent with senior supervision of a research group rather than wide ad-hoc collaboration.
Research keywords
Highlights
Semigroup + inverse-problems framework for learning control operators
Event-triggered control meets online learning under computational delays
A 2026 arXiv preprint (last authorship) studying event-triggered control and online learning for networked systems under computational delays, continuing the delay-aware networked-systems line. Not yet reviewed by Evolve — no verdict, keywords, or reproducibility assessment are on record.
Asynchronous cooperative online learning for multi-robot control
A second 2026 arXiv preprint (last authorship) addressing asynchronous cooperative online learning for multi-robot control under computational delays, extending the same delay-robustness agenda to multi-robot teams. Awaiting Evolve review; no verdict on record.
Timeline
- 2002Earliest works appear in the OpenAlex year-by-year counts (2 works), marking the start of the indexed publication record.
- 2010Annual indexed output reached 30 works, an early high point in the year-by-year counts.
- 2017Indexed output peaked at 43 works, the highest single-year count in the snapshot.
- 2021Recorded 34 works, part of a sustained mid-30s run through 2020–2021 (35 and 34 works).
- 2026Three arXiv preprints enter the Evolve catalog — linear operator learning, event-triggered networked control, and asynchronous multi-robot learning — within a recent three-year window of 75 works.
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, i10-index, year-by-year counts, topic clusters) come from a single OpenAlex snapshot; other databases may differ.
- Output and impact radar axes are unscored: the normalized citation data Evolve needs is absent from this snapshot.
- Only one cataloged paper carries an Evolve verdict and reproducibility rating; the other two 2026 preprints are unreviewed arXiv preprints.
- Profile is name-resolved from public bibliographic data (OpenAlex identity, ORCID 0000-0001-7819-5926); biography beyond affiliation is out of scope.