University of Glasgow researcher in decentralised federated learning, optimal stopping and vehicular/edge computing
2
papers here
2
reviewed by Evolve
1
first-author
1
corresponding
—
works · OpenAlex
9
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-09) 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—
Not scored — this snapshot lacks a complete publication-volume record for the axis, which would measure total output and productivity relative to field norms. Only partial per-year counts are visible, so no estimate is offered.
Impact—
Not scored — field-normalised citation impact data is not yet available in this snapshot beyond a small h-index of 9. The axis would measure uptake and influence of the work against field benchmarks, so no score is estimated.
Focus0/100
Scores 0/100, the lowest axis on the radar, reflecting breadth rather than concentration: works spread across at least five OpenAlex topic clusters — autonomous vehicle technology and safety (14), robotics and sensor-based localization (11), digital transformation in industry (11), VANETs (8) and IoT/edge-fog computing (6) — and the reviewed output splits 0.5/0.5 across two adjacent machine-learning fields. Recent PROSE and SAGE papers suggest a consolidating centre of gravity in decentralised federated learning.
Collaboration22/100
Scores 22/100, indicating a small active network: only two distinct co-authors appear in the recent three-year window, with Ke Xiao and Qiyuan Wang as recurring partners. Author position on the reviewed papers — first and corresponding on PROSE, last on SAGE — suggests compact, self-led teams rather than large consortia, so the low score reflects group size more than isolation.
Activity95/100
Scores 95/100, the strongest axis: 25 works in the last three years, with per-year counts rising from 1–6 over 2007–2023 to 5 in 2022, 6 in 2023, 14 in 2025 and 8 already in 2026. The h-index of 9 and i10-index of 9 remain modest, showing the surge is recent and citations have yet to accumulate; 2026 figures cover a partial year.
Reproducibility55/100
Scores 55/100, computed from the two assessed papers (reproPapers 2, mean 55), both rated medium reproducibility readiness. The constraints are empirical: Evolve's review of PROSE notes no empirical validation at all despite mostly sound reservation-value proofs, while SAGE relies on single-run experiments with gossip-level accuracy ceilings, leaving robust, reproducible gains under-evidenced.
Research summary
Christos Anagnostopoulos is a researcher at the University of Glasgow whose recent work centres on decentralised federated learning and sequential decision theory, building on a bibliographic record that also spans vehicular networking and edge computing. The OpenAlex record shows an h-index of 9 and i10-index of 9, with output accelerating sharply: one to six works per year from 2007 to 2023, 14 works in 2025, and eight already recorded in 2026, giving 25 works over the last three years and an activity score of 95/100. Topic clusters cover autonomous vehicle technology and safety (14 works), robotics and sensor-based localization (11), digital transformation in industry (11), vehicular ad hoc networks (8), and IoT with edge/fog computing (6), while the field distribution splits evenly across two decentralised-federated-learning fields. Evolve's catalog holds two 2026 arXiv papers: PROSE, where he is first and corresponding author, develops an optimal-stopping theory of peer selection with perishable evidence, rated analytically sound but without empirical validation; SAGE, where he is last author, frames peer selection as optimal stopping with gossip fallback, theoretically sound but constrained by single-run experiments. Reproducibility averages 55/100 across the two assessed papers, both rated medium readiness, and collaboration remains narrow at two distinct recent co-authors.
Collaboration pattern
The recent record shows a deliberately compact collaboration pattern: only two distinct co-authors appear in the recent three-year window, with Ke Xiao and Qiyuan Wang recurring across works. Author positioning on the catalog papers — first and corresponding on PROSE, last on SAGE — indicates he typically leads small teams end-to-end rather than joining large consortia. The collaboration score of 22/100 therefore reflects team size rather than absence of partners.
Research keywords
Highlights
PROSE: optimal-stopping theory for peer selection with perishable evidence
SAGE: gossip-fallback peer selection framed as optimal stopping
Sharp output acceleration since 2025
Per-year OpenAlex counts jump from 6 works in 2023 and 3 in 2024 to 14 in 2025 and 8 already recorded in 2026, with 25 works in the recent three-year window and an activity score of 95/100.
Timeline
- 2007Earliest work recorded in the OpenAlex profile (1 work); activity stays sparse through the following years.
- 2018A steady but modest trickle continues, with two works recorded in 2018 after single-work years from 2015 to 2017.
- 2022Output begins to climb: five works recorded in 2022, followed by six in 2023.
- 2025Peak productivity: fourteen works recorded in 2025, the highest single-year count in the profile.
- 2026Two Evolve-reviewed arXiv preprints, PROSE and SAGE, mark the move into decentralised federated learning and optimal stopping; eight works are already recorded this year.
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 are drawn from OpenAlex only (identity: openalex); venue and citation coverage may be incomplete.
- “Christos Anagnostopoulos” is a relatively common Greek name; this profile is name-resolved from public bibliographic data, and the mixed topic profile (vehicular networks alongside federated learning) means same-name conflation cannot be fully excluded.
- The output and impact dimensions are unscored in this snapshot, and the reproducibility score rests on only two assessed papers (mean 55).
- 2026 publication counts are partial-year figures, and the two catalog papers are arXiv preprints that have not undergone peer review.