Early-career researcher applying graph learning and NLP to literature mining and biomedical evidence synthesis
2
papers here
2
reviewed by Evolve
2
first-author
0
corresponding
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works · OpenAlex
0
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 is not yet scored: no total publication count is available beyond the 5 recent three-year works, so this axis — which would measure cumulative publication volume and productivity across the full career — currently lacks sufficient OpenAlex data.
Impact—
Impact is not yet scored: h-index and i10-index are recorded as zero with no citation distribution, likely a young-record artifact rather than measured non-influence; this axis would track citations and scholarly influence once data accrues.
Focus0/100
The focus score of 0 reflects an intentionally even split: the OpenAlex field distribution assigns 0.5 to machine learning for scientific discovery (literature mining and graph learning) and 0.5 to machine learning for biomedical evidence synthesis (NLP / knowledge graphs). Topic counts spread across five areas — scientometrics and bibliometrics (2), recommender systems (2), graph neural networks (2), meta-analysis (1), and information retrieval (1) — suggesting a two-sided agenda rather than scattered output.
Collaboration36/100
With a collaboration score of 36 and 5 distinct coauthors on record, Fred Sun works within a compact, stable team. Both 2026 catalog papers are first-authored, and the frequent-coauthor data centers on Shangqi Guo (listed twice), Jingze Wang, and Minkun Xu. The pattern suggests the researcher leads authorship while relying on a small recurring group rather than large multi-institution consortia.
Activity52/100
Activity is moderate (score 52) and highly concentrated: the OpenAlex record shows 5 works in 2026, and the recent three-year count is the same 5 works, meaning essentially all recorded output falls in the current year. Both reviewed papers are 2026 arXiv preprints, consistent with an early-stage record whose publication cadence has only just begun.
Reproducibility20/100
Reproducibility is the weakest measured axis (score 20, the mean across 2 scored papers, both rated low readiness by Evolve). The reviews nonetheless note honest, well-controlled ablations; the limiting factors are practical — a single 140-query Cochrane benchmark in the biomedical paper, and distilled labels with a single domain in the forecasting paper — so low scores reflect narrow evaluation setups, not careless method reporting.
Research summary
Fred Sun is an early-stage researcher whose visible record begins in 2026, listed at United States Satellite Laboratory (country of record: CN) and resolved from OpenAlex bibliographic data; because 'Fred Sun' is a common name, identity confidence rests on the bibliographic match alone. The profile is built around two 2026 first-authored arXiv preprints reviewed by Evolve. The first argues that deeper retrieval raises false positives on null biomedical queries and proposes principled stopping for causal knowledge-graph search, evaluated on a single 140-query Cochrane benchmark. The second aligns query-time semantics with evolving concept graphs to improve scientific relation forecasting, with well-controlled ablations but distilled labels and a single domain. This yields an even field split (0.5/0.5) between machine learning for scientific discovery and machine learning for biomedical evidence synthesis, spanning five topics from scientometrics to graph neural networks. The OpenAlex record shows 5 works in 2026 and 5 distinct coauthors, with h-index and i10-index of zero — consistent with a debut year rather than established influence. Radar scores are moderate for activity (52) and low for collaboration (36) and reproducibility (20, two papers rated low readiness), while focus scores 0 for the even two-field split. Output and impact are not yet scored. Overall, a promising but very early profile.
Collaboration pattern
The collaboration pattern is compact and stable: 5 distinct coauthors on the OpenAlex record, with recurring collaboration concentrated on Shangqi Guo (listed twice in the frequent-coauthor data), Jingze Wang, and Minkun Xu. Both 2026 catalog papers are first-authored (neither corresponding), suggesting Fred Sun leads research and writing within a small recurring team rather than large consortia. With a collaboration score of 36, the network is narrow but consistent.
Research keywords
Highlights
Principled stopping against publication-bias drift in biomedical causal search
Query-time alignment of evolving concept graphs for relation forecasting
A two-sided agenda: literature mining meets evidence synthesis
The OpenAlex field distribution splits evenly (0.5/0.5) between machine learning for scientific discovery and machine learning for biomedical evidence synthesis; both catalog papers share a first-authored, evaluation-honest style, though each is currently rated low on reproducibility readiness.
Timeline
- 2026OpenAlex record shows 5 works in 2026, marking the profile's first visible appearance in bibliographic data.
- 2026First-authored arXiv preprint on publication-bias drift and principled stopping for biomedical causal search.
- 2026First-authored arXiv preprint on time-aligned evolving concept graphs for scientific relation forecasting.
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.
- All bibliometrics come from OpenAlex only; no ORCID is linked, and 'Fred Sun' is a common name, so the record may not fully resolve this individual.
- Output and impact dimensions are unscored: beyond 5 works in 2026, no cumulative publication or citation distribution is available (h-index and i10-index are zero).
- Only two papers are in the Evolve catalog, both 2026 arXiv preprints; peer-reviewed venue status is unknown.
- Reproducibility scores cover just 2 papers, both rated low readiness; no independent code or data release information is available beyond the review verdicts.