Yale researcher bridging retinal circuit genetics and privacy-preserving synthetic data for the AI era
2
papers here
2
reviewed by Evolve
1
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-02) 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 yet scored: the catalog holds only two 2026 bioRxiv preprints, with no publication counts, venue record, or productivity trend available. This axis would measure total research output and authorship roles across the full publication record.
Impact—
Not yet scored: no citation counts, h-index, or readership signals exist for these two 2026 preprints. This axis would measure citation-based influence and uptake by other researchers once citations accumulate.
Focus0/100
Scores 0/100, driven directly by the field distribution: 50% systems neuroscience (retinal circuitry) and 50% computational biology / statistical privacy. The portfolio is evenly bimodal rather than scattered — a first-authored retinal genetics preprint and a middle-authored synthetic-data paper share little methodological ground — so this axis penalizes breadth of span rather than depth of work.
Collaboration67/100
Scores 67/100, grounded in 28 distinct co-authors appearing across only two cataloged papers, plus a stable recurring group of roughly a dozen frequent partners (e.g. W. Lei, J. Pisano, J. Minehart, J. H. Singer, J. Demb). Working consistently within sizeable teams rather than solo indicates an embedded, team-science mode of research at Yale.
Activity—
Not yet scored: the data provide no publication frequency, preprint cadence, or time-stamped record beyond two papers both dated 2026. This axis would measure publishing rhythm and research tempo over recent years.
Reproducibility55/100
Scores 55/100, the mean over two papers (reproPapers = 2), both rated medium reproducibility readiness. The retinal genetics study is strongly multimodal but stays correlational, and the SPHERE paper withholds algorithmic detail under a non-standard privacy framework — both leaving replication gaps that hold the average near the middle of the scale.
Research summary
Park, S. J. (Yale University) presents an unusually two-sided early profile in the Evolve catalog: half of the reviewed output sits in systems neuroscience of retinal circuitry and half in computational biology / statistical privacy. The neuroscience line is led by a 2026 first-author bioRxiv preprint that molecularly identifies wide-field amacrine cells in mouse retina encoding stimulus orientation, using intersectional genetics built around Bhlhe22 and the kappa opioid receptor; Evolve's review calls the multimodal evidence strong but correlational, with cell-type heterogeneity and causal tests still open. The computational line appears in the 2026 SPHERE paper on unlocking sensitive data in the age of AI, where Park is a middle author; the review notes claims of exact-moment synthetic twins with strong privacy and AI-agent utility demonstrated on UK Biobank data, while flagging algorithm secrecy and a non-standard privacy framework as limits on verification. Radar dimensions show collaboration at 67/100 with 28 distinct co-authors, reproducibility at 55/100 averaged over two papers, and a focus score of 0 reflecting the even 50/50 field split. Output, impact, and activity axes lack sufficient underlying data and are deliberately not scored. Both catalog entries are 2026 preprints, so this should be read as an early-stage profile built from a small, name-resolved slice of public bibliographic data.
Collaboration pattern
Park works embedded in sizeable teams: 28 distinct co-authors appear across only two cataloged papers, with a recurring core of about a dozen partners — including W. Lei, J. Pisano, A. Orpia, J. Minehart, J. Pottackal, C. Hanke-Gogokhia, S. E. Ross, J. H. Singer, and J. Demb. The collaboration score of 67/100 reflects this breadth paired with stable repeat partners, consistent with team-based science rather than solo work.
Research keywords
Highlights
Genetic ID of orientation-tuned wide-field amacrine cells
SPHERE: synthetic twins for sensitive data in the AI era
Timeline
- 2026Posts a first-author bioRxiv preprint molecularly identifying orientation-encoding wide-field amacrine cells in mouse retina
- 2026Appears as a middle author on the SPHERE preprint on privacy-preserving synthetic data sharing in the age of AI
- 2026Both cataloged works appear in the same year, outlining an early-stage research profile spanning two distant fields
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 computed from OpenAlex-derived public bibliographic data only; no citation counts, h-index, or venue metrics were available for these 2026 preprints.
- The Evolve catalog contains only two papers for this researcher, both 2026 bioRxiv preprints not yet peer-reviewed, so all assessments are early-stage.
- Output, impact, and activity radar axes are unscored due to insufficient underlying data.
- The name 'Park, S. J.' is common; this profile is name-resolved from public bibliographic data, and no ORCID or country field was available to confirm identity.