Ross Gore

Old Dominion University · United States

Unclaimed · AI-structured profileOpenAlex

Old Dominion University researcher bridging agentic AI for genomics and machine-learning security cryptography

papers here

reviewed by Evolve

first-author

corresponding

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works · OpenAlex

h-index · OpenAlex

Bibliometric figures come from OpenAlex (2026-10-15) 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 snapshot lacks enough publication metadata for a reliable output measure. This axis would capture total works, output velocity, and venue spread; only 12 works, all dated 2026, are currently on file.

Impact—

Not yet scored: citation data is insufficient. The snapshot shows h-index 1 and i10-index 0, with 2026 works too recent to accrue citations. This axis would measure citation uptake, influence, and reach across the field.

Focus0/100

A focus score of 0 reflects a split portfolio: the field distribution assigns 0.5 to machine learning security and applied cryptography and 0.5 to bioinformatics (agentic AI for genomics). Topic counts confirm the breadth — Artificial Intelligence Applications (4), Impact of AI and Big Data on Business and Society (3), and Complex Network Analysis, Blockchain Applications and Security, and Distributed Systems and Fault Tolerance (2 each). Current work runs along two distinct technical lines in parallel.

Collaboration66/100

A collaboration score of 66 rests on 27 distinct coauthors in the snapshot. The frequent-coauthor list — Sachin Shetty, Eranga Bandara, Xueping Liang, Asanga Gunaratna, Tharaka Hewa, Peter Foytik, and others — indicates a recurring core team, and both reviewed 2026 papers place him in a middle author position on multi-author teams rather than as corresponding author.

Activity75/100

An activity score of 75 reflects a recent, concentrated burst: countsByYear records 12 works in 2026, and recent3yWorks also totals 12, meaning essentially all cataloged output falls within the snapshot year. Both Evolve-reviewed papers are 2026 arXiv preprints. No earlier-year counts appear in the snapshot, so the longer-term publishing cadence cannot be established from this data.

Reproducibility38/100

A reproducibility score of 38 is the mean across 2 Evolve-reviewed papers. The white-box CLWE-RFF backdoor study rates medium: results are credible in numpy/scipy, but small scale and weak test power limit the undetectability evidence. BaseCamp rates low, with every reported result described as an unmeasured placeholder. The contrast suggests measurement and artifact availability are the main levers for improvement.

Research summary

Ross Gore is a researcher at Old Dominion University in the United States. This profile is name-resolved from public OpenAlex bibliographic data, and because the name is shared by multiple scholars it should be read with care. The snapshot shows a concentrated 2026 output burst: 12 works recorded for the year, matching the recent3yWorks total of 12, split evenly between machine learning security and applied cryptography and agentic-AI-driven bioinformatics for genomics. Evolve has reviewed two 2026 arXiv papers. BaseCamp proposes six LLM agents for the decision layer of DNA sequencing data pipelines; the review finds the design sensible but notes that every reported result is an unmeasured placeholder, yielding low reproducibility readiness. A second paper implements the Goldwasser white-box undetectable backdoor for random Fourier features in numpy/scipy; its results are judged credible, although small scale and weak test power limit the undetectability evidence. The reproducibility mean across the two reviewed papers is 38. Collaboration is broad, with 27 distinct coauthors and a recurring core team. Citation indicators remain minimal — h-index 1, i10-index 0 — so the output and impact dimensions are left unscored. Topic data rounds out the picture: artificial intelligence applications (4), AI and big data in business and society (3), and complex networks, blockchain, and distributed systems (2 each).

Collaboration pattern

Collaboration is broad for an early-snapshot profile: 27 distinct coauthors, with a recurring core cluster around Sachin Shetty, Eranga Bandara, Xueping Liang, Asanga Gunaratna, Tharaka Hewa, and Peter Foytik. Both Evolve-reviewed 2026 papers list him in a middle author position, not corresponding author, consistent with contributing specialist modules to team projects. Note that his own name appears among the frequent coauthors, likely a name-disambiguation artifact in the source graph.

Research keywords

Agentic AILLM agentsDNA sequencing pipelinesVariant callingBioinformatics workflow automationHuman-in-the-loopContinuous learning with errors (CLWE)Random Fourier featuresWhite-box undetectable backdoorReproducibility studyLattice-based cryptographySparse Gaussian Pancakes

Highlights

BaseCamp: six LLM agents for DNA sequencing pipelines
This 2026 arXiv framework assigns six LLM agents to the decision layer of DNA sequencing data pipelines, with human-in-the-loop oversight. Evolve's review calls the design sensible but flags that every reported result is an unmeasured placeholder, hence low reproducibility readiness.
Realizing a white-box undetectable backdoor on random Fourier features
An implementation study shows the Goldwasser CLWE-RFF backdoor is realizable in numpy/scipy, sitting at the intersection of continuous learning with errors, lattice-based cryptography, and random Fourier features. Evolve judges the results credible while noting that small scale and weak test power limit the undetectability evidence; reproducibility readiness is medium.
Twin-track 2026 output across security and genomics
The snapshot records 12 works in 2026, split evenly between machine learning security and applied cryptography and agentic-AI genomics, with 27 distinct coauthors — a broad single-year footprint, though citation indicators remain at h-index 1.

Timeline

2026Released BaseCamp on arXiv, an agentic AI framework using six LLM agents to automate DNA sequencing data pipelines (Evolve-reviewed; low reproducibility readiness).
2026Published the numpy/scipy implementation of the Goldwasser white-box undetectable backdoor for random Fourier features (Evolve-reviewed; medium reproducibility readiness).
2026Snapshot totals 12 works for the year across two evenly split fields, with 27 distinct coauthors; output and impact remain unscored pending more citation data.

Papers in the Evolve catalog

Frequent co-authors

Michael Collins×1 Jada Cumberland×1 Brianne Dunn×1 Ross Gore×1 Samuel Jackson×1 Sachin Shetty×1 Eranga Bandara×1 Xueping Liang×1 Asanga Gunaratna×1 Tharaka Hewa×1 Abdul Rahman×1 Peter Foytik×1

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 an OpenAlex-sourced snapshot; no ORCID was available, and 'Ross Gore' is a shared name, so some records may belong to other researchers.
The frequent-coauthor list contains the researcher's own name, a likely entity-disambiguation artifact that signals noise in the underlying graph.
Output and impact axes are unscored: the snapshot reports h-index 1 and i10-index 0, and the 12 recorded works are all from 2026, too recent for citation accumulation.
Only two papers carry Evolve reviews, so the reproducibility mean of 38 and the topic counts reflect a small, very recent sample rather than a career-long record.