ML researcher spanning privacy-preserving learning, edge/fog systems, and LLM-era retrieval and multi-agent systems
3
papers here
3
reviewed by Evolve
0
first-author
0
corresponding
—
works · OpenAlex
31
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-16) 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—
Per-year work counts are available — 1 indexed work in 2010 rising to 51 in 2026 — but no computed output score exists in this snapshot; this axis would measure publication volume and productivity trajectory, and is left unscored here.
Impact—
An h-index of 31 and i10-index of 86 are recorded, but citation totals, per-paper citation distributions, and a computed impact score are absent from this snapshot; this axis would measure the citation-based influence of the body of work.
Focus0/100
The focus score is 0, reflecting marked breadth: OpenAlex field weights split exactly evenly (0.333 each) across applied machine learning/marketing analytics, LLM multi-agent systems/distributed systems, and multimodal information retrieval (RAG). Five topic clusters — led by privacy-preserving technologies (111 works) and IoT/edge-fog computing (67) — reinforce a portfolio spanning networked learning systems through 2026-era generative-AI infrastructure rather than one narrow specialization.
Collaboration50/100
Scoring 50, the collaboration axis rests on 11 distinct co-authors in the recent three-year window — moderate relative to 138 works, suggesting small focused teams rather than large consortia. The frequent-coauthor list links Brinton to a long-running cluster (Mung Chiang, Jan P. Allebach, Dong-Jun Han, Seyyedali Hosseinalipour, Shiqiang Wang) alongside newer collaborators on the 2026 arXiv papers (Tianqi Wang, Sheikh Shams Azam, Evan Chen, Anton Wiranata, Zhan-Lun Chang, Wan Eih Huang).
Activity100/100
Activity scores a perfect 100. The counts-by-year series shows sustained acceleration: single-digit works from 2010 to 2017 (8 in 2016, 5 in 2017), then 13 in 2018, 15 in 2020, 23 in each of 2021 and 2022, 39 in 2023, 47 in 2024, 40 in 2025, and 51 indexed works in 2026 — totalling 138 works over the last three years.
Reproducibility43/100
Reproducibility scores 43, computed from Evolve's reviews of 3 papers (reproMean 43). The two LLM-systems papers are rated medium readiness: the multimodal RAG work shows consistent gains but narrow benchmarks and missing key ablations, while PlanFence is protocol-clean yet its baselines fail by construction over three workflow families. The CatBoost B2B pipeline is rated low, evaluated on proprietary HP data without external validation.
Research summary
Christopher G. Brinton, affiliated in this record with Purdue University, West Lafayette, is name-resolved here from public OpenAlex bibliographic data. The snapshot reports an h-index of 31 and an i10-index of 86, with 138 works over the last three years and an annual record that climbs from single digits before 2016 to 51 indexed works in 2026. Topical clusters are led by privacy-preserving technologies in data (111 works), IoT and edge/fog computing (67), stochastic gradient optimization (46), online learning and analytics (38), and domain adaptation and few-shot learning (34). Recent output splits evenly across three fields — applied machine learning and marketing analytics, LLM multi-agent systems and distributed systems, and multimodal information retrieval (RAG) — a breadth the radar's focus axis scores at 0. Evolve has reviewed three 2026 arXiv papers: a generator-in-the-loop alignment study for multimodal RAG and a dependency-scoped memory-validation protocol for LLM agents, both with Brinton as last author, plus a graph-and-CatBoost B2B conversion pipeline in the middle position. Reproducibility across the three averages 43, with verdicts citing narrow benchmarks, by-construction baselines, and proprietary data. Frequent co-authors include Mung Chiang, Jan P. Allebach, Dong-Jun Han, Seyyedali Hosseinalipour, and Shiqiang Wang.
Collaboration pattern
Eleven distinct co-authors appear in the last three years, and the frequent-coauthor list shows two generations of collaboration: a long-standing cluster with Mung Chiang, Jan P. Allebach, Dong-Jun Han, Seyyedali Hosseinalipour, and Shiqiang Wang, plus newer 2026 collaborators including Tianqi Wang, Sheikh Shams Azam, Evan Chen, Anton Wiranata, Zhan-Lun Chang, and Wan Eih Huang. On the reviewed papers Brinton sits last author twice, consistent with a senior role; the collaboration score is 50.
Research keywords
Highlights
Generator-in-the-Loop Alignment for Multimodal RAG
PlanFence: Dependency-Scoped Validation for LLM-Agent Shared Memory
Graph-and-CatBoost Pipeline for B2B Conversion Prediction
Timeline
- 2010Earliest indexed works appear in the OpenAlex record: 1 work in 2010 and 4 in 2011.
- 2018Annual output reaches 13 works, opening a sustained climb through 15 in 2020 and 23 in each of 2021 and 2022.
- 2024Output peaks at 47 indexed works, up from 39 in 2023.
- 202651 indexed works are recorded for the year; all three Evolve-reviewed arXiv papers appear, with Brinton as last author on the multimodal RAG and LLM-agent memory papers.
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 come from an OpenAlex snapshot only; no computed output or impact scores were available, so those radar axes are left unscored.
- The record lists affiliation Purdue University, West Lafayette but carries the country code 'SG'; entity resolution is name-based and should be treated with care.
- Evolve paper assessments cover only three 2026 arXiv papers; the reproducibility mean (43) reflects those papers, not the full corpus.
- Per-year work counts for 2026 are as reported by the snapshot and may shift as the year completes and indexing lags resolve.