AIOps researcher at IIT Jodhpur building reproducible on-premise LLM pipelines for Kubernetes root cause analysis
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 (2026-10-15) 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 reports h-index 0 and i10-index 0 with no publication-volume measure beyond five 2026 works. This axis would measure total output and productivity against field norms once a longer publication record accumulates.
Impact—
Not yet scored: h-index and i10-index are both 0 and no citation counts appear in the snapshot, so influence cannot be estimated. This axis would measure citations, uptake, and effect of the work on later research and operational practice.
Focus0/100
Scores 0, and the raw record explains why: fieldDist splits evenly (0.5/0.5) between 'AI for IT operations / systems and security' and 'AIOps / machine learning for systems', while OpenAlex topics still spread over five labels (Topic Modeling 4; Software Engineering Research 2; Multimodal ML Applications 2; System Performance and Reliability 2; Cloud Computing 1). With only five works, stable topical concentration has not yet crystallized.
Collaboration22/100
Scores 22 on a small base: OpenAlex records only 2 distinct coauthors in the recent window, with Susil Kumar Mohanty and Jeenal Chaudhary as the recurring partners. Both Evolve-reviewed papers list Patel as first author and not corresponding, pointing to a compact, hands-on group in which this researcher drives the experiments and the writing rather than coordinating a large network.
Activity52/100
Scores 52, mid-range, and the raw counts show why: countsByYear records all 5 works in 2026 and recent3yWorks is 5, so output is recent and concentrated rather than accumulated over years. A steady cadence within 2026 sits on a thin three-year base, which is the honest reading for an early-stage record.
Reproducibility100/100
Scores 100: reproMean is 100 across reproPapers 2, and both Evolve-reviewed papers carry 'high' reproducibility readiness. TriFleetRCA documents a single-GPU setup tested over 4 injected faults (n=100) with wide confidence intervals reported, and TriCalRAG ships a three-strategy RAG benchmark — a consistently transparent, re-runnable evaluation style across the record.
Research summary
Rohit Patel is an early-stage researcher at the Indian Institute of Technology Jodhpur (India) working at the intersection of AIOps, machine learning for systems, and software reliability. The Evolve catalog holds two 2026 arXiv preprints with Patel as first author: TriFleetRCA, a single-GPU on-premise LLM pipeline for Kubernetes root cause analysis scoring 0.85–0.95 over four injected faults (n=100), and TriCalRAG, a three-strategy retrieval-augmented benchmark showing that RAG stabilizes zero-shot-degenerate LLM log-anomaly detection on one GPU, backed by solid calibration analysis. The consistent theme is private, on-premise LLM inference combined with retrieval-augmented generation for operational diagnostics, evaluated honestly at small scale rather than oversold. Bibliometrics, name-resolved from OpenAlex without a linked ORCID, record 5 works in 2026 (recent3yWorks: 5), 2 distinct coauthors with Susil Kumar Mohanty and Jeenal Chaudhary recurring, h-index and i10-index of 0, and a top reproducibility score (reproMean 100 across 2 papers). Output and impact are not yet scored because citation data is insufficient. This is an early-stage profile: the record is short, entirely preprint-based in this catalog, and citations have not yet accumulated.
Collaboration pattern
Collaboration is compact: the collaboration dimension scores 22 and OpenAlex lists only 2 distinct coauthors in the recent window. Susil Kumar Mohanty and Jeenal Chaudhary recur as the key partners, and Patel is first author on both Evolve-reviewed papers (non-corresponding). The pattern suggests a small IIT Jodhpur group where Patel leads experiments and writing rather than a large multi-institution network.
Research keywords
Highlights
Single-GPU on-premise root cause analysis for Kubernetes
Three-strategy RAG benchmark for on-premise AIOps RCA
A consistently reproducible evaluation style
Both cataloged papers carry 'high' reproducibility readiness and the reproducibility dimension scores 100 (reproMean 100 over 2 papers) — single-GPU setups, fixed fault suites, and reported confidence intervals make the work easy to re-run and stress-test.
Timeline
- 2026First cataloged preprint: TriCalRAG, a three-strategy retrieval-augmented benchmark for on-premise LLM-based root cause analysis in AIOps (arXiv, first author).
- 2026Follow-up system paper: TriFleetRCA, on-premise LLM root cause analysis for Kubernetes, reporting 0.85–0.95 over 4 injected faults (n=100) on a single GPU.
- 2026OpenAlex records 5 works in 2026 (recent3yWorks: 5) with 2 distinct coauthors — an early, concentrated publication burst.
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.
- Bibliometric metrics come from OpenAlex only (identity: openalex); citation coverage for very recent 2026 arXiv preprints is likely incomplete.
- No ORCID is linked and 'Rohit Patel' is a common name — this profile is name-resolved from public bibliographic data and could merge or split records.
- Output and impact dimensions are unscored because h-index and i10-index are 0 and citation counts are absent from the snapshot.
- Qualitative verdicts rest on only 2 Evolve-reviewed papers, both 2026 arXiv preprints; this is an early-stage profile with no citation history.