Siddharth Vohra

Output—

Output metrics are not yet computed for this profile: the snapshot lacks reliable totals for works, venues, and productivity volume, so this axis — which would measure publication volume and output consistency over time — is left unscored rather than estimated.

Impact—

Impact cannot be scored yet: the snapshot shows only an h-index of 1 and an i10-index of 0, far too little citation signal for a meaningful score; this axis would measure citation uptake, influence on follow-up work, and downstream reuse.

Focus0/100

The focus score of 0 reflects extreme breadth rather than missing data: the field distribution splits exactly evenly at one-third each across NLP/AI fairness evaluation, retrieval-augmented generation evaluation, and LLM evaluation, while topic assignments cluster into five areas — topic modeling, NLP techniques, domain adaptation and few-shot learning, explainable AI, and ML classification — with two mentions apiece. No single direction yet dominates the profile.

Collaboration32/100

The collaboration score of 32 sits low: the recent window lists only 4 distinct co-authors, and the profile names four frequent collaborators — Manikandan Ravikiran, Runmin Jiang, Xiaomo Li, and Min Xu. Authorship patterns reinforce a compact team structure: first author on the fairness audit and the RAG identity-handoff audit, last author on the multiple-choice robustness audit. The network is small but recurs across evaluation projects.

Activity47/100

Activity scores 47, an intermediate reading on thin data: the recent three-year window contains 4 works, and OpenAlex counts-by-year shows single works in 2012, 2020, and 2022 before a step up to 4 works in 2026 — the year of all three Evolve-reviewed papers. The pattern suggests a burst of evaluation research concentrated in 2026 rather than sustained high-volume production.

Reproducibility85/100

Reproducibility is the profile's strongest axis at 85, the mean across 3 audited papers. Two of the three Evolve-reviewed papers carry a high reproducibility rating — the pre-registered fairness audit using equivalence testing, and the RAG identity-handoff audit described as careful and reproducible — while the NC-MCAR robustness audit rates medium, dinged for a missing noise-floor baseline. Methodological transparency is a consistent signature.