Google ML researcher: production multi-task recommender systems and faster experimentation at YouTube.
2
papers here
2
reviewed by Evolve
1
first-author
0
corresponding
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works · OpenAlex
6
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-08) 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—
No output score yet: the bibliometric snapshot does not provide a complete, disambiguated works list for this author, so total publication volume cannot be reliably scored. This axis would measure cumulative works and yearly productivity trends.
Impact—
Impact is not scored: citation totals beyond h-index (6) and i10-index (4) are not available in the snapshot, so scholarly influence cannot be assessed. This axis would measure citations received and downstream adoption of the work.
Focus0/100
Focus scores 0/100, the lowest possible: the reviewed work splits evenly (0.5/0.5) between two recommender-systems fields, and the OpenAlex topic profile spans five areas — medical image segmentation (5), multi-criteria decision making (3), transportation planning (3), urban transport systems (3), and neural network applications (2). Whether this breadth reflects deliberate range or name-disambiguation noise is unresolved.
Collaboration55/100
Collaboration scores 55/100: 15 distinct coauthors across 12 works in the trailing three years, with a recurring Google core — frequent collaborators include Aniruddh Nath, Lukasz Heldt, Florian Raudies, Yiping Yuan, and Shawn Andrews. Authorship positions vary (first author on the 2026 Light Heads paper, middle author on the cross-task paper), consistent with large industrial team publications.
Activity75/100
Activity scores 75/100: yearly output climbed from 1-2 works in 2019-2022 to 4 in 2023 and 4 in 2024, reaching a peak of 6 in 2025, with 2 already recorded in 2026; the trailing three years total 12 works. The trajectory marks a clearly accelerating, currently very active researcher.
Reproducibility20/100
Reproducibility scores 20/100 (reproMean 20 across 2 repro-assessed papers): both reviewed 2026 papers are rated low readiness. The Light Heads paper ships frozen-backbone adapter heads but carries thin offline statistics, and the cross-task framework is tied to YouTube production infrastructure — artifacts external labs typically cannot rerun.
Research summary
Sanjay Surendranath Girija is a machine-learning researcher at Google (United States) whose reviewed work targets production-scale recommender systems and multi-task learning, validated on YouTube traffic. The Evolve catalog holds two 2026 arXiv papers. As first author of 'Lightweight Ranking Heads,' he contributed to frozen-backbone adapter heads that reportedly cut YouTube multi-task experiment cycles from 24 to 11 days; as a middle author of 'Learned Cross-Task Relationships,' he worked on approximating joint label distributions through pairwise auxiliary heads. The OpenAlex snapshot (h-index 6, i10-index 4) records 12 works over the trailing three years and 15 distinct coauthors, with yearly output rising from 1-2 works in 2019-2022 to 4 in 2023-2024 and a peak of 6 in 2025 — a clearly accelerating trajectory (activity 75/100). Radar scores place collaboration at 55, reproducibility at 20 (both reviewed papers rated low), and focus at 0, reflecting an even 0.5/0.5 split across two recommender-systems fields and a broad OpenAlex topic profile spanning medical image segmentation, transportation planning, and multi-criteria decision making, which may reflect earlier or differently disambiguated work. Output and impact remain unscored for lack of complete data. This is an early-stage profile with strong industrial grounding.
Collaboration pattern
The record shows Google-team coauthorship: 15 distinct coauthors across the trailing three years, with a recurring core including Aniruddh Nath, Lukasz Heldt, Florian Raudies, Yiping Yuan, Shawn Andrews, Yanhao Jiang, and Aditya Mahajan. Authorship roles vary — first author on the 2026 'Lightweight Ranking Heads' paper, middle author on 'Learned Cross-Task Relationships' — typical of large industrial team publications around one production system (YouTube).
Research keywords
Highlights
First-authored systems work: Light Heads cut YouTube multi-task experiment cycles from 24 to 11 days
Approximating joint label distributions with pairwise auxiliary heads, deployed on YouTube
Industrial-scale evidence with limited external reproducibility
Both reviewed 2026 papers report validation on YouTube production traffic, yet Evolve rates both as low reproducibility readiness (reproMean 20 across 2 papers) — evidence grounded in production experiments that external teams cannot currently rerun from released artifacts.
Timeline
- 2019Earliest recorded output in the OpenAlex snapshot: 1 work in countsByYear, marking the start of the indexed publication record.
- 2023Output accelerates to 4 works (repeated in 2024), marking a shift into a sustained research rhythm.
- 2025Peak recorded year with 6 works; the trailing three years accumulate 12 works with 15 distinct coauthors.
- 2026Two arXiv papers on multi-task recommender systems at YouTube — first-authored 'Lightweight Ranking Heads' and middle-authored 'Learned Cross-Task Relationships'; 2 works already recorded in 2026.
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 data comes from a single OpenAlex-authority snapshot (identity: openalex); the profile is name-resolved from public bibliographic records and disambiguation cannot be fully guaranteed.
- OpenAlex topics (medical image segmentation, multi-criteria decision making, transportation) diverge from the reviewed recommender-systems work; whether those indexed works belong to the same person is unverified, and the countsByYear record has no 2020 entry.
- Citation totals are absent — only h-index (6) and i10-index (4) are provided — so the output and impact radar axes are left unscored rather than estimated.
- Only two papers have been reviewed by Evolve, both 2026 arXiv preprints rated low reproducibility readiness; assessments may change as peer review and code release evolve.