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Mingxing TanResearch Director · WaymoResearcher↗
Researcher
Sharan Narang
WaymoResearch Director
Narang contributed to T5, which framed many language tasks in one text-to-text format,[1] and to influential studies of how model design, data and computing power affect performance at scale. He later became a research leader at Waymo, applying large-scale learning to autonomous driving.
Career & education
Current roleWaymo2026-presentResearch Director
Waymo2026-presentPreviously
Education
Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Citation Trend
Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
citations125,920As of Nov 1, '26
citations+4,135Since last snapshot, Oct 1, '26
Selected contributions
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
With Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Michael Matena, Yanqi Zhou, Wei Li and others ·T5 treats language tasks as text input and text output within one framework.
Llama 2: Open Foundation and Fine-Tuned Chat Models
With Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov and others ·Presented Llama 2 and its chat models, including training methods and capability evaluations.
PaLM: Scaling Language Modeling with Pathways
With Aakanksha Chowdhery, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung and others ·Presented PaLM, a large language model demonstrating gains in reasoning, translation and coding.
References
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformerjmlr.org
Narang and colleagues describe T5 and show how tasks such as translation and summarization can use the same text-in, text-out setup.
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Reviewed August 8, 2026 · Checked for accuracy and new information.
