--- title: "Paul Barham — author profile and papers" description: "Paul Barham: 2 papers on Elamur Research (2016–2022), h-index 31 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/paul-barham/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/paul-barham/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Paul Barham # Paul Barham Paul Barham's papers here deal with the systems that make large-scale machine learning possible. They focus on how computation can be expressed, distributed and run efficiently across many devices, and on what becomes possible when models are trained at very large scale. The work moves between system design and its use in [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/). The most cited of these papers is the white paper on [TensorFlow](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/) (2016). It describes the successor to Google's earlier DistBelief training system, which had proved inflexible for research and hard to move onto small devices. TensorFlow serves both as an interface for writing machine learning computations and as a runtime for executing them. Programs are stateful dataflow graphs whose nodes are operations with kernels for CPUs or GPUs and whose edges carry typed tensors. The newer direction is shown by [PaLM: Scaling Language Modeling with Pathways](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/) (2022). This work trained a very large dense decoder-only Transformer to study how model scale affects few-shot performance without task-specific fine-tuning. Training was spread over TPU v4 pods using the Pathways system and reached high hardware utilization without pipeline parallelism. The resulting model gave strong few-shot results across language, code, translation and multilingual benchmarks. Co-authors on these papers include [Aakanksha Chowdhery](https://md.research.elamur.ai/free/en/authors/aakanksha-chowdhery/), [Jeff Dean](https://md.research.elamur.ai/free/en/authors/jeff-dean/), [Sharan Narang](https://md.research.elamur.ai/free/en/authors/sharan-narang/) and [Ian Goodfellow](https://md.research.elamur.ai/free/en/authors/ian-goodfellow/). Barham's bylines run from the [University of Cambridge](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) (1995–2002) to [Microsoft Research](https://md.research.elamur.ai/free/en/companies/microsoft-research-united-kingdom/) in Cambridge and [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/), and most recently to [Google](https://md.research.elamur.ai/free/en/companies/google/) (2015–2022). Paul Barham has 2 papers in our library, in 2016–2022, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Aakanksha Chowdhery](https://md.research.elamur.ai/free/en/authors/aakanksha-chowdhery/), [Adam Roberts](https://md.research.elamur.ai/free/en/authors/adam-roberts/), [Charles Sutton](https://md.research.elamur.ai/free/en/authors/charles-sutton/). 2 papers in our library · 2016–2022 [OpenAlex](https://openalex.org/A5110193634) · [**h-index 31**](https://openalex.org/authors/A5110193634) · [55 works](https://openalex.org/works?filter=author.id:A5110193634) · [38,363 citations](https://openalex.org/works?filter=author.id:A5110193634&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/paul-barham/#papers) · [🔗 7,879 citations in our library](https://md.research.elamur.ai/free/en/authors/paul-barham/cited-by/) · [2 more in our library without a page yet](#also) · [⭐ Paper Star rank #144](https://md.research.elamur.ai/free/en/paper-stars/page/2/#star-144) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Paul+Barham) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Paul+Barham) · [dblp](https://dblp.org/search?q=Paul+Barham) · [ACL Anthology](https://aclanthology.org/search/?q=Paul+Barham) ## Affiliations [🏢**Microsoft Research** Cambridge · United Kingdom 2003–2010 · 18 papers](https://md.research.elamur.ai/free/en/companies/microsoft-research-united-kingdom/) · [🏢**Microsoft** Redmond · United States 2002–2016 · 7 papers](https://md.research.elamur.ai/free/en/companies/microsoft/) · [🏢**Google** Mountain View · United States 2015–2022 · 5 papers](https://md.research.elamur.ai/free/en/companies/google/) · [🎓**University of Cambridge** Cambridge · United Kingdom 1995–2002 · 5 papers](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Topics [Language models & pretraining (1)](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/) ### 2010s [**2016** 1 paper](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/) ## Papers The most cited first (OpenAlex and our library). 1. **[TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/)** *(2016 · 9,453 citations)* Google's first-generation distributed training system, DistBelief, was used by more than 50 teams. It proved too rigid for fast research and hard to bring to small devices. This white paper presents TensorFlow, its successor. TensorFlow is both an interface for defining machine learning computations and a system for… 2. **[PaLM: Scaling Language Modeling with Pathways](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)** *(2022 · 2,603 citations)* The authors investigate how the size of a language model affects its ability to solve tasks from a few examples (few-shot), without task-specific fine-tuning. ## Also in our library 2 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 12 of 13 co-authors with a page, the most papers together first. - [Aakanksha Chowdhery](https://md.research.elamur.ai/free/en/authors/aakanksha-chowdhery/) - [Adam Roberts](https://md.research.elamur.ai/free/en/authors/adam-roberts/) - [Charles Sutton](https://md.research.elamur.ai/free/en/authors/charles-sutton/) - [Eugene Brevdo](https://md.research.elamur.ai/free/en/authors/eugene-brevdo/) - [Greg S. Corrado](https://md.research.elamur.ai/free/en/authors/greg-s-corrado/) - [Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/) - [Ian Goodfellow](https://md.research.elamur.ai/free/en/authors/ian-goodfellow/) - [Jacob Devlin](https://md.research.elamur.ai/free/en/authors/jacob-devlin/) - [Jeff Dean](https://md.research.elamur.ai/free/en/authors/jeff-dean/) - [Maarten Bosma](https://md.research.elamur.ai/free/en/authors/maarten-bosma/) - [Sebastian Gehrmann](https://md.research.elamur.ai/free/en/authors/sebastian-gehrmann/) - [Sharan Narang](https://md.research.elamur.ai/free/en/authors/sharan-narang/) ## Cited by Papers of our library cite their works 7,879 times (of the 140,884 papers whose bibliographies we read); [550 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/paul-barham/cited-by/).