--- title: "Mehdi Cherti — author profile and papers" description: "Mehdi Cherti: 2 papers on Elamur Research (2022), h-index 10 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/mehdi-cherti/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/mehdi-cherti/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Mehdi Cherti # Mehdi Cherti Mehdi Cherti works on large-scale machine learning for [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/), especially models that learn jointly from images and their accompanying text. The papers here share a common question: how open, web-scale data and openly released code can make research on contrastive image-text models possible outside a small number of industry labs. The methods are empirical and computational. They involve collecting and filtering very large image-text corpora, training CLIP-style models with the open-source OpenCLIP codebase, and measuring how performance changes as models and data grow. The most cited of Cherti's papers here is [LAION-5B](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/) (2022). It describes a public collection of billions of image-text pairs gathered from Common Crawl using images and their HTML alt-text. Pairs were kept only when an existing CLIP model judged image and caption to be sufficiently similar, and OpenCLIP models trained on a subset showed that the data was usable. The companion paper, [Reproducible scaling laws for contrastive language-image learning](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/) (2022), builds on that resource. It trains CLIP models on public LAION subsets while varying the size of the vision encoder, the amount of data and the number of samples seen, which yields a scaling study anyone can reproduce. Both works date from 2022 and together represent the most recent direction in the papers here: open data paired with transparent measurement of scaling behaviour. Bylines from 2021 to 2025 place Cherti at [Forschungszentrum Jülich](https://md.research.elamur.ai/free/en/labs/forschungszentrum-julich/) and its Jülich Supercomputing Centre. Earlier papers came from the Laboratoire de l'Accélérateur Linéaire in Orsay and the Laboratoire de Recherche en Informatique in Gif-sur-Yvette. Frequent collaborators include [Christoph Schuhmann](https://md.research.elamur.ai/free/en/authors/christoph-schuhmann/), [Romain Beaumont](https://md.research.elamur.ai/free/en/authors/romain-beaumont/), [Ross Wightman](https://md.research.elamur.ai/free/en/authors/ross-wightman/), [Mitchell Wortsman](https://md.research.elamur.ai/free/en/authors/mitchell-wortsman/) and Cade Gordon. Other co-authors include [Jenia Jitsev](https://md.research.elamur.ai/free/en/authors/jenia-jitsev/) and Ludwig Schmidt. Mehdi Cherti has 2 papers in our library, in 2022, most of them on [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/); often with [Cade Gordon](https://md.research.elamur.ai/free/en/authors/cade-gordon/), [Christoph Schuhmann](https://md.research.elamur.ai/free/en/authors/christoph-schuhmann/), [Mitchell Wortsman](https://md.research.elamur.ai/free/en/authors/mitchell-wortsman/). 2 papers in our library · 2022 [OpenAlex](https://openalex.org/A5050394079) · [ORCID](https://orcid.org/0000-0001-5865-0469) · [**h-index 10**](https://openalex.org/authors/A5050394079) · [38 works](https://openalex.org/works?filter=author.id:A5050394079) · [2,203 citations](https://openalex.org/works?filter=author.id:A5050394079&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/mehdi-cherti/#papers) · [🔗 602 citations in our library](https://md.research.elamur.ai/free/en/authors/mehdi-cherti/cited-by/) · [2 more in our library without a page yet](#also) · [⭐ Paper Star rank #1118](https://md.research.elamur.ai/free/en/paper-stars/page/12/#star-1118) 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=Mehdi+Cherti) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Mehdi+Cherti) · [dblp](https://dblp.org/search?q=Mehdi+Cherti) · [ACL Anthology](https://aclanthology.org/search/?q=Mehdi+Cherti) ## Affiliations [🔬**Forschungszentrum Jülich** Jülich · Germany 2021–2025 · 21 papers](https://md.research.elamur.ai/free/en/labs/forschungszentrum-julich/) · [🔬**Jülich Supercomputing Centre** Jülich · Germany 2021–2025 · 15 papers](https://ror.org/04g2j9721) · [🔬**Laboratoire de l'Accélérateur Linéaire** Orsay · France 2016–2018 · 3 papers](https://ror.org/046w3ze59) · [🔬**Centre de Gestion Scientifique** Paris · France 2018 · 2 papers](https://ror.org/003y0kg83) · [🔬**Laboratoire de Recherche en Informatique** Gif-sur-Yvette · France 2016 · 2 papers](https://ror.org/04e3ktk27) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Concepts, shelves and topics Of its works in our library: the number of works on each and its rating among them. 💡 Concepts 1[Scaling laws · 1 work](https://md.research.elamur.ai/free/en/concepts/scaling-laws/) 📚 Shelves 2[Other · 1 work](https://md.research.elamur.ai/free/en/shelves/other/) · [Vision, graphics & robotics · 1 work](https://md.research.elamur.ai/free/en/shelves/vision-robotics/) 🧭 Topics 1[Vision & language · 1 work](https://md.research.elamur.ai/free/en/topics/multimodal/) ## Works by year ### 2020s [**2022** 2 papers](https://md.research.elamur.ai/free/en/authors/mehdi-cherti/year/2022/) ## Papers The most cited first (OpenAlex and our library). 1. **[LAION-5B: An open large-scale dataset for training next generation image-text models](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/)** *(2022 · 1,400 citations)* Recent image-text models such as CLIP, ALIGN, BASIC and Imagen are trained on hundreds of millions to billions of image-text pairs collected from the web. 2. **[Reproducible scaling laws for contrastive language-image learning](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/)** *(2022 · 750 citations)* Earlier scaling-law research mostly used closed data and models, or looked only at language or only at vision. ## 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 co-authors with a page, the most papers together first. - [Cade Gordon](https://md.research.elamur.ai/free/en/authors/cade-gordon/) ×2 - [Christoph Schuhmann](https://md.research.elamur.ai/free/en/authors/christoph-schuhmann/) ×2 - [Mitchell Wortsman](https://md.research.elamur.ai/free/en/authors/mitchell-wortsman/) ×2 - [Romain Beaumont](https://md.research.elamur.ai/free/en/authors/romain-beaumont/) ×2 - [Ross Wightman](https://md.research.elamur.ai/free/en/authors/ross-wightman/) ×2 - [Aarush Katta](https://md.research.elamur.ai/free/en/authors/aarush-katta/) - [Clayton Mullis](https://md.research.elamur.ai/free/en/authors/clayton-mullis/) - [Gabriel Ilharco](https://md.research.elamur.ai/free/en/authors/gabriel-ilharco/) - [Jenia Jitsev](https://md.research.elamur.ai/free/en/authors/jenia-jitsev/) - [Ludwig Schmidt](https://md.research.elamur.ai/free/en/authors/ludwig-schmidt/) - [Richard Vencu](https://md.research.elamur.ai/free/en/authors/richard-vencu/) - [Theo Coombes](https://md.research.elamur.ai/free/en/authors/theo-coombes/) ## Cited by Papers of our library cite their works 602 times (of the 140,884 papers whose bibliographies we read); [42 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/mehdi-cherti/cited-by/). Updated 23 October 2026