--- title: "Kwangjun Ahn · Microsoft — author profile and papers" description: "Kwangjun Ahn, Microsoft: 2 papers on Elamur Research (2023), h-index 12 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/kwangjun-ahn/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/kwangjun-ahn/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Kwangjun Ahn # Kwangjun Ahn Kwangjun Ahn has 2 papers in our library, in 2023, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Suvrit Sra](https://md.research.elamur.ai/free/en/authors/suvrit-sra/), [Xiang Cheng](https://md.research.elamur.ai/free/en/authors/xiang-cheng/), [Chulhee Yun](https://md.research.elamur.ai/free/en/authors/chulhee-yun/). 2 papers in our library · 2023 [OpenAlex](https://openalex.org/A5079986554) · [ORCID](https://orcid.org/0000-0001-5516-5775) · [**h-index 12**](https://openalex.org/authors/A5079986554) · [49 works](https://openalex.org/works?filter=author.id:A5079986554) · [938 citations](https://openalex.org/works?filter=author.id:A5079986554&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/kwangjun-ahn/#papers) · [🔗 Cited 38 times in our library](https://md.research.elamur.ai/free/en/authors/kwangjun-ahn/cited-by/) · [⭐ Paper Stars ranking: #9804](https://md.research.elamur.ai/free/en/paper-stars/page/99/#star-9804) 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=Kwangjun+Ahn) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Kwangjun+Ahn) · [dblp](https://dblp.org/search?q=Kwangjun+Ahn) · [ACL Anthology](https://aclanthology.org/search/?q=Kwangjun+Ahn) ## Affiliations [🏢**Microsoft** Redmond · United States 2024–2025 · 6 papers*current*](https://md.research.elamur.ai/free/en/companies/microsoft/) · [🎓**Massachusetts Institute of Technology** Cambridge · United States 2020–2024 · 16 papers](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) · [🎓**Korea Advanced Institute of Science and Technology** Daejeon · South Korea 2016–2018 · 4 papers](https://md.research.elamur.ai/free/en/universities/korea-advanced-institute-of-science-and-technology/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 4 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 1 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2023** 2 papers](https://md.research.elamur.ai/free/en/authors/kwangjun-ahn/year/2023/) ## Papers The most cited first (OpenAlex and our library). 1. **[Transformers learn to implement preconditioned gradient descent for in-context learning](https://md.research.elamur.ai/free/en/papers/ahn-2023-transformers-learn-implement-preconditioned-gradient/)** *(2023 · 37 citations)* The paper asks whether transformers merely can represent optimization algorithms or whether ordinary training actually drives them to do so. 2. **[Linear attention is (maybe) all you need (to understand transformer optimization)](https://md.research.elamur.ai/free/en/papers/ahn-2023-linear-attention-maybe-all-need/)** *(2023 · 10 citations)* Training Transformers is notoriously hard. Plain SGD lags well behind adaptive optimizers such as Adam, and the loss landscape shows unusual properties that classical optimization theory does not account for. Full Transformers are too complex to analyze directly, so the authors look for the simplest model that still… ## Co-authors here 3 co-authors with a page, the most papers together first. - [Suvrit Sra](https://md.research.elamur.ai/free/en/authors/suvrit-sra/) ×2 - [Xiang Cheng](https://md.research.elamur.ai/free/en/authors/xiang-cheng/) ×2 - [Chulhee Yun](https://md.research.elamur.ai/free/en/authors/chulhee-yun/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/): [NeurIPS 2023](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2023/) ## Cited by Papers of our library cite their works 38 times (of the 149,439 papers whose bibliographies we read); [12 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/kwangjun-ahn/cited-by/). Updated 3 November 2026