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Varun Chandrasekaran

Varun Chandrasekaran has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining; often with Ece Kamar, Eric Horvitz, Hamid Palangi.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 13 · 72 works · 2,880 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 916 times in our library · ⭐ Paper Stars ranking: #1260

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Illinois Urbana-ChampaignUrbana · United States2023–2026 · 26 paperscurrent🎓University of VirginiaCharlottesville · United States2026 · 1 papercurrent🎓University of Wisconsin–MadisonMadison · United States2018–2024 · 20 papers🏢MicrosoftRedmond · United States2023–2025 · 5 papers🔬Vector InstituteToronto · Canada2021–2022 · 3 papers🎓University of TorontoToronto · Canada2022 · 2 papers

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

💡 Concepts 2Key ideas and methods explained on our concept pages.Artificial intelligence · 1 workLarge language models · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Sparks of Artificial General Intelligence: Early experiments with GPT-4 (2023 · 2,439 citations)
    The paper asks whether a large language model trained only to predict the next token can show broad, general-purpose intelligence.
  2. SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification (2026 · 1 citation)
    Moving legacy C code to Rust can remove whole classes of memory bugs. Rule-based converters such as C2Rust produce Rust that leans heavily on unsafe , and plain LLM prompting gives no assurance that the result behaves like the original. SACTOR addresses both problems. A libclang-based C Parser splits a program into…

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 916 times (of the 149,439 papers whose bibliographies we read); 192 citing papers with a page here →.

Updated

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