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Jun Shern Chan

Jun Shern Chan has 2 papers in our library, in 2024–2025; often with Dane Sherburn, Evan Mays, Giulio Starace.

2 papers in our library · 2024–2025

OpenAlex · h-index 4 · 7 works · 216 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 2,499 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #6188

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

🎓New York UniversityNew York · United States2022–2023 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (2024 · 140 citations)
    MLE-bench addresses the lack of benchmarks that test whether AI agents can carry out machine learning engineering autonomously from start to finish, rather than solving isolated coding problems.
  2. PaperBench: Evaluating AI's Ability to Replicate AI Research (2025 · 86 citations)
    PaperBench measures whether AI agents can independently reproduce recent machine learning research. Agents receive 20 Spotlight and Oral papers from ICML 2024, covering 12 topic areas. For each paper, an agent must write a complete codebase from scratch, without access to the authors' original code, and include a…

All works

6 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. MLE-bench: Evaluating Machine Learning Agents on Machine Learning EngineeringarXiv:2410.07095Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, et al.2024 · 140 citations
  2. PaperBench: Evaluating AI's Ability to Replicate AI ResearcharXiv:2504.01848Giulio Starace, Oliver Jaffe, Dane Sherburn, et al.2025 · 86 citations
  3. Training Language Models with Language Feedback at Scale ↗arXiv:2303.16755 · no summary yetJérémy Scheurer et al.2023 · 85 citations
  4. Training Language Models with Language Feedback ↗arXiv (Cornell University) · no summary yetJérémy Scheurer et al.2022 · 58 citations
  5. Improving Code Generation by Training with Natural Language Feedback ↗arXiv:2303.16749 · no summary yetAngelica Chen et al.2023 · 47 citations
  6. How Would the Viewer Feel? Estimating Wellbeing from Video Scenarios ↗neural information processing systems · no summary yetMantas Mazeika et al.2022 · 4 citations

 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 2,499 times (of the 149,439 papers whose bibliographies we read); 654 citing papers with a page here →.

Updated

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