The Atlas Cluster Trace Repository. - George Amvrosiadis; Michael Kuchnik; Jun Woo Park; Chuck Cranor; Gregory R. Ganger; Elisabeth Moore; Nathan DeBardeleben (login Usenix Mag., 2018)

Efficient Augmentation via Data Subsampling. - Michael Kuchnik; Virginia Smith (ICLR (Poster), 2019)

File systems unfit as distributed storage backends: lessons from 10 years of Ceph evolution. - Abutalib Aghayev; Sage A. Weil; Michael Kuchnik; Mark Nelson 002; Gregory R. Ganger; George Amvrosiadis (SOSP, 2019)

The Case for Custom Storage Backends in Distributed Storage Systems. - Abutalib Aghayev; Sage A. Weil; Michael Kuchnik; Mark Nelson 002; Gregory R. Ganger; George Amvrosiadis (ACM Trans. Storage, 2020)

File Systems Unfit as Distributed Storage Back Ends: Lessons from 10 Years of Ceph Evolution. - Abutalib Aghayev; Sage A. Weil; Michael Kuchnik; Mark Nelson 002; Greg Ganger; George Amvrosiadis (login Usenix Mag., 2020)

Progressive Compressed Records: Taking a Byte out of Deep Learning Data. - Michael Kuchnik; George Amvrosiadis; Virginia Smith (Proc. VLDB Endow., 2021)

Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. - Michael Kuchnik; Ana Klimovic; Jiri Simsa; Virginia Smith; George Amvrosiadis (MLSys, 2022)

Validating Large Language Models with ReLM. - Michael Kuchnik; Virginia Smith; George Amvrosiadis (MLSys, 2023)

Beyond Model Efficiency: Data Optimizations for Machine Learning Systems - Michael Kuchnik (2023)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Pieter Gijsbers; Joan Giner-Miguelez; Nitisha Jain; Michael Kuchnik; Quentin Lhoest; Pierre Marcenac; Manil Maskey; Peter Mattson; Luis Oala; et al. (DEEM@SIGMOD, 2024)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Luca Foschini 002; Joan Giner-Miguelez; Pieter Gijsbers; Sujata S. Goswami; Nitisha Jain; Michalis Karamousadakis; Michael Kuchnik; Satyapriya Krishna; Sylvain Lesage; et al. (NeurIPS, 2024)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Joan Giner-Miguelez; Nitisha Jain; Michael Kuchnik; Quentin Lhoest; Pierre Marcenac; Manil Maskey; Peter Mattson; Luis Oala; Pierre Ruyssen; et al. (CoRR, 2024)

Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)

A Standardized Machine-readable Dataset Documentation Format for Responsible AI. - Nitisha Jain; Mubashara Akhtar; Joan Giner-Miguelez; Rajat Shinde; Joaquin Vanschoren; Steffen Vogler; Subhra S. Goswami; Yuhan Rao; Tim Santos; Luis Oala; Michalis Karamousadakis; Manil Maskey; et al. (CoRR, 2024)

Revisiting Reliability in Large-Scale Machine Learning Research Clusters. - Apostolos Kokolis; Michael Kuchnik; John Hoffman; Adithya Kumar; Parth Malani; Faye Ma; Zachary DeVito; Shubho Sengupta; Kalyan Saladi; Carole-Jean Wu (HPCA, 2025)

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench. - Edan Toledo; Karen Hambardzumyan; Martin Josifoski; Rishi Hazra; Nicolas Mario Baldwin; Alexis Audran-Reiss; Michael Kuchnik; Despoina Magka; Minqi Jiang; Alisia Maria Lupidi; Andrei Lupu; Roberta Raileanu; et al. (CoRR, 2025)