EC-Cache: Load-Balanced, Low-Latency Cluster Caching with Online Erasure Coding. - K. V. Rashmi; Mosharaf Chowdhury; Jack Kosaian; Ion Stoica; Kannan Ramchandran (OSDI, 2016)

Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation. - Jack Kosaian; K. V. Rashmi; Shivaram Venkataraman (CoRR, 2018)

Vantage: optimizing video upload for time-shifted viewing of social live streams. - Devdeep Ray; Jack Kosaian; K. V. Rashmi; Srinivasan Seshan (SIGCOMM, 2019)

Parity models: erasure-coded resilience for prediction serving systems. - Jack Kosaian; K. V. Rashmi; Shivaram Venkataraman (SOSP, 2019)

Parity Models: A General Framework for Coding-Based Resilience in ML Inference. - Jack Kosaian; K. V. Rashmi; Shivaram Venkataraman (CoRR, 2019)

Learning-Based Coded Computation. - Jack Kosaian; K. V. Rashmi; Shivaram Venkataraman (IEEE J. Sel. Areas Inf. Theory, 2020)

Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size. - Jack Kosaian; Amar Phanishayee; Matthai Philipose; Debadeepta Dey; Rashmi Vinayak (ICML, 2021)

Arithmetic-intensity-guided fault tolerance for neural network inference on GPUs. - Jack Kosaian; K. V. Rashmi (SC, 2021)

ECRM: Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding. - Kaige Liu; Jack Kosaian; K. V. Rashmi (CoRR, 2021)

A Study on the Intersection of GPU Utilization and CNN Inference. - Jack Kosaian; Amar Phanishayee (CoRR, 2022)

Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding. - Tianyu Zhang; Kaige Liu; Jack Kosaian; Juncheng Yang; Rashmi Vinayak (Proc. VLDB Endow., 2023)

EVT: Accelerating Deep Learning Training with Epilogue Visitor Tree. - Zhaodong Chen; Andrew Kerr; Richard Cai; Jack Kosaian; Haicheng Wu; Yufei Ding 001; Yuan Xie 001 (ASPLOS (3), 2024)

Rethinking Erasure-Coding Libraries in the Age of Optimized Machine Learning. - Jiyu Hu; Jack Kosaian; K. V. Rashmi (HotStorage, 2024)