jkosaian at cs.cmu.edu
I am a fifth-year Ph.D. student in the Computer Science Department at Carnegie Mellon University, where I am fortunate to work with Rashmi Vinayak as part of the Parallel Data Lab.
My research interests fall broadly within computer systems and networks. My current focus is resource efficiency and reliability in machine learning inference and training systems.
I was previously an undergraduate at the University of Michigan, where I worked with Mosharaf Chowdhury.
- Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs
Jack Kosaian, K. V. Rashmi
International Conference on High Performance Computing, Networking, Storage and Analysis (SC), 2021
- Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size
Jack Kosaian, Amar Phanishayee, Matthai Philipose, Debadeepta Dey, K. V. Rashmi
International Conference on Machine Learning (ICML), 2021
- Parity Models: Erasure-Coded Resilience for Prediction Serving Systems
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
ACM Symposium on Operating Systems Principles (SOSP), 2019
[code] [slides] [video]
- Vantage: Optimizing Video Upload for Time-shifted Viewing of Social Live Streams
Devdeep Ray, Jack Kosaian, K. V. Rashmi, Srinivasan Seshan
ACM SIGCOMM 2019
- EC-Cache: Load-Balanced, Low-Latency Cluster Caching with Online Erasure Coding
K. V. Rashmi, Mosharaf Chowdhury, Jack Kosaian, Ion Stoica, Kannan Ramchandran
USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016
I have been fortunate to be a teaching assistant for the following courses:
15-712: Advanced Operating Systems and Distributed Systems (Spring 2021)
15-440: Distributed Systems (Spring 2020)
EECS 370: Introduction to Computer Organization (Fall 2015, Winter 2016)