Andrew Or

I am a fourth year PhD student advised by Michael J. Freedman. I am primarily interested in building systems for deep learning. Previously I spent two years at Databricks developing Apache Spark, of which I am a PMC member. Before that I graduated from UC Berkeley in 2013 with a degree in EECS.

[CV] [Github] [andrewor at princeton.edu]


Conference publications

Resource Elasticity in Distributed Deep Learning.
Andrew Or, Haoyu Zhang, Michael J. Freedman. MLSys 2020.

ReLAQS: Reducing Latency for Multi-Tenant Approximate Queries via Scheduling.
Logan Stafman, Andrew Or, Michael J. Freedman. Middleware 2019. (Best paper)

SLAQ: Quality-Driven Scheduling for Distributed Machine Learning.
Haoyu Zhang, Logan Stafman, Andrew Or, Michael J. Freedman. SOCC 2017. (Best paper)

Scaling Spark in the Real World: Performance and Usability.
Michael Armbrust, Tathagata Das, Aaron Davidson, Ali Ghodsi, Andrew Or, Ion Stoica, Matei Zaharia et al. VLDB 2015.

Troubleshooting blackbox SDN control software with minimal causal sequences.
Colin Scott, Andreas Wundsam, Barath Raghavan, Aurojit Panda, Andrew Or, Scott Shenker et al. SIGCOMM 2014.