Zongheng Yang

Favorite coffee shops:
Verve, West Santa Cruz
Cosube, Portland
Coffee Collective, Copenhagen

I'm a PhD candidate in the RISELab at UC Berkeley, where I'm advised by Ion Stoica. My research focuses on learning and optimizing the capabilities of data systems using deep learning.

In my previous life, I worked at Google Brain (TensorFlow & deep learning research; 2016-2017), and interned at Microsoft Research (2019), Databricks (2014), and Twitter (2013). I received my Bachelors in computer science and math from UC Berkeley. During my undergrad, I worked on various big data systems in the AMPLab.


Earlier Work

Earlier Projects

  • Ray: contributed a fault-tolerant and highly available control-state storage (chain-replicated redis).
  • TensorFlow: my public work at Google Brain included working on TensorFlow. One of my contributions is a new checkpoint format with much better efficiency.
  • Succinct/ZipG: a data store that queries directly on compressed files. I worked on ZipG, a distributed graph store on top that focuses on memory-efficiency.
  • SparkR: an R front-end for Apache Spark. Talk: SparkR: Enabling Interactive Data Science at Scale. Spark Summit, June 2014, San Francisco.
  • Apache Spark: I was a Spark contributor, mostly on Spark SQL.