September 26-27, 2016
New York, NY

Benefits of scaling machine learning

Reza Zadeh (Matroid | Stanford)
2:20pm–3:00pm Monday, 09/26/2016
Location: 3D08

What you'll learn

  • Explore two projects that have benefitted greatly through scaling: obtaining leading results on the Princeton ModelNet object recognition task and matrix computations and optimization in Apache Spark
  • Description

    Machine learning is evolving to utilize new hardware, such as GPUs and large commodity clusters. University and industry researchers have been using these new computing platforms to scale machine learning across many dimensions. Reza Zadeh presents two projects that have benefitted greatly through scaling: obtaining leading results on the Princeton ModelNet object recognition task and matrix computations and optimization in Apache Spark.

    Photo of Reza Zadeh

    Reza Zadeh

    Matroid | Stanford

    Reza Bosagh Zadeh is founder and CEO at Matroid and an adjunct professor at Stanford University, where he teaches two PhD-level classes: Distributed Algorithms and Optimization and Discrete Mathematics and Algorithms. His work focuses on machine learning, distributed computing, and discrete applied mathematics. His awards include a KDD best paper award and the Gene Golub Outstanding Thesis Award. Reza has served on the technical advisory boards of Microsoft and Databricks. He is the initial creator of the linear algebra package in Apache Spark. Through Apache Spark, Reza’s work has been incorporated into industrial and academic cluster computing environments. Reza holds a PhD in computational mathematics from Stanford, where he worked under the supervision of Gunnar Carlsson. As part of his research, Reza built the machine learning algorithms behind Twitter’s who-to-follow system, the first product to use machine learning at Twitter.