Systems for ML

Workshop on Systems for ML and Open Source Software at NIPS 2018

December 7, 2018

Workshop on Systems for ML

A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This birth is driven by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale learning systems. The goal of this workshop is to bring together experts working at the crossroads of machine learning, system design and software engineering to explore the challenges faced when building practical large-scale ML systems. In particular, we aim to elicit new connections among these diverse fields, and identify tools, best practices and design principles. We also want to think about how to do research in this area and properly evaluate it. The workshop will cover ML and AI platforms and algorithm toolkits, as well as dive into machine learning-focused developments in distributed learning platforms, programming languages, data structures, GPU processing, and other topics.

This workshop will follow the successful model we have previously run with the ML Systems Workshops @ NIPS and ICML, and SOSP

Call for Papers

We invite participation in the Systems for ML Workshop which will be held in conjunction with NIPS 2018 in Montreal, Canada.

  • Submission Deadline: October 19, 2018 11.59pm PST
  • Author Notification Deadline: November 9, 2018 November 12, 2018
  • Submission Details: See the CFP

Organizing Committee

Program Committee

  • Aparna Lakshmi Ratan - Facebook AI
  • Andrew Tulloch - Facebook
  • Junchen Jiang - University of Chicago
  • Dan Alistarh - IST Austria
  • Asim Kadav - NEC Labs
  • Haoyu Zhang - Princeton University
  • Riley Spahn - Columbia University
  • Christopher Ré - Stanford University
  • Yangqing Jia - Facebook
  • Jinyang Li - New York University
  • Aish Fenton - Netflix
  • Mihir Nanavati - Microsoft Research
  • Mathias Lécuyer - Columbia University
  • Shivaram Venkataraman - University of Wisconsin–Madison
  • Garth Gibson - Vector Institute
  • Vladimir Feinberg - Sisu Data
  • François Belletti - Google
  • Jason Gauci - Facebook
  • Joseph Bradley - Databricks
  • Robert Nishihara - University of California, Berkeley
  • Daniel Lo - Microsoft
  • Joshua Lockerman - Yale University
  • Aurojit Panda - New York University
  • Sarah Bird - Facebook AI
  • Dan Crankshaw - University of California, Berkeley
  • Joseph Gonzalez - University of California, Berkeley
  • Jacob Nelson - Microsoft
  • Zachary DeVito - Facebook
  • Alex Ratner - Stanford University