Author(s)

Alexander Ratner, Stanford University
Dan Alistarh, IST Austria
Gustavo Alons, ETH Zurich
David G. Andersen, Carnegie Mellon University
Peter Bailis, Stanford University
Sarah Bird, Microsoft
Nicholas Carlini, Google
Bryan Catanzaro, Nvidia
Jennifer Chayes, Microsoft
Eric Chung, Microsoft
Bill Dally, Stanford University
Jeff Dean, Google
Inderjit S. Dhillon, University of Texas at Austin
Alexandros Dimakis, University of Texas at Austin
Pradeep Dubey, Intel
Charles Elkan, University of California, San Diego
Grigori Fursin, cTuning Foundation
Gregory R. Ganger, Carnegie Mellon University
Lise Getoor, University of California, Santa Cruz
Phillip B. Gibbons, Carnegie Mellon University
Garth A. Gibson, Vector Institute
Joseph E. Gonzalez, University of California, Berkeley
Justin E. Gottschlich, IntelFollow
Song Han, Massachusetts Institute of Technology
Kim Hazelwood, Facebook
Furong Huang, University of Maryland
Martin Jaggi, EPFL
Kevin Jamieson, University of Washington
Michael I. Jordan, University of California, Berkeley
Gauri Joshi, Carnegie Mellon University
Rania Khalaf, IBM Research
Jason Knight, Intel
Jakub Konecny, Google
Tim Kraska, Massachusetts Institute of Technology
Arun Kumar, University of California,, San Diego
Anastasios Kyrillidis, Rice University
Aparna Lakshmiratan, Facebook
Jing Li, University of Wisconsin-Madison
Samuel Madden, Massachusetts Institute of Technology
H B. McMahan, Google
Erik Meijer, Facebook
Ioannis Mitliagkas, Mila
Rajat Monga, Google
Derek Murray, Google
Kunle Olukotun, Stanford University
Dimitris Papailiopoulos, University of Wisconsin-Madison
Gennady Pekhimenko, University of Toronto
Christopher Re, Stanford University
Theodoros Rekatsinas, University of Wisconsin-Madison
Afshin Rostamizadeh, Google
Christopher De Sa, Cornell University
Hanie Sedghi, Google
Siddhartha Sen, Microsoft
Virginia Smith, Carnegie Mellon University
Alex Smola, Amazon
Dawn Song, University of California, Berkeley
Evan Sparks, Determined AI
Ion Stoica, University of California, Berkeley
Vivienne Sze, Massachusetts Institute of Technology
Madeleine Udell, Cornell University
Joaquin Vanschoren, Eindhoven University of Technology
Shivaram Venkataraman, University of Wisconsin-Madison
Rashmi Vinayak, Carnegie Mellon University
Markus Weimer, Microsoft
Andrew G. Wilson, Cornell University
Eric Xing, Carnegie Mellon University
Matei Zaharia, Stanford University
Ce Zhang, ETH Zurich
Ameet Talwalkar, Carnegie Mellon University

Document Type

Conference Paper

Date of this Version

2019

Abstract

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two.

Share

COinS
 

Date Posted: 18 December 2020