Due to the surge in the Omicron Variant and based on feedback from the MLSys Program Committee, the MLSys Board and 2022 Chairs have collectively decided to postpone the conference. The conference will now run from August 29 through September 1st. The conference will remain a hybrid event with the in-person component held at the Santa Clara Convention Center. We are working closely with the Santa Clara Convention Center to ensure that appropriate safety protocols are in place.
Register starting Jun 20 01 PM PDT »Register 06/20 »
The non-profit corporation that runs MLSys aims to foster the exchange of research advances at the intersection of machine learning and systems, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.
About the Conference
The MLSys community recognized that many critical future challenges are at the intersection of Machine Learning and Systems. The community was created to solve these exciting problems by recognizing the needs for scaling interdisciplinary collaboration as well as the importance of working together between industry and academia.
The community has come together to create a new conference for research spanning ML and systems, MLSys. The goal is to make a home for exceptional new research, a venue to showcase the growing trend, and more importantly a way to scale ever more complex research collaboration.
The steering committee and program committees consist of 50 leading members of emerging machine learning systems area coming from industry and academia with expertise ranging from machine learning to systems to security. The MLSys community welcomes industry participation and sponsorships, we believe the investment will pay dividend in both technology advancement and industry growth for years to come.
The Conference on Machine Learning and Systems targets research at the intersection of machine learning and systems. The conference aims to elicit new connections amongst these fields, including identifying best practices and design principles for learning systems, as well as developing novel learning methods and theory tailored to practical machine learning workflows. Topics include:
|- Efficient model training, inference, and serving||- Distributed and parallel learning algorithms|
|- Privacy and security for ML applications||- Testing, debugging, and monitoring of ML applications|
|- Fairness, interpretability and explainability for ML applications||- Data preparation, feature selection, and feature extraction|
|- ML programming models and abstractions||- ML compilers and runtime|
|- Programming languages for machine learning||- Visualization of data, models, and predictions|
|- Specialized hardware for machine learning||- Hardware-efficient ML methods|
|- Machine Learning for Systems||- Systems for Machine Learning|
|Conference Sessions||Mon Aug 29th through Sep 1st|
|Paper Submission deadline (extended from the 8th)||Oct 15 '21 08:00 PM UTC *|
|Workshop Application Opens||Oct 28 '21 12:00 AM UTC *|
|SponsorPortalOpen||Nov 10 '21 07:07 PM UTC *|
|Workshop Application Close||Nov 25 '21 01:00 AM UTC *|
|Workshop Notifications||Dec 06 '21 01:00 AM UTC *|
|Paper Decision Notification||Jan 15 '22 07:00 AM UTC *|
|CameraReadyDeadline||Mar 04 '22 09:00 PM UTC *|
|Workshop Submission Deadline (for workshop paper submissions)||Apr 18 '22 07:00 AM UTC *|
|Workshop Submission Notifications (for workshop paper submissions)||May 06 '22 07:00 AM UTC *|
|Registration Opens||Jun 20 '22 08:00 PM UTC *|
|Early pricing before this date.||Aug 01 '22 08:00 PM UTC *|
|Sponsor Payment Deadline||Aug 06 '22 01:00 AM UTC *|
|Last chance for a refund on registration fees||Aug 21 '22 08:00 PM UTC *|
|Registration Close||Sep 02 '22 08:00 PM UTC *|
|All dates »||
We would like to thank our (growing) list of sponsors for their strong support for the MLSys Community. If your company would like to sponsor, please let us know.View MLSys 2022 sponsors »Become a 2022 Sponsor »
Diana Marculescu, UT Austin
Yuejie Chi, CMU
Workshop and Tutorial Chairs
Chris De Sa, Cornell
Boyue Li, CMU
Wen-ming Ye, Amazon
Jakub Konečný, Google
Artifact Evaluation Chairs
Tianqi Chen, CMU
Panel and Young Professional Activities Chair
Udit Gupta, Harvard/FAIR
The organizers can be contacted here.
Ameet Talwalkar (President)
Virginia Smith (Secretary)
Inderjit Dhillon (Treasurer)
Michael I. Jordan