MLSYS 2022 will be in person only, no hybrid/virtual attendance supported. We strongly encourage attendees to wear masks, be vaccinated, and to social distance. We will follow all government and venue guidelines. Face shields will be available for poster presenters; masks will be available for registrants and strongly encouraged. If you are having any Covid symptoms, we ask that you stay home or at your hotel.
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 2024 Sponsor
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
- Privacy and security for ML applications
- Fairness, interpretability and explainability for ML applications
- ML programming models and abstractions
- Programming languages for machine learning
- Specialized hardware for machine learning
- Machine Learning for Systems
- Distributed and parallel learning algorithms
- Testing, debugging, and monitoring of ML applications
- Data preparation, feature selection, and feature extraction
- ML compilers and runtime
- Visualization of data, models, and predictions
- Hardware-efficient ML methods
- Systems for Machine Learning
|Nov 10 '21 11:07 AM PST *
|Mar 18 '22 01:00 PM PDT *
|Jun 20 '22 01:00 PM PDT *
|Jun 29 '22 (Anywhere on Earth)
|Jul 06 '22 06:00 PM PDT *
|Early pricing before this date.
|Aug 01 '22 01:00 PM PDT *
|Sponsor Payment Deadline
|Aug 05 '22 06:00 PM PDT *
|Last chance for a refund on registration fees
|Aug 11 '22 11:59 PM PDT *
|SlidesLive recordings due
|Aug 14 '22 (Anywhere on Earth)
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
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.