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MLSys 2025

The Eighth Annual Conference on Machine Learning and Systems

Santa Clara Convention Center

Mon May 12th through Thu the 15th

MLSys Conference Venue
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Announcements

Hotel Reservations 

Make your reservations now.  The best rates end on Friday  April 18th at 11:59 pm. pacific time zone.   Rooms at best pricing are available only from May 11 - 16th.  

MLSys has contracted hotel guest rooms for the Conference at group pricing, requiring reservations only through this link at the Hilton Santa Clara. We thank you for supporting the conference booking through our room blocks!  Best rates are available until April 18, 2025.
Reserve at Hilton Santa Clara

Sponsors

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 2025 sponsors »Become a 2025 Sponsor Sponsor Info »

Conference Overview

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
  • Large language model (LLM) training, fine-tuning, and inference 
  • Compound AI systems and AI agent systems
  • Distributed and federated learning algorithms
  • Privacy and security for ML applications
  • ML methods for job scheduling in computing systems
  • Testing, debugging, and monitoring of ML applications
  • Fairness, interpretability, and explainability for ML applications
  • Data preparation and data cleaning
  • ML programming models and abstractions
  • Programming languages for machine learning
  • ML compilers and runtimes
  • Visualization of data, models, and predictions
  • Specialized hardware for machine learning
  • LLM-based hardware design or system optimization techniques 
  • Hardware-efficient ML methods
  • Machine learning benchmarks, datasets, and tooling

Organizing Committee

General Chair

  • Matei Zaharia (UC Berkeley and Databricks)

Program Chairs

  • Gauri Joshi (Carnegie Mellon University)
  • Yingyan (Celine) Lin (Georgia Tech)

Artifact Evaluation Chairs

  • Xupeng Miao (Purdue)
  • Minjia Zhang (UIUC)

Workflow Chair

  • Zhenyu (Sherry) Xue (MLSys)

Panel and Young Professional Activities Chairs

  • Dan Fu (USCD)
  • Hui Guan (University of Massachusetts, Amherst; Amazon)

Sponsor Chair

  • Wenming Ye (Google)

Publications Chairs

  • Tian Li (U Chicago)
  • Ang Li (University of Maryland, College Park)

Travel Grant Chairs

  • Zhijian Liu (UCSD)
  • Hanrui Wang (UCLA)

Logistics Chairs

  • Mary Ellen Perry (MLSys)
  • Susan Perry (MLSys Staff)
  • Max W. (MLSys Staff)

Board

Tianqi Chen (President) 
Phillip Gibbons (Secretary) 
Christopher De Sa (Treasurer) 
Gennady Pekhimenko 
Ameet Talwalkar
Carole Jean Wu
Dawn Song
Diana Marculescu
Michael Carbin 
Yuejie Chi

Steering Committee

Jennifer Chayes 
Bill Dally 
Jeff Dean 
Michael I. Jordan 
Yann LeCun 
Fei-Fei Li 
Dawn Song 
Eric Xing

Mission Statement 

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. With the growing importance of holistic machine learning and systems approaches when building real-world AI systems, the MLSys conference plays an even more significant role in today’s AI landscape.

Interdisciplinary Focus: MLSys uniquely bridges the gap between machine learning and systems design. In the era of generative AI, which requires significant computational resources and innovative algorithms, this interdisciplinary approach is crucial for developing more efficient and effective AI systems.

Optimization of AI Systems: The conference discusses not just AI models but also the systems that support them. This includes topics like hardware acceleration, distributed computing, and energy-efficient designs, all of which are vital for running large-scale AI models efficiently.

Advancements in Modeling: The conference showcases the latest advancements in machine learning models with practical system considerations. With rapid developments in this field, MLSys provides a platform for researchers and practitioners to present their latest findings, contributing to the collective knowledge and progress in intelligent systems.

Industry and Academic Collaboration: MLSys is a meeting point for both industry leaders and academic researchers. This collaboration fosters the translation of academic research into practical, real-world applications in the field of machine learning and systems.

Ethical and Societal Implications: As AI systems become more prevalent, its societal and ethical implications become more significant. MLSys provides a forum for discussing these implications, ensuring that advancements in AI are aligned with ethical standards and societal needs.

Education and Training: By bringing together leading experts in the field, MLSys plays a role in education and training for the next generation of AI and systems researchers and practitioners, who will be at the forefront of developing and deploying AI technologies.

The steering committee and program committees consist of 110 leading members of the AI 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 dividends in both technology advancement and industry growth for years to come.