As experimental methods continue to evolve, generating increasingly complex and high-resolution datasets, machine learning (ML) is becoming an essential tool across numerous scientific disciplines. This conference will explore emerging ML methods and their applications in scientific discovery, focusing on processing technologies and strategies to accelerate deep learning and inference.
Fee Waivers: Thanks to generous support from our sponsors, the NSF HDR Institute AI-Accelerated Algorithms for Data-Driven Discovery (A3D3) and Exploring Neural Network Processors for AI in Science and Engineering (Voyager), we are offering in-person registration fee waivers (subject to funding availability) for graduate students on a first come first serve basis. If funds are still available, we will also open up fee waivers to undergraduate students and early-career researchers (<7 years since PhD) with financial need. Please indicate in the registration form if you are seeking a registration fee waiver and do not pay the fee.
Important Deadlines

• Abstract Submission: June 1, 2026
• (Optional) Extended Abstract Submission: June 15, 2026
• Early Bird Registration: July 15, 2026
  
  Topics include, but are not limited to:
  - Machine Learning Algorithm Design & Optimization
  - Accelerated Inference & Real-Time Processing
  - Scientific Applications of Fast ML
  - Scalable & Distributed ML Systems
  - Advanced Hardware & Computing Architectures 

We welcome abstracts for:

• Presentations and/or Posters
• • New for 2026: Submitters will have the option to submit a 4-page extended abstract (paper) to OpenReview (details to follow)
• Tutorials
• Topical (birds-of-a-feather) sessions

More information and registration details will follow. We look forward to welcoming you to San Diego this September!

This hands-on workshop provides a practical introduction to command-line Linux and high-performance computing (HPC), tailored for students and researchers new to computational environments. Participants will learn the fundamentals of using the Linux command line for navigating directories, manipulating files, and executing basic shell commands. This session also covers essential skills for working on HPC systems, including secure file transfer with tools like scp and rsync, connecting to remote clusters via SSH, and basic file manipulation using vi. By the end of the workshop, attendees will be equipped to effectively interact with ARCC2, UC’s research computing cluster, and have the foundation necessary to begin implementing complex research workflows.

This workshop introduces participants to SLURM, a widely used job scheduling system for high-performance computing (HPC) environments, and is designed as a follow-up to the Intro to Linux/Intro to HPC session. Familiarity with Linux command line is expected. Attendees will learn how to submit, monitor, and manage computational jobs on UC’s supercomputing cluster ARCC2 using core SLURM commands such as sbatch, squeue, scancel, and salloc. The workshop will cover key concepts including partitions, job scripts, resource requests, and job arrays, providing practical examples to help users optimize their computing workflows. By the end of the workshop, attendees will have the foundational tools needed to work effectively in SLURM-managed environments.