Fast Machine Learning for Science Conference 2026

The Fast Machine Learning for Science Conference 2026 will be hosted by UC San Diego from August 31 to September 4, 2026.

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: All available fee waivers have now been claimed. Thanks to the NSF HDR Institute AI-Accelerated Algorithms for Data-Driven Discovery (A3D3) and Exploring Neural Network Processors for AI in Science and Engineering (Voyager) for support.

Confirmed Plenary Speakers

Topics

Topics include, but are not limited to:

Machine Learning Algorithm Design & Optimization

• Novel efficient architectures  

• Hyperparameter optimization and tuning  

• Model compression (quantization, sparsity)  

• Hardware/software co-design for ML efficiency 

• Large language model optimization and implementation

Accelerated Inference & Real-Time Processing

• Low-latency ML for scientific experiments

• FPGA/NPU/GPU-based ML acceleration 

• ML for trigger systems and data acquisition  

• On-detector and edge inference

Scientific Applications of Fast ML

• High-energy physics, astrophysics, and astronomy

• Space science and satellite-based ML  

• Genomics and medical imaging  

• Climate and environmental modeling

• Biological science and  neuroscience

• Fusion

• Quantum computing   

• Material science

• Robotics

Scalable & Distributed ML Systems

• Cloud-based, accelerated ML processing  

• Distributed inference

• Acceleration-as-a-service

• ML compilers and runtimes

• Benchmarks and datasets

Advanced Hardware & Computing Architectures

• Specialized AI accelerators

• Tools and methodologies for accelerating ML algorithms

• Heterogeneous computing platforms for ML  

• Beyond CMOS

Important Deadlines

• Abstract Submission: June 1, 2026 June 15, 2026
• (Optional) Extended Abstract Submission: June 15, 2026 July 1, 2026
• Early Bird Registration: July 15, 2026
• (Optional) Extended Abstract Camera-Ready Deadline: August 15, 2026

We welcome abstracts for:

• Presentations and/or Posters
  • New for 2026: Submitters will have the option to submit a 4-page extended abstract to OpenReview (See submission instructions)
• 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!

Best regards,
On behalf of the Organizers

Scientific Committee

• Thea K. Årrestad (ETH Zürich)
• Javier Duarte (UCSD)
• Phil Harris (MIT)
• Burt Holzman (Fermilab)
• Scott Hauck (U. Washington)
• Shih-Chieh Hsu (U. Washington)
• Sergo Jindariani (Fermilab)
• Mia Liu (Purdue University)
• Allison M. Deiana (Southern M. U.)
• Mark Neubauer (U. Illinois U-C)
• Jennifer Ngadiuba (Fermilab)
• Maurizio Pierini (CERN)
• Sioni Summers (CERN)
• Alex Tapper (Imperial College)
• Nhan Tran (Fermilab)

Organizing Committee

• Javier Duarte (UCSD) - Chair
• Ryan Kastner (UCSD)
• Aobo Li (UCSD)
• Floor Broekgaarden (UCSD)
• Liang Yang (UCSD)
• Melissa Quinnan (UCSD)
• Yi-Zhuang You (UCSD)
• Daniel Diaz (SDSC)
• Amitava Majumdar (SDSC)
• Alexander Tuna (SDSC)
• Zhijian Liu (UCSD)
• Chris Theissen (UCSD)