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.
Topics
Topics include, but are not limited to:
Machine Learning Algorithm Design & Optimization
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Novel efficient architectures
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Hyperparameter optimization and tuning
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Model compression (quantization, sparsity)
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Hardware/software co-design for ML efficiency
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Large language model optimization and implementation
Accelerated Inference & Real-Time Processing
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Low-latency ML for scientific experiments
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FPGA/NPU/GPU-based ML acceleration
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ML for trigger systems and data acquisition
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On-detector and edge inference
Scientific Applications of Fast ML
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High-energy physics, astrophysics, and astronomy
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Space science and satellite-based ML
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Genomics and medical imaging
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Climate and environmental modeling
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Biological science and neuroscience
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Fusion
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Quantum computing
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Material science
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Robotics
Scalable & Distributed ML Systems
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Cloud-based, accelerated ML processing
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Distributed inference
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Acceleration-as-a-service
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ML compilers and runtimes
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Benchmarks and datasets
Advanced Hardware & Computing Architectures
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Specialized AI accelerators
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Tools and methodologies for accelerating ML algorithms
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Heterogeneous computing platforms for ML
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Beyond CMOS
Important Deadlines
- Abstract Submission: June 1, 2026
- Early Bird Registration Deadline: July 15, 2026
We welcome abstracts for:
- Scientific talks
- Posters
- 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)