Choose timezone
Your profile timezone:
Fast Machine Learning for Science Conference 2026
→
US/Pacific
, , , , , , , , ,
Description
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: 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.
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
- (Optional) Extended Abstract Submission: June 15, 2026
- Early Bird Registration: July 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 (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!
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)
Contact
Registration
Registration
Participants