Date: 2026-08-31 to 2026-09-04
Location: San Diego, USA
Type: Conference
Website: indico.cern.ch
What Is Fast Machine Learning for Science Conference 2026?
The Fast Machine Learning for Science Conference 2026 is a five-day event hosted by UC San Diego, dedicated to the intersection of high-speed machine learning and experimental science. The conference focuses on the development and deployment of fast ML techniques tailored to the unique constraints of scientific applications—where inference latency, throughput, and energy efficiency are often as critical as model accuracy. Organized under the auspices of the broader fast machine learning community, which has strong ties to CERN and high-energy physics, this gathering brings together researchers, engineers, and domain scientists to address the growing demand for real-time AI in experimental settings.
This year’s edition, running from August 31 to September 4, 2026, in San Diego, builds on previous iterations that have explored topics such as FPGA-based inference, model compression, and trigger systems for particle physics. The conference is particularly relevant as scientific instruments—from telescopes to particle colliders—generate data at rates that outpace traditional processing pipelines. The Fast Machine Learning for Science Conference 2026 aims to bridge the gap between cutting-edge ML research and the practical requirements of scientific discovery, making it a critical forum for anyone working on AI acceleration in research contexts.
Why It Matters for AI Professionals
For AI professionals, the Fast Machine Learning for Science Conference 2026 offers a rare deep dive into the constraints and innovations that drive real-world ML deployment. Unlike many industry conferences that focus on scaling models for cloud environments, this event emphasizes hardware-software co-design, latency-critical inference, and resource-constrained deployment—skills increasingly valuable across sectors like autonomous systems, edge computing, and industrial IoT. Attendees will gain exposure to techniques such as quantization, pruning, and custom hardware accelerators that are directly transferable to commercial applications.
The conference also serves as a networking hub for professionals working at the frontier of ML acceleration. With a focus on experimental science, the event attracts experts from national laboratories, top-tier universities, and hardware vendors who are solving problems that often precede mainstream adoption. For AI professionals looking to stay ahead of the curve in efficient ML, the Fast Machine Learning for Science Conference 2026 provides actionable insights and access to a community that prioritizes performance over hype.
What to Expect
The Fast Machine Learning for Science Conference 2026 is structured around three core pillars: ML acceleration, hardware-software co-design, and real-time ML for experimental science. While the full program is still being finalized, attendees can anticipate the following themes and activities:
- Technical Sessions: Presentations on novel architectures for fast inference, including FPGA, ASIC, and GPU-based solutions tailored to scientific data streams.
- Hardware-Software Co-Design: Workshops and talks exploring how to optimize ML models for specific hardware platforms, from embedded systems to high-performance computing clusters.
- Real-Time ML Applications: Case studies from fields such as particle physics, astrophysics, and neuroscience where ML models must operate within strict latency budgets (microseconds to milliseconds).
- Poster Sessions and Demos: Opportunities to see live implementations of fast ML systems and discuss technical challenges with their creators.
- Keynote Speakers: Details to be announced. Previous editions have featured leading researchers from CERN, Fermilab, and UC San Diego.
Who Should Attend
The Fast Machine Learning for Science Conference 2026 is designed for a technically oriented audience. Primary attendees include machine learning researchers and engineers working on model optimization and acceleration; domain scientists in physics, astronomy, and biology who need real-time inference in their experiments; hardware engineers and system architects developing specialized accelerators; and graduate students or postdocs seeking to apply ML in scientific contexts. The conference is also relevant for industry professionals from sectors like autonomous vehicles, robotics, and telecommunications who face similar latency and throughput constraints. While the focus is on science, the technical depth makes it valuable for any AI practitioner interested in efficient, deployable ML systems.
How to Register
Registration details for the Fast Machine Learning for Science Conference 2026 are available through the official event website hosted on the CERN Indico platform. Pricing information and registration deadlines are to be announced. To stay updated and secure your place, visit the conference page at indico.cern.ch/event/1654479/ and follow the registration link once it becomes active. Early registration is recommended, as space may be limited for this specialized event.
