MI8 Collimators Surogate Model
This project is working to create a ML surogate model of the exisitng MI8 collimation system. The purpose of the ML model is to aid in the tuning of the collimation system for acc…
Unleashing AI Transformer Models on FPGAs for Accelerating LHC and Particle Physics
This project centers on the deployment of Transformer models for Field Programmable Gate Arrays (FPGA), in order to seamlessly integrate AI capabilities into particle physics expe…
Machine Learning for Linac Improved Performance
In Linacs at FNAL and J-PARC, the current emittance optimization procedure is limited to manual adjustments of a few parameters; using a larger number is not practically feasible…
Next-Generation Beam Cooling and Control with Optical Stochastic Cooling
This program leverages the physics and technology of optical stochastic cooling (OSC) to explore new possibilities in beam control and sensing. The planned architecture and perfo…
In-storage computing for multi-messenger astronomy in neutrino experiments and cosmological surveys
This project aims to address the big-data challenges and stringent time constraints facing multi-messenger astronomy (MMA) in neutrino experiments and cosomological surveys. Inste…
high level synthesis for machine learning (previously hls4ml)
This project develops hardware-software AI codesign tools for FPGAs and ASICs for algorithms running at the extreme edge.
Streamining intelligent detectors for sPHENIX/EIC
his project develops real-time algorithms for event filtering with tracking detectors for nuclear physics collider experiments.
In-pixel AI for future tracking detectors
This project explores novel AI-on-chip technology for intelligent detectors embedded with sensing technology
SONIC: AI acceleration as a service
This project focuses on integration of AI hardware for at-scale inference acceleration for particle physics experiments.
Uncertainty Quantification and Instrument Automation to enable next generation cosmological discoveries
This project will develop AI-based tools to enable critical sectors for near-future cosmic applications. Uncertainty quantification is essential for performing discovery science n…
READS: Real-time Edge AI for Distributed Systems
This project will develop and deploy low-latency controls and prediction algorithms at the Fermilab accelerator complex
Simulation-based inference for cosmology
This project will develop and use simulation-based inference to estimate cosmological parameters related to cosmic acceleration in the early and late universe — via the cosmic mic…
Denoising Diffusion to Accelerate Detector Simulation
This program aims to develop generative models for quickly simulating showers of particles in calorimeters for LHC experiments
Extreme data reduction for the edge
This projects develops AI algorithms and tools for near-sensor data reduction in custom hardware.
Machine Learning for Accelerator Operations Using Big Data Analytics / L-CAPE
Big data analytics for anomaly prediction and classification, enabling automatic mitigation, operational savings, and predictive maintenance of the Fermilab LINAC