Accelerating HEP Science: Inference and Machine Learning at Extreme Scales
Developing Galaxy Image deblending with the gravitational lensing effects and scaling AI/ML algorithms
Microsoft Discovery
Speed up transparent, governed R&D discovery with AI agents and graph knowledge engines.
Streamining intelligent detectors for sPHENIX/EIC
his project develops real-time algorithms for event filtering with tracking detectors for nuclear physics collider experiments.
Continuous Structure Descriptors for XANES Interpretation
Seeking a continuous local structure motif that correlates to X-ray spectral signatures
In-pixel AI for future tracking detectors
This project explores novel AI-on-chip technology for intelligent detectors embedded with sensing technology
Instrument Documentation Search
Provide quick answers about instrument operations by searching ingested manuals.
Machine Learning for Autonomous Control of Scientific User Facilities
BNL will work alongside SLAC, to implement ML algorithm(s) into NSLS-II Operations to interpret accelerator data more intelligently. We intend to train said algorithms with 5+ ye…
Towards Edge Computing: A Software and Hardware Co-Design Methodology for Application-Specific Integrated Circuit (ASIC)-based Scientific Neuromorphic Computing (NC)
One project: Current deep neural network (DNN)-based Artificial Intelligence (AI) algorithms have already been successfully applied to particle physics applications. This project…
AI/ML for Applications in High Energy adn Nuclear Physics
Develop state-of-the-art cycle-consistent GANs to bridge the gap between simulations and experimental data; develop real-time particle tracking with deep learning on field program…
SONIC: AI acceleration as a service
This project focuses on integration of AI hardware for at-scale inference acceleration for particle physics experiments.
RAPIDS3: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence
A SciDAC computer science institute and BNL is co-leading AI team
Use AI/ML to Enhance the Bioimaging Capabilities at Brookhaven National Laboratory (BNL)
Three projects: use expertise in ML as one element in building an integrated multiscale bioimaging capability at BNL; use AI/ML tools use to accelerate the analysis of protein str…
Automated sorting of high repetition rate coherent diffraction data from XFELS
'Coherent X-rays are routinely provided today by the latest Synchrotron and X-ray Free-electron Laser Sources. When these diffract from a crystal containing defects, interference…
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
mass3
GenAI on STEM-optimized LLMs
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…
Collaborative Machine learning platform for Scientific Discovery
New advances in scientific applied machine learning (ML) offer an opportunity to leverage the commonalities, scientific insights and collected experience of the larger scientific…
Denoising Diffusion to Accelerate Detector Simulation
This program aims to develop generative models for quickly simulating showers of particles in calorimeters for LHC experiments
Tackling Solid-State Electrochemical Interfaces from Structure to Function Utilizing HPC and Machine Learning Tools
Applying AI/ML methods to discover better solid state battery interface material using HPC
5G-enabled Reliable and Decentralized IoT Framework with Blockchain
We propose to develop an end-to-end, 5G-enabled, reliable, and decentralized IoT framework that improves data collection and communication among edge computing devices for science…
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
AI/ML in Particle Accelerator Controls System
Improving the safety and performance of particle accelerator operations through artificial intelligence assisted control systems.