Plenary
Future direction and current capabilities of FTS software - Thomas Proffen
STS Instrument Software Needs - Shuo Qian
STS Experiment Automation Needs - Yaohua Liu
STS FTS/STS Data Acquisition & Automation - Matt Pearson
Inverse and Data Analytic methods for Experimental Facilities - Rick Archibald
AI Initiative at ORNL - Pradeep Ramuhalli
Interconnected Science Ecosystem - Ben Mintz
HPC and AI Convergence in Edge-to-Exascale Science Infrastructures - Arjun Shankar
Towards Autonomous Hyperspectral Computed Tomography Instruments - Singanallur Venkatakrishnan
AI-Guided Experimentation in Multi-dimensional Transmission Electron Microscopy- Maxim Ziatdinov
Breakout sessions
Experiment Automation
Autonomous X-ray Scattering Experiments at NSLS-II - Masafumi Fukuto
Using Bluesky for Data Acquisition - Tom Caswell
Overall System Architecture
SNS First Target Station Architecture - Peter F. Peterson
Overview of NSLS-II Computational Infrastructure and Plans - Stuart Campbell
DAQ Architecture for Instruments at the European Spallation Source - Tobias Richter
Strategy for STS Software Development
Software Development Lifecycle (SDLC): Overview - John Hetrick
Thoughts and lessons learned on software development for the European Spallation Source project - Jon Taylor
Unique Software Needs by STS
Reduction and Analysis Challenges Across the STS Instrument Suite - Garrett E. Granroth
Atomistic Modeling and Machine Learning for Neutron Scattering Data Analysis - Yongqiang (YQ) Cheng
Machine Learning for Neutron Data Analytics - Guannan Zhang
