3 citations · 3 across the 4 of their papers we have counts for
5 papers
Strategies for Deploying AI Agents in Production at Scientific User Facilities
Ming Du, Xiangyu Yin, Michael Prince +40
Agentic artificial intelligence (AI) is moving beyond research demonstrations toward production use at scientific user facilities, including light sources, neutron sources, nanosci…
Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Yanqing Luo, Julius Hense, Niklas Prenißl +4
Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primarily rely on heatmaps that hig…
CVEvolve: Autonomous Algorithm Discovery for Unstructured Scientific Data Processing
Ming Du, Xiangyu Yin, Yanqi Luo +4
Scientific data processing often requires task-specific algorithms or AI models, creating a barrier for domain scientists who need to analyze their data but may not have extensive…
EAA: Automating materials characterization with vision language model agents
Ming Du, Yanqi Luo, Srutarshi Banerjee +3
We present Experiment Automation Agents (EAA), a vision-language-model-driven agentic system designed to automate complex experimental microscopy workflows. EAA integrates multimod…
Opportunities for Retrieval and Tool Augmented Large Language Models in Scientific Facilities
Michael H. Prince, Henry Chan, Aikaterini Vriza +6
Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of ma…