Overview of AI Applications in Experimental Hadronic Physics

This talk presents a focused overview of recent AI/ML applications in experimental hadronic physics, emphasizing data analysis perspectives and highlighting emerging ML-driven techniques with the potential for broader adoption. These techniques have the potential to significantly enhance current and future experiments, including the future Electron-Ion Collider. The talk will explore how ML-driven approaches enable fast, high-fidelity simulations, improve PID, and offer the potential for data-driven learning from real data, provided high-purity samples are available. While I will not specifically delve into AI/ML for streaming readout—an area deserving its own dedicated discussion—I will highlight relevant connections and implicitly address some aspects of near real-time analysis. Beyond these immediate applications, deep learning integration within analysis pipelines facilitates holistic event-level learning, leveraging all available event information. This approach, when coupled with uncertainty quantification, allows for more robust analysis frameworks. Additionally, recent advancements in distributed multi-objective optimization are opening new possibilities for detector optimization, alignment, and calibration, enabling the determination of tradeoff solutions in complex, constrained parameter spaces. Time permitting, I will also touch on the potential for designing agentic scientific workflows, leveraging specialized AI agents to assist with data retrieval, summarization, and experimental guidance—drawing on ongoing initiatives that have only recently begun exploring this research direction.