Early Career Award Recipient Aleksandra Ćiprijanović Aims to Create Universal AI Analysis Framework

Why It Matters

Domain‑shift limits AI reliability in particle physics, so a cross‑frontier framework can accelerate discovery and reduce costly re‑training. The initiative also showcases how early‑career federal funding can seed reusable scientific AI tools.

Key Takeaways

• Domain shift degrades AI trained on simulations.
• DOE award funds universal AI framework for HEP.
• Modular software will accept any dataset, model, task.
• Starts with cosmology, expands to collider, neutrino data.
• Fermilab’s computing resources enable broad testing and adoption.

Pulse Analysis

Artificial intelligence has become a cornerstone of data analysis in high‑energy physics, yet its promise is often hampered by the domain‑shift problem. Researchers typically train models on Monte‑Carlo simulations because real detector data are scarce or noisy. However, approximations in the simulations and unknown physical effects create a mismatch that degrades model performance when applied to actual experiments. This gap has forced physicists to invest significant time in bespoke calibrations, limiting the speed at which new insights can be extracted from massive datasets.

Ćiprijanović’s award‑backed project seeks to eliminate that bottleneck by delivering a universal, plug‑and‑play AI framework. Built with a modular architecture, the software lets users import any high‑energy physics dataset, select from a library of machine‑learning models, and specify the scientific task—whether it’s event classification, anomaly detection, or parameter inference. By abstracting away the intricacies of data preprocessing and model tuning, the platform promises to democratize advanced AI techniques across cosmology, collider physics, and neutrino experiments. Early testing at Fermilab will leverage the lab’s extensive computing clusters and expertise from multiple research frontiers, ensuring the tool meets the diverse needs of the community.

Beyond particle physics, the initiative illustrates how targeted federal investments can accelerate the creation of reusable AI infrastructure for science. A successful universal framework could become a template for other domains grappling with simulation‑real‑world gaps, such as climate modeling or biomedical imaging. As the DOE Early Career program continues to nurture innovative talent, projects like this one may set new standards for collaborative, cross‑disciplinary AI development, ultimately driving faster, more reliable breakthroughs across the research ecosystem.