I am a postdoctoral researcher at the Infosys – Cambridge AI Centre in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. My work focuses on uncertainty-aware machine learning for LHC event generation, developing fast and precise surrogate models for scattering amplitudes that provide accurate predictions with well-calibrated uncertainties.
I am also interested in bridging particle physics and AI, creating methods to extract more precise information from both theoretical predictions and LHC data, building on my experience with global analyses using SMEFT and EDM measurements. Working with AI, I mainly, but not only, focus on the trustworthiness of AI predictions by improving them through appropriate uncertainty introduction and handling, and on the interpretability of those results.
Research interests:
Bahl, Henning and Diefenbacher, Sascha and Elmer, Nina and Plehn, Tilman and Spinner, Jonas (2025). Forecasting Generative Amplification. — PDF | Cite | arXiv
Bahl, Henning and Elmer, Nina and Plehn, Tilman and Winterhalder, Ramon (2025). Amplitude Uncertainties Everywhere All at Once. — PDF | Cite | arXiv
Elmer, Nina Marie (2025). Bridging Theory and Data — Uncertainty-Aware Analyses for the LHC and Beyond. — PDF | Cite | arXiv