Tilman Plehn

Develop a method to include uncertainties, starting from Bayesian generative networks; expand strategies to model systematic uncertainties using conditional training on nuisance parameters; extend NNPDF methodology for architecture-driven and parameter-driven uncertainties to generative models; study the effect of guided implicit bias on amplification factors between training and generated sample size.

Address the challenge that high energy physicists currently face using ML techniques. We intend to propose new analytic descriptions in the ML model, and further push the logic of infusing more physics into ML in the LHC domain.

In contemporary high-energy physics, the number of potential scenarios for physics beyond the Standard Model far exceeds the capacity of dedicated searches. This disparity stresses the need for model-agnostic approaches that explore a broad spectrum of possibilities within the theoretical signal landscape.