Lukas Heinrich

Large HEP datasets are not just for training; they are foundational for unlocking power-law scaling, with smaller datasets offering little insight into high-compute performance.

Turns out, you *can* differentiate through the Gillespie algorithm: machine learning gradient estimators unlock parameter inference in stochastic kinetic models where traditional methods fail.