Arno Strouwen
Arno Strouwen is a statistician specializing in optimal experimental design for dynamical systems. He holds a PhD from KU Leuven and teaches experimental design there. He works at PumasAI on noncompartmental analysis and in vitro-in vivo correlation, and previously worked at JuliaHub on quantitative systems pharmacology and consulting for SciML applications. His industry experience includes designing experiments for vaccines and pharmaceuticals at Johnson & Johnson.
Session
Real-time adaptive experimental design for ODE models is hard: each step requires costly posterior inference and optimization. We train a neural network policy offline to amortize this cost. The Julia SciML stack makes this practical: Enzyme.jl differentiates through ODEs, Lux.jl defines the policy network, and Reactant.jl compiles everything to a single GPU program. On a bioreactor benchmark, the learned adaptive policy beats Bayesian D-optimal static designs with a 99.5% win rate.