Christian Weilbach is a researcher and systems builder working on structured learning, persistent memory, and runtimes for intelligent systems. He completed a PhD at UBC on structured amortized variational inference, after earlier work on Bayesian GANs, dictionary learning, and online learning for spiking neurons running Boltzmann machines. He’s currently building the Datahike ecosystem, an immutable, versioned data substrate for intelligent systems, including tools for vector search, branching SQL, and git-like search infrastructure. He has given talks at DeepMind, OpenAI, and MILA, and is interested in systems for deliberate, programmable organization of work, knowledge, and learning.