Christian Weilbach

Researcher & Systems Builder

I work on structured learning, persistent memory, and runtimes for intelligent systems.

Current work

I am building Datahike, an immutable, versioned data substrate for intelligent systems.

Datahike explores how learning, search, and coordination can be treated as long-lived, inspectable processes rather than disposable computations.

datahike.io

Selected research

My doctoral work focused on structured amortized variational inference and the role of explicit structure in learning systems. The following papers are representative outcomes of that line of work:

• Graphically Structured Diffusion Models — generative models with explicit structural priors
• Flexible Diffusion Modeling of Long Videos — large-scale generative modeling
• All-in-One Simulation-Based Inference — unifying amortized and online inference
• Daphne: Multi-Pass Compilation of Probabilistic Programs — compiling probabilistic programs into inference graphs
• Decoupling Conflict Resolution with CDVCS — versioned, conflict-aware replication

Full publication list: Google Scholar →

Perspective

I am interested in systems that support deliberate, programmable organization of work, knowledge, and learning, rather than relying solely on emergent coordination.

Contact

Email: christian@weilbach.name
GitHub: github.com/whilo
LinkedIn