I am a Joint Postdoctoral Fellow at Harvard's Programming Languages and Formal Methods groups and the Basis Research Institute.
I am broadly interested in the modeling of how we perceive the world, and the modeling of reasoning processes. To support this goal, I work in the emerging area between programming languages, machine learning, and probabilistic programming languages.
At Basis, I work on modeling uncertainty in symbolic world models in MARA, designing a robot design language that captures both morphology and control in R-ADA, and modeling LLM generation as an effect, as a framework for building agent harnesses, in effectful. At Harvard, I work on proof automation in Lean and causal systems for drug repurposing.
I completed my PhD doing machine learning and program synthesis-based debugging at the University of Melbourne and previously worked at Cinnamon AI Lab on visually rich document information extraction.
Research interests
- World models: learning, evaluation, and uncertainty
- Languages and abstractions for robot design and LLM agents
- Program synthesis and probabilistic programming
- Neuro-symbolic systems modeled with LLMs, PPLs, and NNs
- Reliable, explainable ML for software, including graph-based learning for code and documents
Recent writing: I linearized my TAIC'26 talk into a blogpost, WorldTest: how do we know whether an AI has learned how a world works? All 43 AutumnBench environments run live in the browser there. The rest of my writing is at datvo06.github.io.
| VRDSynth | Autumn.cpp | NeuroSymbolicDG |
|---|---|---|
