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Helical

Challenges with Locality and State in ExPL Annotations, Part I

ExPL, the experiment specification language of Helical, relies on user-provided annotations to infer an implicit post-interventional causal structure and query set. Annotations are attached to ExPL expressions and provide a link to the program variables in HyPL. Because ExPL encodes stateful executions, we need to be able to reason about the scope over which stateful operations apply. In this two-part blog post we'll talk about some challenges associated with using these annotations.

Jupyter DSLs

One of the broader goals of the Helical project is to make writing, maintaining, and debugging experiments easier and safer for the end-user through a novel domain-specific language. However, learning a new formal language can itself contribute to the difficulty of encoding an experiment. Therefore, we are intersted in mitigating the effects of language learning/novelty. To this end, a Northeastern coop student (Kevin G. Yang) investigated the suitability of using Jupyter notebooks as an execution environment for experiments last year.

  • in Helical
  • 2 min read

New student, new project!

I want to extend a belated welcome to Zixuan (Jason) Yu, a Northeastern University undergraduate student who is working with me on a research coop through December 2025. Jason's project focuses on identifying elements of the Mastodon code base where we might either want to intervene (in order to answer a research question) or where there might be associated privacy considerations.