The research field of end-user programming has largely been concerned with helping non-
experts learn to code sufficiently well in order to achieve their tasks. Generative AI stands to …

Probabilistic programming is a powerful means for formally specifying machine learning
models. The inference engine of a probabilistic programming environment can be used for …

Spreadsheet errors are ubiquitous and costly, an unfortunate combination that is well-
reported. A large class of these errors can be attributed to the inability to clearly see the …

Stan is a probabilistic programming language that has been increasingly used for real-world
scalable projects. However, to make practical inference possible, the language sacrifices …

Although researchers have proposed different definitions for Computational Thinking (CT),
one commonality across these definitions is the emphasis on having students formulate and …

Probabilistic programming allows developers to focus on the modeling aspect in the
Bayesian workflow by abstracting away the posterior inference machinery. In practice …

The construction industry is a new avenue for big data and data science with sensors and
cyber-physical systems deployed in the field. Construction students need to develop …

Research Work in Progress Paper: Loops are a fundamental concept of programming. For
novice programmers loops generally and nested loops specifically pose big difficulties. This …

This discussion paper presents a conversation between researchers having active interests
in the usability of probabilistic programming languages (PPLs), but coming from a wide …

We present Multiverse Explorer, a domain-specific probabilistic programming language
presented as a visual language integrated with a domain world model. The interactive …