I am supervising the following undergraduate and postgraduate students:
I have supervised the following undergraduate and postgraduate students:
This project implemented a new kind of selection highlighting for matrices and table views in the Fluid programming language, taking advantage of a new graph-based dependency analysis which is fast enough for interactive queries. As the user moves their mouse over the output, the current mouse position is treated as an output selection and used to compute the corresponding input selection, which is then used to transiently render borders around selected regions in real time. This significantly improves the usability and interactivity of Fluid visualisations.
Colin Crawford (2024). NPM Publishing Setup for the Fluid Language.This project involved setting up an automated build script to publish Fluid as a library on the Node package manager and integrating that with the Fluid continuous integration/continuous deployment workflow on GitHub. This will make it easier for potential adopters to install the language and use it to author visualisations.
Hana Iza Kim (2024). Puppeteer-Based Web Testing for Fluid Visualisations.This involved configuring the JavaScript web automation framework, Puppeteer, to work with our PureScript implementation, providing a library of common test functions, and then using that library to implement some basic headless user interface tests for some of our Fluid examples. This made a significant difference to the robustness of the testing pipeline and is something we now use every day as part of our local development workflow.
Thomas Frith (2024). Scoping Provenance Queries to Non-Inert Data.This project involved a significant redesign of the Fluid selection mechanism to incorporate a new notion of data which is inert. An output is inert if it doesn’t need any input; an input is inert if it isn’t needed for any output. Restricting the dependency analysis to non-inert data means the user is only presented with data which is actually used somewhere (i.e. by at least one part of the output); we conjecture that this reduces the cognitive burden for users by reducing the amount of data they need to consider at any time.
Piotr Kozicki (2023). Probabilistic Programming and Automatic Differentiation. Harleen Gulati (2023). Data-Driven Debugging for Transparent Research Outputs.