Dr Kiel Hards
Epi | Data Science Lead
Kiel is a senior data scientist who builds cloud-native analytics pipelines and applies generative AI to real-time intelligence challenges. He has delivered national threat-monitoring platforms, automated global information feeds, and prototyped LLM-powered recommenders that surface critical insights faster. Backed by 45 + peer-reviewed publications and stewardship of multi-million-dollar research programmes, he combines scientific rigour with production engineering across Python, R and SQL.
Workshop
Taking the plunge into Positron — Posit's R & Python friendly Data Science IDE
Since its beta release in 2024, Positron, the latest Data Science IDE from Posit and the spiritual successor to RStudio, has taken the open-source data science community by storm. The IDE combines the many well-loved features of RStudio with the modern interface and extensibility of VS Code, providing first-class support for both R and Python out of the box. The pace of development has been rapid: 2026 alone has brought a fully redesigned Notebook Editor, inline Quarto output, an integrated Packages pane, first-class R debugging with breakpoints, and a new unified AI assistant purpose-built for data science. Join us for an interactive training on what Positron offers including a showcase of its latest features, and an honest discussion of how to navigate the switch from RStudio and avoid common frustrations. If you aren’t set up already we can help you install Positron live during the session and will work through hands-on demonstrations of how to customise and elevate your data science workflows.
Workshop
Taking your automated reporting game to the next level with Quarto
Quarto is one of Posit’s latest multi-language reporting offerings. It is an open-source scientific and technical publishing system that allows users to publish reproducible, production quality articles, presentations and dashboards. Having been rigorously developed by the team at Posit as the ‘successor’ to R Markdown, the framework offers full backwards compatibility with the original features of R Markdown, along with new features such as language support for Python and Julia, formats such as presentations, PDF, Microsoft Word, even website and ebook formats! You can even use Quarto documents as an environment for your data science experimentation, thanks to the multi-language integration which allows you to run code directly from the Quarto document itself. The framework uses a rich Markdown syntax to provide access to powerful technical and scientific notations such as equations, citations, figures, cross-references, advanced layouts and more. Quarto documents can easily be automated and published to online services such as Posit Connect or Confluence whenever your data changes. Join us to learn how to harness the power of Quarto to dynamically create and share high quality reports and presentations, fit these reports into your existing R or Python workflow and have all your favourite analytics built straight in!
Abstract
Bringing next generation mapping approaches to R with the Toro package
Interactive spatial visualizations are a cornerstone for understanding and communicating complexity in almost all technical and scientific disciplines and are commonly embedded in reports or interactive dashboards As the amount of data grows as well the pressure to create operational intelligence grows existing tools used to generate and display maps hit their performance ceiling. They often lack modern features essential for complex datasets such as those arising from large scale or multi-year projects. MapLibre GL JS is an open-source JavaScript/TypeScript library for rendering interactive maps in the browser using WebGL (and soon WebGPU) that is built from the ground up for responsiveness and scale. To enable report and dashboard developers in R to leverage this powerful library, the team at Epi developed Toro: a R package providing bindings to MapLibre GL JS, allowing users to create interactive maps that can easily be integrated into both Quarto and the R Shiny dashboard framework. In this talk, we will discuss the technical challenges we had to overcome during the development of the package and provide examples of how Toro enabled spatial visualisation and exploration of data that otherwise would have been limited, too slow or too hard to scale using traditional tools.