Guest lecture by Alexander Lex "Literate Visualization: Making Visual Analysis Sessions Reproducible and Reusable"
24.03.2022, starts at 15:00
Alexander Lex, University of Utah
Alexander Lex is an Associate Professor of Computer Science at the Scientific Computing and Imaging Institute and the School of Computing at the University of Utah. He directs the Visualization Design Lab where he develops visualization methods and systems to help solve today’s scientific problems. Before joining the University of Utah, he was a lecturer and post-doctoral visualization researcher at Harvard University. He received his PhD, master’s, and undergraduate degrees from Graz University of Technology. In 2011 he was a visiting researcher at Harvard Medical School.
Alexander Lex is the recipient of an NSF CAREER award and multiple best paper awards or best paper honorable mentions at IEEE VIS, ACM CHI, and other conferences. He also received a best dissertation award from his alma mater. he co-founded Datavisyn (http://datavisyn.io), a startup company developing visual analytics solutions for the pharmaceutical industry, where he is currently spending his sabbatical.
Literate Visualization: Making Visual Analysis Sessions Reproducible and Reusable
Interactive visualization is an important part of the data science process. It enables analysts to directly interact with the data, exploring it with minimal effort. Unlike code, however, an interactive visualization session is ephemeral and can't be easily shared, revisited, or reused. Computational notebooks, such as Jupyter Notebooks, R Markdown, or Observable are widely used in data science. These notebooks are an embodiment of Knuth's “Literate Programming”, where the logic of a program is explained in natural language, figures, and equations. As a consequence, they are both reproducible, and reusable.
In this talk, I will sketch approaches to "Literate Visualization". I will show how we can leverage provenance data of an analysis session to create well-documented and annotated visualization stories that enable reproducibility and sharing. I will also introduce work on inferring analysis goals, which allows us to understand the analysis process at a higher level. Understanding analysis goals enables us to enhance interaction capabilities and even re-used visual analysis processes. I will conclude by demonstrating how this provenance data can be leveraged to bridge between computational and interactive environments.