I will go into more detail about my work in two topics: "guided visual exploration" and "uncertainty visualization".

Guided visual exploration

We developed Evolutionary Visual Exploration (EVE) as a framework for guided visual search that combines visual analytics with stochastic optimisation to aid the exploration of multidimensional datasets characterised by a large number of possible views or projections. In the first part of this talk, I will present the results of joint interdisciplinary work in the design, implementation and evaluation of an EVE tool called EvoGraphDice. The need for such a tool rose from our close collaboration with biologists and researchers in agronomy. Our method complements PCA, clustering and regression types of analysis, offering additional features such as interactivity and adaptability.

Uncertainty visualization

Information visualization can show not only what we know about the data but also the degree of our confidence in that data. Communicating such information is important for more accurate interpretation of the data and better decision making. In the second part of the talk I will report on my previous and ongoing research in uncertainty visualization where we focus on intuitive ways to communicate qualitative uncertainty to lay audiences. Namely, we investigated the use of sketchiness as both a visual variable and an overall rendering style to convey visual impreciseness that may be associated with data quality. Our results show that sketchiness is a viable alternative to other visual variables to convey qualitative uncertainty and that there is a link between sketchy rendering and increased user engagement.