Lab Visit | Visulization and Data Analysis

15.12.2015 15:00 - 17:00

Dear colleagues,

Our regular lab visits give our local network of scientists and students the chance to get exclusive inside perspectives into the broad spectrum of ongoing cognition research at the Cognitive Science Research Platform. This time we will visit the research group "Visualization and Data Analysis" led by Univ.-Prof Torsten Möller at the Faculty of Informatics.

The visit will include demos on all areas of research (see below), an introduction to the empirical methods used by the group and, if time allows, a little hands on case study.

For organizational concerns it is important for the organizers to know the exact number of participants. Therefore, if you want to attend the lab visit please save the date and register at until December 15, 2015.

See you on Tuesday!

Best regards,
CogSci Team


Research areas of the "Visualization and Data Analysis" group

Design Studies
How can we turn domain problems into actionable viz tools? Many of our research projects are problem-driven: we collaborate with domain experts with driving data analysis problems and design novel visual analysis solutions with and for them. Following a human-centered design process, our goal is to provide our collaborators with more effective and efficient ways of analyzing their data. We then engage in abstracting our problem-driven findings into more general insights useful for the visualization and data science communities. In our design studies, we, for instance, collaborated with fisheries managers, automotive engineers, mathematicians, biologists and bioinformaticians, special effects designers, and others.

Parameter space analysis
How can we leverage visualization to better understand computational simulations and models? Building and understanding models with the aid of the computer is the bedrock of modern science, also known as computational science. While one of the challenges in computational science has been the sheer amount of data that becomes available, the more difficult task is often to understand the complexity of interactions between inputs and outputs of such models. Our group is working on better tools to understand these interactions better through visual tools.

Sampling and reconstruction
How do we properly sample continuous data? Studying continuous phenomena using computer requires their discretization. The sampling strategy used impacts the quality and efficiency of the analysis. We study the advantages of different regular sampling patterns in 3D and higher dimensions and compare them to non-uniform sampling and reconstruction strategies. Applications range from rendering applications to experimental design and statistics. 


Visualization and Data Analysis

Faculty of Computer Science
University of Vienna
Währinger Straße 29/S6
(Innenhof Sensengasse 6)
A-1090 Vienna