2015.11.17 The AViDA lab joined the Data Science and Security Cluser (DSSC)
2015.09.23 Wright State will host the first presidential debate next year
2015.04.16 The department took delivery of a 21.16 TFlop/s high performance computer with 2048 cores, which will tie directly into the capabilities of the Appenzeller Visualization laboratory. It was ranked 310 in the top 500 computer list.
2015.03.16 Dayton is also ranked among the best metro areas for STEM professionals (ranked 16th) according to WalletHub.
2015.02.26 Dayton is among the top 10 cities in America for engineers according to Forbes. See here for more information.
2015.02.25 VTK now supports rendering into external OpenGL contexts with their upcoming release version 6.2. Further details can be found in this blog post. This then no longer requires the multipass rendering hack for integrating VTK with Vrui.
2014.04.12 The Appenzeller Visualization Laboratory keeps getting upgraded; this time touch capabilities were added to make some of the displays even more interactive.
2011.04.04 The importance of visualization as a means of interpreting large amounts of data is nicely illustrated by the New York Times.
2009.17.12 The dragonfly reconstruction work of our group (with Chris Koehler as the main driving force) was mentioned in the current issue of TecPlot's Contours Newsletter.
2009.06.10 The department of Computer Science and Engineering now offers a Visualization Option in the B.S. degree of Computer Science allowing undergraduate students to specialize in this important area.
Scientific visualization as a research area has impacted an enormous number of engineering disciplines. In particular, it helps users get better insight into their data. In many areas, it is almost impossible to reasonably analyze data without an appropriate visualization due to the overwhelming amount of information present in the data. Data sets resulting from simulations or experiments in various application fields, for example computational fluid dynamics (CFD) or medical imaging, have to be analyzed and represented in a way that the user is enabled to easily investigate and explore the data. The Advanced Visual Data Analysis group is devoted to solve some of the visualization and analysis challenges arising from these applications. This includes, for example, large-scale visualization and analysis using feature detection and image processing techniques. Examples of current work can be found in the projects section.