Category: Publications

We have created the Myocardial Uncertainty Viewer (muView) tool
for exploring data stemming from the forward simulation of cardiac ischemia. The simulation
uses a collection of conductivity values to understand how ischemic regions affect the
undamaged anisotropic heart tissue. muView combines a suite of visual analysis methods to
explore the area surrounding the ischemic zone and identify how perturbations of variables
changes the propagation of their effects.

We present an approach to investigate the memory behavior of a parallel kernel executing on thousands of threads simultaneously within the CUDA architecture. Our top-down approach allows for quickly identifying any significant differences between the execution of the many blocks and warps. As interesting warps are identified, we allow further investigation of memory behavior by visualizing the shared memory bank conflicts and global memory coalescence.

In this paper, we introduce a new analysis and visualization framework which enables interactive exploration of robustness of critical points for both stationary and time-varying 2D vector fields. This framework allows the end-users to investigate how the stability of a critical point evolves over time.

A multiscale approach to network event identification using geolocated twitter data Chao Yang, Ian Jensen, and Paul Rosen In Proceedings of

We introduce several useful utilities in development for the creation and analysis of real wildland fire simulations using WRF and SFIRE. These utilities exist as standalone programs and scripts as well as extensions to other well known software.

We propose to aid the interactive visualization of time-varying spatial datasets by simplifying node position data over the entire simulation as opposed to over individual states. Our approach is based on two observations. The first observation is that the trajectory of some nodes can be approximated well without recording the position of the node for every state. The second observation is that there are groups of nodes whose motion from one state to the next can be approximated well with a single transformation. We present dataset simplification techniques that take advantage of this node data redundancy.

RTW is an efficient method for adaptively sampling a shadow map using a set of user/developer-defined heuristics. RTW is both memory and computationally efficient, delivering exceedingly high-quality results in real-time.