A Grammar of Analysis for Volumetric Astrophysical Data

Abstract

Analyzing complex, multi-source, multi-format and multi-modal data from astrophysical simulations, observations and theory requires methods for transforming raw numbers into manipulable quantities, and the application of high-level semantic models on top of those quantities. In this talk I will present methods for defining and applying a grammar of analysis to volumetric astrophysical data, and describe the implications this has for visualization, analysis and inference in astrophysics.

Matthew Turk

Assistant Professor of Information Sciences

I am interested in the intersection of data analysis, visualization and open source in the sciences.