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.

Date
Oct 26, 2020
Matthew Turk
Matthew Turk
Assistant Professor of Information Sciences

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