Seemingly harmless choices in visualization design and content selection can distort your data and lead to false conclusions. Designers have traditionally relied on esthetics and heuristics to guide their work. Is there a more objective method for creating visualizations that are perceptually accurate?
Aneesh Karve presents a quantitative framework for identifying and overcoming distortions by applying recent research in algebraic visualization.
Aneesh Karve is the CTO of Quilt Data, a Y Combinator company advancing an open source standard for versioned data. Previously, Aneesh was a product manager, lead designer, and software engineer at companies including Microsoft, NVIDIA, and Matterport and the general manager and founding member of AdJitsu, the first real-time 3D advertising platform for iOS (acquired by Amobee in 2012). He holds degrees in chemistry, mathematics, and computer science. Aneesh’s research background spans proteomics, machine learning, and algebraic number theory.
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