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2317 SPEEDWAY , Austin, Texas 78712

https://stat.utexas.edu/training/seminar-series
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Eric Jonas (UC Berkeley Center for Computational Imaging & RISELab)

Title: Exploiting computational scale for richer model-based inference

Abstract: Understanding the deluge of scientific data acquired from next-generation technologies, from astronomy to neuroscience, requires advances in translating our existing knowledge to useful models. Here I show how our recent advances in scalable computing, from “serverless” cloud offerings to deep function approximation, can let us capture and exploit this prior knowledge. Examples include models derived from human intuition (for neural connectomics), carefully-engineered physical systems (for imaging through scattering media), and even direct simulation (for superresolution microscopy). By expanding the space of models we can work with, we can avoid common data science pitfalls while making computing at scale accessible to the entire scientific community.

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