Seminar: Computational Modelling and Data Science at CSIRO Data61

Title: Computational Modelling and Data Science at CSIRO Data61
Speaker: Simon Dunstall – CSIRO
Date & Time: 11am, Friday 9 March
Location: Room 439-201, Engineering Science, 70 Symonds St, Auckland.

Abstract: In this seminar I will present on three interrelated topics associated with applied science and technology development being undertaken in the Decision Sciences program of CSIRO Data61, and from which stem some openly-available tools and useful data science ideas. The first topic is that of mixing large-scale numerical simulations and statistics as part of a multi-year initiative in quantitatively assessing risks associated with forest fires started by power infrastructure, the second topic is on novel data science approaches to fire fighting resource planning, and the third topic is on CSIRO’s Workspace platform which supports high-productivity scientific applications development and is being used inside and outside CSIRO for a wide range of applications including Industry4.0 projects with major manufacturers, natural hazard numerical simulation tools, and virtual coaching tools for Olympic watersports.

Bio: Simon Dunstall is the research program leader for the Decision Sciences program in Data61, and the immediate past-president of the Australian Society of Operations Research. Data61 is the mathematics, statistics and ICT business unit of CSIRO in Australia, and Decision Sciences is one of five programs in Data61 which span a total of 450 R&D staff. Decision Sciences’ mission is to build domain-specific, user-oriented, computationally-intensive decision support systems for established international markets and emerging high-tech industries. In doing this we extend and utilise our scientifically-advanced numerical simulation engines, solvers and software libraries, and high productivity / HPC application-development toolkits. We also participate from in the national conversation about the ethics and impacts of digital technologies, and we apply economics, data science and social sciences to directly demonstrate and facilitate positive community and societal benefits from digital technology.

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