<div dir="auto"><div><div class="gmail_quote"><div dir="ltr" class="gmail_attr"><br></div><br><br><div dir="ltr"><font face="georgia, serif">Caros </font><div><font face="georgia, serif"><br></font></div><div><font face="georgia, serif">Divulgando a Conferência do Prof. Marc Genton que será realizada no dia 9 de dezembro as 14horas no auditório Antonio Gilioli do IME-USP.</font></div><div><font face="georgia, serif"><br></font></div><div><font face="georgia, serif">Atualmente Marc é <i>Distinguished Professor of Statisitcs at KAUST </i>e líder do grupo de pesquisa <i>Spatiol-Temporal Statistics and Data Science.</i> O grupo tem desenvolvido diversas aplicações em ciências ambientais e climáticas, energias renováveis, geofísica e ciências marinhas. Para mais informações veja : </font></div><div><br></div><div><a href="https://cemse.kaust.edu.sa/profiles/marc-g-genton" target="_blank" rel="noreferrer">Marc G. Genton | Computer, Electrical and Mathematical Sciences and Engineering</a></div><div><br></div><div>Esperamos contar com a presença de todos. Caso não seja possível a participação presencial, há um link para apresentação no Youtube conforme informado abaixo. </div><div><br></div><div>Saudações</div><div><br></div><div>Márcia D´Elia Branco</div><div>Professora Titular</div><div>Departamento de Estatística - IME - USP </div><div><br></div><div>------------------------------------------------------------------------------------------------------------------------</div><div><div><span style="font-size:large">Seminário Temático Séries Temporais</span></div><div><font size="4">dia 9/dez, segunda, às 14 horas no auditório Antonio Gilioli - IME-USP</font></div><div><font size="4"><br></font></div><div><font size="4">Convido a todos que puderem assistir presencialmente, será no auditório Antonio Gilioli no 2o andar do Bloco A do IME-USP</font></div><div><font size="4"><br></font></div><div>Youtube: <a href="https://stream.meet.google.com/stream/c864c234-9116-477f-ad65-20eafce541b9" target="_blank" rel="noreferrer">https://stream.meet.google.com/stream/c864c234-9116-477f-ad65-20eafce541b9</a></div><div><font size="4"><br></font></div><div><span style="font-size:12pt">Large-Scale Spatial Data Science with ExaGeoStat</span></div><div><p class="MsoNormal"><span style="font-size:12pt"> <u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:12pt">Marc G. Genton, Statistics Program, King Abdullah University of Science and Technology (KAUST), Saudi Arabia<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:12pt"> <u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:12pt">Spatial data science relies on some fundamental problems such as: 1) Spatial Gaussian likelihood inference; 2) Spatial kriging; 3) Gaussian random field simulations; 4) Multivariate Gaussian probabilities; and 5) Robust inference for spatial data. These problems develop into very challenging tasks when the number of spatial locations grows large. Moreover, they are the cornerstone of more sophisticated procedures involving non-Gaussian distributions, multivariate random fields, or space-time processes. Parallel computing becomes necessary for avoiding computational and memory restrictions associated with large-scale spatial data science applications. In this talk, I will demonstrate how high-performance computing (HPC) can provide solutions to the aforementioned problems using tile-based linear algebra, tile low-rank approximations, as well as multi- and mixed-precision computational statistics. I will introduce ExaGeoStat, and its R version ExaGeoStatR, a powerful software that can perform exascale (10^18 flops/s) geostatistics by exploiting the power of existing parallel computing hardware systems, such as shared-memory, possibly equipped with GPUs, and distributed-memory systems, i.e., supercomputers. I will then describe how ExaGeoStat can be used to design competitions on spatial statistics for large datasets and to benchmark new methods developed by statisticians and data scientists for large-scale spatial data science.<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:12pt"> </span></p></div></div></div>
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