[ABE-L] Conferência Prof. Marc Genton - IME-USP

[Márcia Branco]

Caros

Divulgando a Conferência do Prof. Marc Genton que será realizada no dia 9
de dezembro as 14horas no auditório Antonio Gilioli do IME-USP.

Atualmente Marc é  *Distinguished Professor of Statisitcs at KAUST *e líder
do grupo de pesquisa *Spatiol-Temporal Statistics and Data Science.* O
grupo tem desenvolvido diversas aplicações em ciências ambientais e
climáticas, energias renováveis, geofísica e ciências marinhas. Para mais
informações veja :

Marc G. Genton | Computer, Electrical and Mathematical Sciences and
Engineering <https://cemse.kaust.edu.sa/profiles/marc-g-genton>

Esperamos contar com a presença de todos. Caso não seja possível a
participação presencial, há um link para apresentação no Youtube conforme
informado abaixo.

Saudações

Márcia D´Elia Branco
Professora Titular
Departamento de Estatística - IME - USP

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Seminário Temático Séries Temporais
dia 9/dez, segunda, às 14 horas no auditório Antonio Gilioli - IME-USP

Convido a todos que puderem assistir presencialmente, será no auditório
Antonio Gilioli no 2o andar do Bloco A do IME-USP

Youtube:
https://stream.meet.google.com/stream/c864c234-9116-477f-ad65-20eafce541b9

Large-Scale Spatial Data Science with ExaGeoStat



Marc G. Genton, Statistics Program, King Abdullah University of Science and
Technology (KAUST), Saudi Arabia



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.
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