[ABE-L] Seminário DEST/UFMG em 10/11/2023

[Marcos Prates]

Caros,

Na próxima sexta-feira (10 de Novembro, às 13:30h) o ciclo de Seminários do
Departamento de Estatística da UFMG terá a apresentação do prof. Abhirup
Datta da John Hopkins - USA.

Dr. Datta is an Associate Professor in the Department of Biostatistics at
Johns Hopkins University. He completed his PhD in Biostatistics from
University of Minnesota. Dr. Datta’s research focuses on developing spatial
models for geographically indexed data. His work on Nearest Neighbor
Gaussian Processes (NNGP) has become one of the most widely used methods
for scalable analysis of massive geospatial data. His recent work focuses
on developing theory and methodology for combining machine learning
algorithms with traditional spatial modeling, and application of the
methodology to air pollution and infectious disease modeling. He also works
on developing Bayesian hierarchical models for multi-source data with
applications in global health. His research as Principal Investigator is
funded by grants from the National Science Foundation (NSF), National
Institute of Environmental Health Sciences (NIEHS) and the Bill and Melinda
Gates Foundation. He has received the Early Career Investigator award from
the American Statistical Association Section of Environmental Health, the
Young Statistical Scientist Award (YSSA) by the International Indian
Statistical Association (IISA), and the Abdel El-Shaarawi Early
Investigator's Award from the The International Environmetrics Society
(TIES).

Title: Combining machine learning with Gaussian processes for geospatial
data

Abstract: Spatial generalized linear mixed-models, consisting of a linear
covariate effect and a Gaussian Process (GP) distributed spatial random
effect, are widely used for analyses of geospatial data. We consider the
setting where the covariate effect is non-linear and propose modeling it
using a flexible machine learning algorithm like random forests or deep
neural networks. We propose well-principled extensions of these methods,
for estimating non-linear covariate effects in spatial mixed models where
the spatial correlation is still modeled using GP. The basic principle is
guided by how ordinary least squares extends to generalized least squares
for linear models to account for dependence. We demonstrate how the same
extension can be done for these machine learning approaches like random
forests and neural networks. We provide extensive theoretical and empirical
support for the methods and show how they fare better than naïve or
brute-force approaches to use machine learning algorithms for spatially
correlated data. We demonstrate the RandomForestsGLS R-package that
implements this extension for random forests.

O seminário será transmitido ao vivo pelo canal do Youtube "Seminários DEST
- UFMG <https://www.youtube.com/@seminariosdest-ufmg>".

https://www.youtube.com/@seminariosdest-ufmg

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