About Daniela Cisneros Daniela Cisneros Ph.D. Student, Statistics Statistics of extremes spatio-temporal statistics Daniela Cisneros was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Daniela successfully defended her PhD thesis entitled " Extreme-Value Models and Graphical Methods for Spatial Wildfire Risk Assessment" on September 11th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Jorge Mateu (external examiner from Universitat Jaume I, Castillón, Spain), Hernando Ombao, and Matteo Parsani. For her next career steps, Daniela will return to Mexico where Articles Related News January 2024 New paper accepted in Spatial Statistics 1 min read · Mon, Jan 8 2024 Spotlight News Cisneros, D., Richards, J., Dahal, A., Lombardo, L., and Huser, R. (2024+), Deep graphical regression models for jointly moderate and extreme Australian wildfires, Spatial Statistics, to appear [ PDF preprint] December 2023 New paper accepted in Journal of Agricultural, Biological, and Environmental Statistics (JABES) 1 min read · Tue, Dec 19 2023 Spotlight News Cisneros, D., Hazra, A., and Huser, R. (2023+), Spatial wildfire risk modeling using a tree-based multivariate generalized Pareto mixture model, Journal of Agricultural, Biological, and Environmental Statistics, to appear [ PDF preprint] September 2023 Top honors for KAUST extSTAT group at EVA 2023 2 min read · Wed, Sep 13 2023 Awards News KAUST extSTAT Research Group wins prestigious awards at EVA 2023 Conference in Milan. Congratulations to Daniela for successfully defending her PhD thesis 1 min read · Mon, Sep 11 2023 Spotlight News Left: Daniela and three (out of four) committee members after her PhD defense, with Prof. Jorge Mateu (left), Daniela (center left), Prof. Raphaël Huser (center right), and Prof. Hernando Ombao (right). Prof. Matteo Parsani (not on the picture) was the last committee member. Right: picture taken after the defense, with Daniela (center) and Prof. Raphael Huser (in right corner holding his three kids). July 2023 Congratulations to Paolo and Yalla team for their success at the EVA2023 conference 1 min read · Thu, Jul 6 2023 News Spotlight We are thrilled to extend our warmest congratulations to Paolo Victor Redondo and Team Yalla for their outstanding achievements at the EVA 3023 conference! The event, which aims to bring together researchers in Extreme Value Theory (EVT), methods, and its applications, witnessed Paolo's remarkable win for the Best Poster Presentation and Team Yalla's triumphant victory in the EVA Data Competition. Paolo's Best Poster Presentation award demonstrates his commitment, hard work, and enthusiasm for EVT and biostatistics. His remarkable presenting abilities and groundbreaking work on brain January 2023 Two new papers in Extremes 1 min read · Thu, Jan 19 2023 Spotlight News Two new papers accepted to the Extremes Special Issue on the EVA Data Competition: Zhang, Z., Krainski, E., Zhong, P., Rue, H., and Huser, R. (2023+), Joint modeling and prediction of massive Spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach, Extremes, to appear [ PDF preprint]. Cisneros, D., Gong, Y., Yadav, R., Hazra, A., and Huser, R. (2022+), A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, Extremes, to appear [ PDF preprint].
New paper accepted in Spatial Statistics 1 min read · Mon, Jan 8 2024 Spotlight News Cisneros, D., Richards, J., Dahal, A., Lombardo, L., and Huser, R. (2024+), Deep graphical regression models for jointly moderate and extreme Australian wildfires, Spatial Statistics, to appear [ PDF preprint]
New paper accepted in Journal of Agricultural, Biological, and Environmental Statistics (JABES) 1 min read · Tue, Dec 19 2023 Spotlight News Cisneros, D., Hazra, A., and Huser, R. (2023+), Spatial wildfire risk modeling using a tree-based multivariate generalized Pareto mixture model, Journal of Agricultural, Biological, and Environmental Statistics, to appear [ PDF preprint]
Top honors for KAUST extSTAT group at EVA 2023 2 min read · Wed, Sep 13 2023 Awards News KAUST extSTAT Research Group wins prestigious awards at EVA 2023 Conference in Milan.
Congratulations to Daniela for successfully defending her PhD thesis 1 min read · Mon, Sep 11 2023 Spotlight News Left: Daniela and three (out of four) committee members after her PhD defense, with Prof. Jorge Mateu (left), Daniela (center left), Prof. Raphaël Huser (center right), and Prof. Hernando Ombao (right). Prof. Matteo Parsani (not on the picture) was the last committee member. Right: picture taken after the defense, with Daniela (center) and Prof. Raphael Huser (in right corner holding his three kids).
Congratulations to Paolo and Yalla team for their success at the EVA2023 conference 1 min read · Thu, Jul 6 2023 News Spotlight We are thrilled to extend our warmest congratulations to Paolo Victor Redondo and Team Yalla for their outstanding achievements at the EVA 3023 conference! The event, which aims to bring together researchers in Extreme Value Theory (EVT), methods, and its applications, witnessed Paolo's remarkable win for the Best Poster Presentation and Team Yalla's triumphant victory in the EVA Data Competition. Paolo's Best Poster Presentation award demonstrates his commitment, hard work, and enthusiasm for EVT and biostatistics. His remarkable presenting abilities and groundbreaking work on brain
Two new papers in Extremes 1 min read · Thu, Jan 19 2023 Spotlight News Two new papers accepted to the Extremes Special Issue on the EVA Data Competition: Zhang, Z., Krainski, E., Zhong, P., Rue, H., and Huser, R. (2023+), Joint modeling and prediction of massive Spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach, Extremes, to appear [ PDF preprint]. Cisneros, D., Gong, Y., Yadav, R., Hazra, A., and Huser, R. (2022+), A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, Extremes, to appear [ PDF preprint].
