Applied Machine Learning

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

Daniela Cisneros, Ph.D. Student, Statistics

Sep 11, 16:00 - 17:00

B3 L5 R5220

extreme statistics Applied Machine Learning geospatial statistics

The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.

Malik Shahid Sultan

Ph.D. Student, Statistics

Applied Machine Learning Deep learning Deep Reinforcement Learning Functional Time Series

Malik Shahid Sultan is a Ph.D. student in Bio-Engineering ( Bioinformatics & Machine Learning) at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group. Education and Early Career Malik Shahid Sultan hold BSc. in Industrial Engineering with a Gold Medal from the University of Engineering and Technology Taxila (Pakistan). Malik holds M.Sc. from King Abdullah University of Science and Technology (KAUST) (Saudi Arabia) in Bio-Engineering (Bioinformatics & Machine Learning). During the Fall of 2021