Improving extreme weather generation and spatial variability of weather generators based on resampling using the Direct Sampling algorithm
- Jorge Luis Guevara Diaz
- Maria Julia De Castro Villafranca Garcia
- et al.
- 2022
- AGU Fall 2022
Daniela Szwarcman received a B.Sc. in Electronic Engineering from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil, in 2013. The following year, she joined the Semiconductor Laboratory on PUC-Rio and enrolled in the Master's degree program in Electrical Engineering focused on Nanotechnology. In 2016, she applied for a Ph.D. in Electrical Engineering at PUC-Rio, but in the area of Artificial Intelligence. Her research focused on deep neural networks and Neural Architecture Search. In 2017, she joined IBM Research as a Ph.D. student intern. She participated in many AI projects, mainly developing and implementing deep models for problems that involve spatial and temporal data. Daniela finished her Ph.D. in 2020 and is now working as a Research Scientist at IBM Research, primarily developing and applying deep networks in the context of weather and climate problems. Her research interests include deep learning, generative models, and physics-informed machine learning.