104 citations · 197 across the 5 of their papers we have counts for
5 papers
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Thomas Vandal, Evan Kodra, Jennifer Dy +3
Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (…
Physics-guided probabilistic modeling of extreme precipitation under climate change
Evan Kodra, Singdhansu Chatterjee, Stone Chen +1
Earth System Models (ESMs) are the state of the art for projecting the effects of climate change. However, longstanding uncertainties in their ability to simulate regional and loca…
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
Thomas Vandal, Evan Kodra, Sangram Ganguly +3
The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are r…
Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation
Thomas Vandal, Evan Kodra, Auroop R Ganguly
Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have be…
Network science based quantification of resilience demonstrated on the Indian Railways Network
Udit Bhatia, Devashish Kumar, Evan Kodra +1
The structure, interdependence, and fragility of systems ranging from power grids and transportation to ecology, climate, biology and even human communities and the Internet, have…