I am a 3rd year MS/PhD student (1st year post-Masters degree) in the Energy and Resources Group and also am pursuing an MS degree in the Systems track of Civil and Environmental Engineering. Broadly, I'm interested in the use of large environmental datasets (e.g. remote sensing data, climate model output) to (a) improve predictions of the impact of climate change on human systems (e.g. agriculture, public health) and (b) improve how we manage natural resources under climate change. To give an example of the latter, I'm interested in improving reservoir management efficiency by obtaining more accurate estimates of snowpack mass and spatial distribution. The methods currently in use to estimate snow rely on a stationary climate and don't take advantage of the large array of relevant available data thanks to innovations in satellite and airborne observations. Updating these methods would allow water managers to make more efficient use of the snowmelt that they observe, even when warmer winters are expected to lead to diminished snowpacks in California and in many coastal mountain ranges around the world. I do not have a computer science background but have worked with parallel systems in the past. With this class, I'm hoping to move from a user that understands the basics of parallel programming and can make use of an existing parallel structure for embarrassingly parallel programs to a user that can start from a problem statement and think through how one might best utilize parallel resources to tackle that problem and what those resources should look like. In particular, I'm interested in applications of parallel computing to the analysis of large spatial (raster and vector) datasets.