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Computational and Data-Enabled Science and Engineering

Dissertation (Thesis) Learning Resources

While some of my coursework did of course prepare me to write my dissertation, a lot of additional domain specific knowledge was needed for a variety of related topics. And not being the kind of person who is comfortable with a thin veneer of knowledge, but really wants to know what I'm talking about, I ended up doing a lot of learning from open sources in a number of areas to fill in gaps in my knowledge.

I'm going to split these into two broad categories: data science related information for specific statistical topics and methods, and the science of ice sheets (glaciology, and related areas) including ice sheet modeling. Think of this as prep for my qualifying exams.

In the margin, I've linked my initial project plan that briefly describes the general aim. I'll have to write a formal dissertation proposal in 2022, and I'll add that here when that's done.

Data Science Topics:

• Bayesian Statistics

• Approximation Theory

• Bayesian Regression and Bayesian Time Series

• Spatial Interpolation

• Gaussian Processes

• Hierarchical Gaussian Processes

• Partial Parallel Emulation

• Time Series

• Emulators

• Markov Chain Monte Carlo Methods

• Stochastic Processes

• Irregular Time Series

• Uncertainty Quantification

Earth Science and Glaciology Modeling Topics

• Glaciology

• Geostatistics

• Ice Sheets & Ice Sheet Modeling

• Ice Physics

• Climate Change & Sea-level Rise

Project Proposal

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