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Computational and Data-Enabled Science and Engineering

Starting in Fall 2019, I began a doctoral program in Computational and Data-Enabled Science and Engineering (CDSE) at the University at Buffalo (SUNY Buffalo).  It ended in 2024.

The program is very interdisciplinary.  The core is a mixture of Statistics/Data Science, Applied Math and Numerical Methods, and High Performance and Data-Intensive Computing.  I have a lot of stats and applied math from my previous degrees, so for now I'm focusing on the computational side (taking courses in Python, R and Parallel Computing), and GIS.

The thing about computing is that it tends to produce output, so I'm going to use this page, in part, to showcase some of the random things that learning process produces.

For my dissertation, we've finally focused on applying gaussian process regression to modeling the Greenland ice sheets in both time and space. There is a lot I have to figure out to make this happen so, some of the pages below will also be used to link to the kind of information I'm absorbing and learning about for this task: things like Bayesian statistics, time series analysis, gaussian processes, and glaciology. I'm going to put up a data analysis page to post some of my analysis and code.

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