This is my advising blog. It's basically a repository for useful tips, rules of thumb, best practices, common pitfalls, and checklists for doing research in computational statistics and machine learning. I created this blog because when graduate students start, they have to do many things that they've never done before—writing a paper, coding a software package, creating research media, etc—and often have the same questions as they work through those processes for the first time. My hope is that this blog will serve as a repository of "How To" guides to make the learning process easier.
Suggestions for new things to add here are much appreciated!