Hi there, and thanks for stopping by! In this post, I briefly introduce my current ideas for this blog and say a bit about myself.

As of September, I’ve been a first-year PhD student at Oxford’s Statistics department. I received my bachelor’s in Physics from Harvard in 2015, and after working for two years am excited to be back in an academic setting. Part of this transition means more freedom and a lot more self-structured learning time. Blogging seemed like a good outlet to keep track of interesting ideas I work through, and make them more polished than my natural scratch work / code.

Recently, the fields of statistics / machine learning / AI have received a great amount of public attention. Many sectors across science, technology, and business are looking to get in on the benefits, which creates a demand for educational and computational resources. It’s exciting that machine learning and data science as fields have adopted a culture of openness, which includes offering widely accessible learning materials, free software packages, and public code. As a believer in these values, blogging seemed like a great way to practice my own public communication / sharing of ideas.

Right now, I envision posting on the following areas, in order of high to low proportion:

1. Explanations of concepts in advanced undergraduate statistics, using visualizations and public code to help make ideas intuitive and easy to follow.
2. Snapshots of applied data analysis and visualization of some public datasets I can find.
3. General musings on the PhD experience and the cultural values of the machine learning community.

I’ve chosen to blog primarily with the R Markdown framework, which I think is designed to easily encourage principles of reproducibility and interactivity. Stay tuned for a post on choosing a blogging framework!