Efficient study of a topic is a powerful skill for a wide variety of domains. However – despite having several classes on writing and research in High School and Undergraduate Studies – it wasn’t something that I truly appreciated and learned how to do well until Graduate School. I think this really boils down to lacking the kind of personal application that makes these efforts meaningful. However now that I have come to appreciate it, I want to highlight how I think how I study new topics, and how this applies to many topics outside of academic research.

Python is a relatively simple language compared to others such as C++. Despite its simplicity, Python really shines because of its robust standard library, extensive 3rd party library ecosystem (especially for statistics and data analysis), and intuitiveness. This post tracks my process of learning how to program well in Python.

From time to time, I get questions about how I read and retain as much information as I do. While it is never easy – especially with dry technical documents – there are a few strategies that I have learned along the what that I find helpful. In this post I provide some general suggestions along with some that are more useful in computer systems research.

Foundation Parallel Operations #

Fundamental to developing high-performance software is having an understanding of the basics of parallel architecture.

C++ is a huge language. It has tools form imperative, functional, object-oriented, and generic paradigms. And that leaves out the extremely fine control over things like memory allocation strategies in the standard library not generally available elsewhere. In this post, I present my learning path through C++ and offer some suggestions for learning this multi-faceted language.

From time to time I get questions about how I stay apprised on all of the topics that I know something about. The short answer is a lot of reading and listening.