I had an idea for something I’d like to make/ see made.
Problem it tries to solve: Too many communication / data-provision platforms.
How it solves it: Allow easy curation and inspection of the communication that drives a project forward.Read More
Here’s some random nonsense about my thinking that I want to share/refine while I’m still learning about Observables.Read More
The summer is over and I am very proud to report that I have accomplished most of my goals I set before starting work with MDAnalysis.Read More
Soldiering on!Read More
Watching Katie Ledecky beat the field by 11 seconds in the 800M swim at the Olympics has led me to the conclusion that I am out of shape. I will never physically be where Katie Ledecky is, but I can use her work ethic as an inspiration to try a little harder academically. Using the Morning Paper as an example, I am going to try to Ledeckify my life a little bit and work hard at becoming more (intellectually) fit every day.
Today’s paper is ‘Visualizing Data with t-SNE’, and given my lack of expertise, I have decided to split this paper into posts. This post will cover:Read More
Last week I went to Austin, TX to Scipy2016. I wasn’t sure what to expect. How would people communicate? Would I fit in, what talks would interest me? Fortunately the conference was a huge success. I have came away a far more confident and motivated programmer than when I went in.Read More
My next subject for bloggery is Principal Component Analysis (PCA) (its sibling Multidimensional scaling has been left out for a future post, but it is just as special, don’t worry). If I were to give a talk on PCA, the slides would be roughly ordered as follows:
In the last blog post I wrote the most common critique I received was that I alienated myself from most of my potential audience. In an email I expressed to my Summer of Code mentor Max Linke my problem:Read More
It occurs to me in my previous post I didn’t thoroughly explain the motivation for dimension reduction in general. When we have this data matrix $X$ with $n$ samples and each sample having $m$ features, this number m can be very large. This data contains information that we want to extract, in the case of molecular dynamics simulations these are parameters describing how the dynamics are occurring. But this data can be features that distinguish faces from others in the dataset, handwritten letters and numbers from other numbers, etc. As it is so eloquently put by Porte and Herbst at ArizonaRead More
Hello! This is my first post moving over to a new site built by wintersmith. Originally I was going to use jekyll pages, but there was an issue with the latest Ruby version not being available for Linux, (maybe macs are better…). I spent way too much time figuring out how to install a markdown plugin that allowed for the inclusion of LaTex. I did this all without realizing I could simply include:Read More
This is my first post about actual programming! When I started doing computational research about three years ago, I was lazy and borderline incompetent. Today, the tools I have learned allow me to be equally lazy while being somewhat more competent. These range from simple lifestyle decisions to basic tech skills.Read More
Hello world! I recently was given the amazing opportunity to contribute to MDAnalysis, an open source Molecular Dynamics simulation Analysis project through the Google Summer of Code initiative. I’ve been encouraged to maintain a blog by those giving me this opportunity so I’ll start things off by explaining how I got this great summer job.Read More