Lured by my tech-brain and the effortless beauty of Ruby code, I left the front-end
sphere to dive into the server-side world of Rails. The result was an intense ramp-up
that had me churning out features within weeks and fighting live fires within
Among my many challenges were working with legacy codebases in a variety of Ruby and
Rails versions; turning over features in a fast-paced, deadline-driven environment; and
learning to operate as part of a tightly integrated team.
Best perk? Hanging out with a team of experienced developers who generously offered
their mentorship as well as their fun personalities.
My primary project was a transition library that allowed one image to be layered onto
another in creative ways. Always the engineer, I utilized variable frame rate animation
and mathematical interpolation to ensure smooth effects. Always the artist, I used my
new library to pioneer a slideshow ad template that increased user engagement.
My greatest challenges were interfacing with a large HaXe code base and cross-browser testing my ads on
all major platforms, including Safari, Chrome, Firefox, and IE 6, 7, and 8.
The Skew-Normal Approximation of the Binomial Distribution · Fall 2010 ~ Spring 2011
When the binomial is asymmetric, the skew-normal
distribution provides a better approximation than the normal distribution, owing to its
third parameter that captures skew.
In my paper, I introduce the skew-normal distribution and present formal proofs for
properties. I then derive a skew-normal approximation of the binomial using the method
of moments. Finally, I demonstrate its improved accuracy over the normal
Data sets were generated using my open source Python and R library, which is
available in this project's github repository.