The earliest memory I have of ‘programming’ is in the early/mid 90s when my father brought home a computer from work. We could play games on it … so of course I took the spreadsheet program he used (LOTUS 123, did I date myself with that?) and tried to modify it to print out a helpful message for him. It … halfway worked? At least I could undo it so he could get back to work…

After that, I picked up programming for real in QBASIC (I still have a few of those programs lying around), got my own (junky) Linux desktop from my cousin, tried to learn VBasic (without a Windows machine), and eventually made it to high school… In college, I studied computer science and mathematics, mostly programming in Java/.NET, although with a bit of everything in the mix. A few of my oldest programming posts on this blog are from that time.

After that, on to grad school! Originally, I was going to study computational linguistics, but that fell through. Then programming languages (the school’s specialty). And finally I ended up studying censorship and computer security… before taking a hard turn into the private sector to follow my PhD advisor.

Since then, I’ve worked in the computer security space at a couple of different companies. Some don’t exist any more, some you’ve probably heard of. I still program for fun too, and not just in security.

But really, I still have a habit of doing a little bit of everything. Whatever seems interesting at the time!

PyBallWorlds

2007-09-26

Back in the first quarter of my Freshman year at Rose-Hulman, we wrote a small Java program called BallWorlds. The idea was to teach us about objects and inheritance by asking us to make a 2d simulation of balls of various types bouncing around in an enclosed environment. There could be balls that bounced off each other, sticky balls that clumped together, balls that grew when they hit something, and really any combination there of. The sky was the limit. The idea so intrigued me that when I was playing with OpenGL (and specifically PyOpenGL), I decided to rewrite the same thing in Python.

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Markov Random Text

2006-12-14

This is work from my first Winter quarter at Rose-Hulman Institute of Technology. Basically, we were to use Markov chains to generate a semi-random text that statistically matches an input text. The short version is that you calculate for each sequence of words of a given length (the chain length) what the possible next words are from the given text, each with a given probability. Then you use that to generate a new text, randomly choosing each new word from the aforementioned probabilities. It’s really fun to play with and I’ve got a half dozen or so examples to show you.

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