This document has been mostly supplanted by this one.

With the help of Tenzin, Robin, Jonathan, Dillan, Chris, Andrew, Gary (who has been a life long mentor of mine, and one of the most brilliant people I know), Stephen, Munkelwitz, Abe Barth-Werb, Jacob Beauregard, Evan Yandell, Zack Ney, Evan Flynn, Ethan Joachim Eldridge, Phelan Vendeville, Mike Torch, Leif K-Brooks, and a handful of other amazing personalities, we revived the Computer Science Student Association (CSSA) at the University of Vermont from a once a month meetup to a hackerspace which became home for 10% of the CS department. From them, I learned about recursive trampolining, decision trees, neural networks, x11 and intel graphics acceleration, beowulf clustering, password cracking, language theory, homoiconicity, data mining, wireless sensors, functional programming and tail call optimization, multiprocessing done right, and a whole bunch of other skills. I was also very inspired by Leif who was running Omegle -- vicariously it was a privilege to see what went into supporting an effort of that size and the concept is brilliant. The CSSA was a fantastic source of inspiration, knowledge, and a network I am very grateful to have worked with. Theme being, have a mentor. Or many mentors who know what you want to learn. During my time at UVM, I spent no less than 500 hours talking to my mentor Gary Johnson about everything he was learning during his grad classes about machine learning (often at the great expense of sleeping)

The second lesson was to secure an independent study, if possible. Working with Josh Bongard (who was our CSSA advisor) was my first dive during undergrad into working with basic neural networks. And taking relevant courses from teachers who know the industry. I was lucky to take a Data Mining class and a course on Artificial Intelligence with Dr. Xindong Wu, who was formerly the Editor-in-Chief for IEEE TKDE, AI&KP and Chair of ICDM. In these courses we used Peter Norvig and Stuart Russel's Artificial Intelligence a Modern Approach (which is one of my favorite books). We want through a detailed comparison of data mining algorithms ( and we implemented each one in different languages with interesting complimentary or illustrative features ranging from common lisp to prolog. The final for the KDM class was a mini dissertation on one of these algorithms, as well as suggested improvements (I did PageRank).

I applied to grad schools specifically working to continue work on my search engine. I was rather disappointed to find (what shouldn't have been surprising) that there were a lot of professors who wanted to work with me on their grants, but none who was willing to work on my project. And to add insult to injury, the professor I had hope to work with was on sabbatical. So, I continued to take classes on Artificial Intelligence at University of Delaware and ran my search engine servers from within the university (which I probably shouldn't be saying) and would bring questions to the faculty every chance I got.

Less than two years into my phd program I got dragged out by an opportunity to start a company, it had some funding offers, and so I decided to take the shot and leave my program. I was kept pretty busy over the next ~4 years w/ engineering efforts for the startups I was with, but I tried to continue to queue up and read academic papers and follow the trends of the industry. If you read my essay on this thread about reaching inaccessible people, I would frequently connect with researchers to ask them about their work and occasionally they were willing to walk me through their paper if I was stumped on something.

This all came in handy when I started Hackerlist, Inc and we pivoted to be an AI consultancy and I had to read papers, interview candidates, and pair the right person to the right contract.

Here's a list of ~75 papers (most AIML or NLP related) which I found worth noting (or was lucky to remember):