Jun 28, 2017

In celebration of my #20YearsACoder, I am conducting a retrospective on the journey.

In 1997, I started my academic career at the University of Memphis, TN as an undergrad. Before that, I had never used a computer; I didn’t even know where the on button was. That year, something magical happened, I discovered the internet. I was in complete awe of technology and wanted that to be my career. I remember how invisible I felt when I made my first online purchase in 1998, a pair of J. Crew boots.

I changed my major from Accounting to Computer Science. I took programming classes, data structures, operating systems, assembly language and so on. Things didn’t come alive for me until I took a class called “Expert Systems,” an artificial intelligence (AI) class. I took that class in the spring of 2000. I can honestly say that class molded the entirety of my career. I wrote Prolog code and loved it. Declarative logic was bae. I still have my textbook and notes from that class. I fell so hard for AI that I went to work at a startup which built online learning platform powered by a naive auto-tutor. We were ahead of our time. The company went belly up during the dot-com boom. I was a junior dev and did QA. I could see the writing on the wall and decided to head back to grad school to spend more time digging deeper into AI I spent the early 2000s coding neural nets, studying control of autonomous agents, deductive databases and so on. By far Stan Franklin’s Control of Autonomous Agents was my favorite class.

In the end, I settled on research that focused on using the Semantic Web to build sensor Ontologies. I did this work under the tutelage of Dr. David Russomanno, the now dean of engineering at IUPUI (Indiana University – Purdue University Indianapolis). Our paper “Building a Sensor Ontology: A Practical Approach Leveraging ISO and OGC Models” made a significant dent. Because of all my work in reasoning, I started daydreaming about building a system that could decode rap lyrics. Jay-Z and Beyonce’s 03 Bonnie and Clyde became a particular fixation. I wanted to build an engine that could decode it. A year or two later my son was born, and I took four years parental leave.

In those four years, it became apparent to me that I wanted to study the other side of computational intelligence. I saw computational intelligence from a higher order perspective, something that is independent of the embodied substrate. I had spent a decade studying computational intelligence in an artificial substrate. Now I wanted to study it in humans; mainly I wanted to know how people interact with computation. This quest led me to UC Berkeley for a Ph.D. in Computer Science Ed. I focused on beginners like I was in 1997. Through research, I learned that the challenge was more social than cognitive. In the near future, I look forward to using this knowledge and this journey to help others learn computation faster.