I am a first year PhD at Texas A&M University with an MS Computer Science from Baylor University.
My email address is
myfirstname.mylastname at NEO-TAMU (or jbjohns AT CSDL, which just forwards to my NEO account)
My wife of 5 years was the Study Abroad Coordinator for the Computer Science Department at Baylor University for their trip to Shanghai, China. I got to tag along. This photograph was taken in the garden at the base of the Oriental Pearl Tower, Asia's highest tower and the world's third highest tower.
My academic interests lie generally in the area of machine learning and artificial intelligence. My Master's thesis was specifically about unsupervised learning--applying mixture models of the exponential Dirichlet compound multinomial to cluster basic block vector data for the SimPoint application. Though still interested generally in ML and AI, I'd like to use the resources presented by a larger university and larger Computer Science department to see about applying ML and AI techniques to different areas, and to explore other areas not related to clustering to see if I might enjoy research in them.
As stated, I have my MS Computer Science already, with my thesis on unsupervised learning, and also a BS Computer Science. I have a great deal of programming experience in Java (language of personal choice, with many hours of use in both academia and industry) and MATLAB. I can code in C++ and am fairly fluent, but prefer Java (on a strictly I-use-it-more basis).
I am taking this class because there are a lot of opportunities to increase my ML/AI experience and apply the two fields to new domains (new to me). Plus, it's neat to see how the "magic" of a handwriting recognition system (for example) really works. One of the first applications I coded in my machine learning class was a classifier for handwritten zip-code digits. It's a hard problem, to be sure, and I'd like to see more information on what the state of the art is.
From this class, I hope to gain a new insight into how the difficult problems associated with assigning meaning to user input are solved in this domain. I've seen firsthand how difficult this problem can be elsewhere. In a domain with so much variability and flexibility as sketch recognition, how does one cope with these difficulties?
In 5 years, I will hopefully have finished my PhD and be earning a lot of money as a professor at some prestigious university. If I can't do that, I'll settle for making even more money in industry. :) Either way, I want to research and push the field and myself farther. I always want to learn, and I feel research is one of the best ways to do that (as opposed to slinging code from 8 to 5).
In 10 years, I hope to be tenured, well published, and really in my stride as a contributing professional in my research area (whatever that turns out to be). If I end up in industry rather than academia, I hope the only difference is that I don't have to worry about tenure.
When I'm not doing academic related things, I like spending time with my wife. We enjoy hiking, camping, playing racquetball, and watching movies together. Since I'm such a computer nerd, I also enjoy playing video games. My wife...not so much. :)
Fun Story: While in Shanghai, we had the unique experience of visiting a Chinese Wal-Mart. It was just that, an experience. For example, in their fresh seafood department, you could net-your-own catfish, eels, turtles, and frogs. In their fresh meat department, if you wanted a rack of spare ribs, you just picked one up out of the bin and chunked it in your buggy, plastic baggies or gloves were purely optional. Same thing with the bins of whole, defeathered chickens and chicken feet. It made me thankful for the USA, plastic wrap, and Styrofoam.
No comments:
Post a Comment