Background

I recieved my Bachelor of Science from the Electrical and Computer Engineering department of Michigan Technological University in 2005. I double-majored in Computer Engineering and Electrical Engineering and graduated summa cum laude. Nearly all of my unergraduate research endeavors were done in cooperation with the Wireless Communication Enterprise.

My first professional intersection with computers ocurred in the form of an internship with IBM in Rochester, MN. During the Summer of 2003, I had the opportunity to design, implement and package an iSeries Navigator System Management Plug-in for SAP. This endeavor was an excellent opportunity to absorb "tech culture" and find my place therein. However, it quickly became apparent Laughing! that user-level applications did not suit my interests. To find a better match, I returned to IBM, Rochester (this time with E&TS) for the Summer of 2004. My work largely focused on timing and synthesis of the floating-point core in the Waternoose microprocessor now used in XBox 360s. Additionally, I contributed some VHDL code. To my surprise, this internship experiment again did not evoke the passion I was seeking. As a result, I moved up the stack and worked part-time during my senior year for one of Michigan's award-winning small businesses, ThermoAnalytics, Inc. Asked to design a Qt Script to C compiler, I was excited to get my hands on a problem of considerable complexity. With this work, I knew I was beginning to discover the aspects of computer science that more closely matched my interests.

Immediately after graduation I drove to Austin, TX, where I began a 12 week internship in IBM's competitive Extreme Blue program. This program is a high-profile opportunity which both opens the doors for corporate funding of projects deemed "risky" and serves as an talent incubator. As part of the Linux Technology Center, my team developed an intelligent kernel-level aberrant behavior tracking tool. In an unsupervised manner, our utilities learned the characteristics of a healthy system, then used this as a reference for diagnosing odd system behavior. This information was then conveyed to developers, thereby expediting the debugging process. It was through this deeply rewarding work that I came to know my ultimate passion: machine learning.

Since that time, I have been happily living the life of a Computer Engineering PhD student at Purdue University. In addition to courses and research, these efforts have allowed me to spend the Summer of 2006 in San Francisco, CA working jointly for Lawrence Livermore National Laboratory and the DOE Joint Genome Institute. Here, I was able to apply techniques and theory from data mining and statistical machine learning to isolate misbehaving production paths in JGI's gene sequencing process. (Among other things, I also had the opportunity to tour the National Ignition Facility--very cool!) I presented my findings to department heads and division directors in a closing talk and at a scholar symposium.