I've been involved in the creation of software for most of my life, and I still build and manage my own computers and network equipment. Google Patents has a nice listing of the patents issued to me for Internet browser technology and computer operating system security advancements.

But my tenure in the corporate world encouraged me to spend more of my time working on public interest causes and to seek a better understanding of the political and economic forces that shape both our lives.  These interests led me to study at Harvard and work with the Program for Networked Governance -- a Harvard social networking research group and the Congressional Management Foundation.

While working towards my Masters degree at the Harvard Kennedy School of Government, I began to develop an interest in advancing productivity in education and academic research. One way this manifests itself in my work is through the development of new methods for use by social scientists. Despite the rapidly increasing amount of text for people to read (especially around elections and policy), we still teach undergrads to read it line by line instead of using more sophisticated methods of filtering and searching it.

I frame this effort as a bridge between qualitative context analysis and empirical analysis methods. In the past, people that crunch numbers haven't worked on content analysis of (political) communication because it is viewed as hard work with minimal payoff. I’ve found that using machine learning and natural language processing methods can resolve these workload problems.

My shift to Information Science (with a Computer Science Minor specialization in Machine Learning/Natural Language Processing) at Cornell was a part of my realization that my work is applicable across a range of social science research projects in several disciplines. Cornell’s unique and long history in machine learning, information retrieval, and natural language processing were attractive. And, when coupled with its extremely supportive environment for multi-disciplinary relationships between engineering and social science researchers, it was clear that I had a found a home.

I work as a part of Claire Cardie and Jeff Hancock’s research group. Thorsten Joachims is my minor adviser in Computer Science/Machine Learning.  Dan Cosley is my other Information Science Field special committee member.

In my first few years at Cornell, I worked on e-government research.  I’ve completed my Computer Science minor specialized in machine learning/data mining/applications in NLP.  And, in my only teaching experience to date (Cornell’s CS 5150 Software Engineering Course with Prof. Bill Arms), I won both the Yahoo! Research Award for Distinguished Teaching and the Cornell Computer Science Department’s Teaching Assistant Award.  Additionally, 30% of the student groups turned their class projects into companies.  One of the student project groups – Seamless Receipts -- received $250,000 in venture capital funding at the end of the semester.  And I now work on the detection of emotion in social media, an extremely challenging topic.

As I mentioned, prior to my return to academia, I worked on software development projects in a range of software engineering and product management positions. I still work as a software development consultant. This experience lends a practical side to my academic research. I still highly value producing tools, which people benefit from each day.

Academic Degrees:

        Harvard University, Master of Public Policy

        University of Washington, Bachelor of Arts, Political Science

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