I grew up in St. Paul, MN and attended Claremont McKenna College from 2000 to 2005, where I graduated with a Bachelor of Arts in Computer Science and Politics (double major, CS course work at Harvey Mudd College). During this time, I took leaves of absence to work on political campaigns in Minnesota and Alaska, serving as Data Manager on Paul Wellstone’s U.S. Senate campaign and Campaign Manager on Mike Yourkowski’s Alaskan State Senate campaign. After graduating, I worked for several years providing web development and database services for non-profits and businesses both big and small.

In 2008, I began doctoral studies in the Political Science Department of the University of Illinois at Urbana-Champaign, with a focus on American politics and political methodology. My campaign experience generated questions daily, but in the heat of the campaign it is difficult to make a systematic study of political behavior. I wanted to understand how citizens understood the political process and what would make them more interested in participating in politics. To better serve these substantive interests, I also pursued a master’s degree in statistics. Prior to starting this program, I had little exposure to statistics, and what I little I had emphasized rote application of standard methods, with little understanding of statistical theory. I was astounded to find an elegant connection between real world processes and mathematical description that still took into account the difficulties of data collection and noisy results. I had always enjoyed building computational models to describe real world problems; statistics added a rigorous mathematical framework to this activity.

After completing my master’s degree in statistics, I decided to shift my doctoral focus to statistics as well and enrolled in the Statistics Department at UIUC. Just as my campaign work prompted my substantive political questions, my studies in political science motivates much of my statistical interests. Researchers, particularly in the social sciences, have detailed, nuanced theories of human behavior, but often find themselves forced to choose from off-the-shelf statistical models and assumptions. My goal is to simplify assumptions and methods to make them more accessible to practicing researchers, while eliminating the gap between substantive theory and statistical inference. In particular, many social science theories involve interactions between people, groups, and the environment that are difficult to capture in standard statistical frameworks, particularly those that rely on strong independence assumptions. I am particularly enthusiastic about using algorithmic models, specified not as equations but as computer programs, to capture more substantive theory while maintaining strict statistical rigor. As a convenient side benefit, models implemented in software make reproduction of research a more straight-forward task, another area in which I strongly believe.

In addition to my academic pursuits, I am the proud husband to Devin and father to Rune and Hazel. When I’m not in my office, you can find me in my garden with my children or riding to the library on my bicycle. Both my wife and I volunteer as mentors through the amazing CU One to One program. On Saturdays, you can find me on the rugby pitch with the Champaign County Flatlanders, where I serve as president of the club. I am also the chair of the W. S. Gosset Society, a reading group for statistics PhD students at UIUC.