The following was written by Theresa Gebert, an undergraduate student at Harvard University.
As part of its Education and Outreach Program this year, SAMSI offered a two-day undergraduate workshop on “topics of current interest in statistics and applied mathematics.” In addition to an overview SAMSI Research Programs, the program topic was Computational Methods in Social Sciences.
Just like many of the other program participants, I had heard about this workshop from my advisor, Joe Blitzstein, the co-director of the Undergraduate Statistics Program at Harvard. Despite my summer research experience in the statistical analysis of human behavior, I was not quite sure what to expect when I flew from Boston, Massachusetts to Raleigh, North Carolina on a Wednesday night. What sorts of computational methods do social scientists need?
As it turns out: many. These methods range from the simple t-test to complex, high-dimensional network analysis. We learned about the building blocks of social networks from Professor Krista Gile, including what we often exclude in our representations, such as multi-modal networks. We learned about political networks from Bruce Desmarais and the idea that the study of people can actually be a systematic process. We were also introduced to issues of data confidentiality by Jerry Reiter, who explained that recoding variables or hiding values is not always enough. (The solution he suggested, first introduced by statistician Donald Rubin, is fully synthetic datasets!) On the very first day, the undergraduate participants were opened to the world of social science as a quantitative problem, which was certainly an approach very different from the one I had encountered before.
But beyond the classroom, the workshop also encouraged the social science of learning from others’ experiences as well. Every speaker started by introducing their own academic and life trajectory: what did they start out doing? What do they do now? How have their goals and dreams changed over time? It was fascinating to learn about the power and the limits of our knowledge in the realm of social networks, but it was equally fascinating to hear that people who had studied English, Chemistry, or hated math, ended up finding their way into social science and statistics somehow.
In addition, it was very valuable to get to know and learn from fellow participants as well. I met some of the brightest, most intellectually curious undergraduate students in those two days. Whether we were bonding over the complexity of the lecture we had just heard, the lab tutorial we were trying to solve, or the problem sets we had to finish before we flew back to our respective schools, it was truly an atmosphere of intellectual curiosity and camaraderie. I have remained in touch with several of the participants; I certainly hope I might get the chance to enter mathematics competitions or hackathons with them in the future!
The next day, David Banks presented a fascinating lecture on dynamic network models; starting with concepts directly from graph theory, Banks ended the talk with his own research in the social network of political blogging in the aftermath of the Trayvon Martin incident, which he conducted using statistical techniques in machine learning and language recognition. It was followed by a practical introduction to the software R and a package that enables the visualization of social networks.
Even though the workshop was just two days, it was surprisingly difficult to fly back to Boston after such an inspiring, intellectual hiatus from college life. What was so satisfying about my experience was that I got the chance to expand both my academic and social networks; I had engaging conversations with fellow participants as well as the brilliant SAMSI post-graduate fellows, conversations which never failed to spark new ideas and interests. I got the chance to meet and listen to professors from a variety of fields and high levels of achievement, who were also incredibly approachable and genuine. The workshop completely surpassed every expectation and cemented my belief that graduate school in Statistics is my dream. I certainly hope I will get the chance to become more involved with SAMSI in the future, and I am so grateful to them for making these opportunities possible for the undergraduate community!