From Monday, June 17 to Friday, June 28, 2019, the Summer Institute in Computational Social Science is sponsoring a Chicago partner site hosted in downtown Chicago. The purpose of the Summer Institute is to bring together graduate students and early career researchers in both social science (broadly conceived) and data science (broadly conceived). Content will include live-streamed lectures from the main site at Princeton University as well as guest speakers who will present on cutting-edge computational research and methods. Topics covered include text analysis, digital data collection, experimental design, non-probability sampling, agent based modeling, and ethics.
One of the main goals of the Summer Institute is to bring together Chicagoland scholars from a range of computational and social sciences to collaborate and learn together. Participants will get hands-on experience using computational methods to test social theories and will develop group projects to present at the end of the second week. One or more collaborative projects that demonstrate extraordinary promise and interdisciplinarity will receive pilot funding for further development, and all participants will be given support in accessing and utilizing the many data sources freely available for research and analysis.
We invite applications from graduate students, postdoctoral researchers, and untenured faculty within 7 years of their Ph.D. Due to limited space, up to twenty participants will be invited. Participants with less experience with social science research will be expected to complete additional readings in advance of the Institute, and participants with less experience coding will be expected to complete a set of online learning modules on the R or Python programming language. Students doing this preparatory work will be supported by a teaching assistant who will hold online office hours before the Institute. To facilitate the planning process, you must submit your application materials by March 30th, 2019.
There is no cost to participate in SICSS-Chicago, and we will provide breakfast and lunch for all on-site days (see schedule for details). Participants from any geographic location are encouraged to apply. Those traveling from outside the Chicago area must provide their own travel and lodging.
Application materials should be received by March 30th, 2019.
We will notify applicants solely through e-mail in mid-April, and will ask participants to confirm their participation very soon thereafter. Inquiries can be sent to email@example.com
Kat Albrecht is pursuing a PhD in Sociology at Northwestern University and a JD at the Northwestern Pritzker School of Law. Her research focuses on investigating how the structure of data shapes research conclusions and broader sociological theory. Using machine learning methods, quantitative causal inference, and mapping techniques she primarily builds and analyzes large criminal justice datasets. She is especially concerned with the economics of fear, the working definition of homicide, and the general state of crime data. She received her bachelor’s degree from the University of Minnesota where she first began exploring the junction of computational methods and the social sciences.
Natalie Gallagher is doctoral student in psychology at Northwestern University. She is fascinated by the human ability to think about social phenomena that emerge from human interaction - social networks and social categories. Exploring these, her work lies at the intersection of social and cognitive research. She draws on psychological, sociological, and computational methods to pursue her questions, and is interested in how research can inform social change. Natalie received her BA in psychology and theater from Georgetown University, and has an MA in psychology from Northwestern.
Tina Law is a PhD student in sociology at Northwestern. Her research explores why we continue to live in unequal neighborhoods even as our cities are constantly changing. In particular, she uses computational methods and large-scale, digitized data from administrative systems and archival sources to understand how historical events shape contemporary neighborhood racial inequality. She is a National Science Foundation Graduate Research Fellow. She holds an MA in sociology from Yale.
Matt Salganik, Chris Bail, more coming soon.
Andrew Papachristos is Professor of Sociology at Northwestern University and he is the Director of the Northwestern Neighborhood and Network (N3) Initiative. He is also a Faculty Fellow at the Institute for Policy Research at Northwestern. His research aims to understand how the connected nature of cities—how their citizens, neighborhoods, and institutions are tied to one another—affect what we think, feel, and do. His main area of research applies network science to the study of gun violence, police misconduct, illegal gun markets, street gangs, and urban neighborhoods.
Rochelle is a Provost’s Postdoctoral Fellow in the Department of Political Science at the University of Chicago, where she’ll begin as Assistant Professor in Fall 2020. Her research examines international norms, gender and advocacy, with a focus on the Muslim world. She is currently working on a book project that examines resistance and defiance towards international norms. The manuscript is based on her dissertation, which won the 2017 Merze Tate (formerly Helen Dwight Reid) Award for the best dissertation in international relations, law, and politics from the American Political Science Association. She teaches computational social science at both the undergraduate and graduate levels, including Machine Learning for Political Science at Stanford and Introduction to Computational Tools and Techniques at Berkeley. She is a certified instructor with Software Carpentry and Data Carpentry. She received her Ph.D. in Political Science with a designated emphasis in Gender & Women’s Studies at the University of California, Berkeley. Before coming to Chicago, she was a post-doc at the Center for International Security and Cooperation at Stanford University.
As we discussed in our call for applications, we have arranged two types of training prior to the event this summer. Some students have more sophisticated coding skills but little exposure to social science; other students have significant exposure to social science but lack strong coding skills.
The majority of the coding work presented at the 2019 SICSS will employ R. However, you are welcome to employ a language of your choice- such as Python, Julia, or other languages that are commonly used by computational social scientists. If you would like to work in R, we recommend that you complete the following courses within DataCamp, a website that teaches people how to code. Obviously, you only need to complete the classes with material that you would like to learn.
Additional readings will be provided on sub-Saharan Africa perspectives.
If you cannot afford datacamp, check out Chris Bail’s Intro to R slides at http://www.chrisbail.net/p/learn-comp-soc.html, or Charles Lanfear’s course at [https://clanfear.github.io/CSSS508/] or Grolemund and Wickham’s online book [https://r4ds.had.co.nz/].
Our institute will bring together people from many fields, and therefore we think that asking you to do some reading before you arrive will help us use our time together more effectively. First, we ask you to read Matt’s book, Bit by Bit: Social Research in the Digital Age, which is a broad introduction to computational social science. Parts of this book will be review for most of you, but if we all read this book ahead of time, then we can use our time together for more advanced topics.
Also, for students with little or no exposure to sociology, economics, or political science, we have assembled a collection of exemplary papers in the core areas addressed by the Russell Sage Foundation. Neither your work nor the work we develop together at the institute need map neatly onto these categories, but if those with less exposure to social science read these, we will increase the chances of interdisciplinary cross-pollination, which we view as critical to the future of computational social science.