From the evening of Sunday, June 16 to the morning of Saturday, June 29, 2019, the Russell Sage Foundation and the Alfred P. Sloan Foundation will sponsor the Summer Institute in Computational Social Science, to be held at Princeton University. The purpose of the Summer Institute is to bring together graduate students, postdoctoral researchers, and beginning faculty interested in computational social science. The Summer Institute is for both social scientists (broadly conceived) and data scientists (broadly conceived).
Because many more people are expected to apply for the workshop than can be accommodated at Princeton, we are hosting a partner site in Boston. The site is organized by former participants of the 2018 SICSS workshop and will feature live streams of the primary location in addition to local speakers. In the afternoons of the first week, participants at the Boston location will also be able to work in teams to learn how to implement the material from the lecture. In the second week, participants will join teams to develop a research project related to computational social science.
There will be ample opportunities for students to discuss their ideas and research with the organizers, other participants, and visiting speakers. One goal of this partner location is to build and expande the network of computational social scientists in the Boston area.
We are inviting applications from Ph.D. students, postdoctoral researchers, and untenured faculty within 7 years of their Ph.D. Meals will be provided during the workshop and we expect to invite about twenty to thirty participants. 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 programming language. Students doing this preparatory work will be supported by a teaching assistant who will hold online office hours before the Institute.
Application materials will be due on Friday, March 29, 2019. Note that this is a month after the main application deadline and after decisions about the main location are expected to have been made. If you have also applied to the Princeton location and have not yet heard from them at the time of your submission, please let us know in your cover letter. Applying to both locations will not affect your chances of being accepted to the Princeton location. However, we assume that if you are accepted there, you will not be joining us in Boston.
There is no cost to participating in the Boston workshop. Limited travel subsidies may be available and, depending on interest and funding, we are considering booking a large Airbnb for participants who are not local. Please let us know in your cover letter if this is something you would be interested in.
All events will be held at:
50 Ames Street
Cambridge, MA 02142
We thank IDSS for providing the space and look forward to announcing additional sponsors for this partner location in the coming months.
Ryan J. Gallagher is a PhD student at Northeastern University. At the Network Science Institute, he researches the dynamics of social networks using tools and theory from natural language processing and communications. He currently studies the affective phenomena of networked counterpublics. Ryan holds an MS in mathematics from the University of Vermont, where he worked with the Computational Story Lab at the Vermont Complex Systems Center, and a BA in math from the University of Connecticut.
David is a PhD candidate in Decision Sciences at Carnegie Mellon University and a visiting scholar at the Wharton School at the University of Pennsylvania. His interests include information avoidance, behavioral interventions (nudges), and decisions from experience. In his dissertation, David studies persuasion in the presence of motivated reasoning. While we might think that changing someone’s mind is all about exposing them to facts that support our views and challenge theirs, such an approach may be more likely to engender defensive information avoidance rather than receptive information processing.
Eaman Jahani is a graduate research assistant pursuing a PhD degree in Social and Engineering Systems with a minor in Statistics at MIT IDSS. Prior to MIT, he was a software engineer at Google for 4 years. His main training is in statistics and computer science, but recently he has been appreciating econometrics and modeling in applied economics. His past research examined the extent of bubbles vs truth-seeking in cryptocurrency markets and socio-economic prediction in social networks. His current research focuses on structural factors such as networks or institutions that regenerate inequality at a micro scale. Eaman spends too much time reading political commentaries.
Yan is currently pursuing a Ph.D. at Human Dynamics group at MIT. She received dual masters in Computer Science and Transportation Engineering from MIT in 2016. Yan is interested in using a broad range of computational techniques to understand the network effect of social influence. In particular, she works on the inference, identification, and modeling of social influence and social learning with large-scale behavioral data in a networked environment. Besides, she also works on the combining network structure and personal attributes in maximizing cascading payoff.
Sanaz Mobasseri is an Assistant Professor of Organizational Behavior at Boston University’s Questrom School of Business. She received her PhD from the Management of Organizations Department at UC Berkeley’s Haas School of Business. Her research examines the role of emotion, cognition, and culture in shaping social networks and labor market outcomes. Much of her work is situated in organizational settings, where she examine the microfoundations of workplace inequality. Although grounded in sociology and organizational theory, her work integrates theoretical insights from social psychology and sociolinguistics. Her research methods are similarly diverse, ranging from experimental studies in the lab to audit studies in the field to computational approaches applied to large archival data sets.
The schedule will be posted in the coming months.