From Monday, June 17 to Friday, June 28, 2019, the California Center for Population Research (CCPR) will sponsor a partner site for the Summer Institute in Computational Social Science (SICSS) in Los Angeles. The purpose of the Summer Institute is to bring together graduate students, postdoctoral researchers, and early career faculty interested in computational social science. The Summer Institute is open to both social scientists (broadly conceived) and data scientists (broadly conceived).
The site is organized by former participants of the 2018 SICSS workshop and will feature live streams of the primary location at Princeton in addition to local speakers who will present on cutting-edge computational social science research. Topics covered in coordination with the Princeton site will include text analysis, digital data collection, online field experiments, non-probability sampling, agent based modeling, and ethics. A site-specific focus at this partner institute will be machine-learning methods for causal inference. During the first week, participants will gain hands-on experience through group work implementing the material from the lectures. For the second week, participants will develop research projects drawing on the content from the first week and form teams to implement these projects.
SICSS-Los Angeles will be held at the University of California at Los Angeles (UCLA). There is no cost to participate, and we will provide refreshments for all on-site days (see schedule for details). Applications are open to all interested individuals regardless of location, but we cannot provide support for travel and lodging in Los Angeles.
We invite applications from graduate students, postdoctoral researchers, and untenured faculty within 7 years of their Ph.D. SICSS-Los Angeles is committed to diversity and inclusion in computational social science, and we particularly encourage applicants from groups currently underrepresented in the field. We anticipate to admit roughly twenty 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. To support this preparatory work, participants will have access to a teaching assistant who will hold online office hours before the Institute.
Application materials will be due on Sunday, April 14, 2019. Note that this is six weeks 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 application.
We are grateful to the California Center for Population Research for their sponsorship of the partner site.
Friedolin Merhout is a doctoral student in the Duke Sociology department. He enjoys exploring how computational methods provide a new lens to view longstanding social science debates, and pondering the potential inherent in the wealth of digital trace data. Before starting the doctoral program at Duke, he earned a BA from Freie Universitaet in his hometown Berlin.
Alina Arseniev-Koehler is currently a graduate student at the University of California Los Angeles pursuing a PhD in Sociology. Substantively, her research interests include culture, cognitive sociology, language, and health and illness. Methodologically, she is interested in computational social science and machine-learning, with a focus on the computational analysis of language. Her Master’s research aimed to provide a cognitively plausible, computational account of the schemata activated by news reporting on obesity. Alina also enjoys learning and teaching new computational techniques and helps coordinate the Computational Sociology Working Group at UCLA.
Jennie E. Brand is Professor of Sociology and Statistics at the University of California, Los Angeles (UCLA). She is Director of the California Center for Population Research (CCPR) and Co-Director of the Center for Social Statistics (CSS) at UCLA. She is Chair-Elect of the Methodology Section of the American Sociological Association (ASA) and an elected Board Member of the International Sociological Association (ISA) Research Committte on Social Stratification and Mobility (RC28). Prof. Brand is a member of the Board of Overseers of the General Social Survey (GSS) and a member of the Technical Review Committee for the National Longitudinal Surveys Program at the Bureau of Labor Statistics. She received the ASA Methodology Leo Goodman Mid-Career Award in 2016, and honorable mention for the ASA Inequality, Poverty, and Mobility William Julius Wilson Mid-Career Award in 2014. Prof. Brand studies social stratification and inequality, mobility, social demography, education, and methods for causal inference.
Pablo Geraldo Bastías is a graduate student at the University of California Los Angeles (UCLA) affiliated to the California Center for Population Research (CCPR). His research examines how institutions influence inequality in education and the labor market, with a particular focus on skill formation systems and school-to-work transitions. He is interested in the intersection of causality, machine learning, and network analysis.
Bernard is a sociology graduate student at UCLA. He developed research interests in culture, science, and computational methods through previous experiences in comparative genomics/bioinformatics and science education research. His master’s thesis adapted models from macroevolutionary biology to explain the historical trajectories of cultural populations like music genres, scientific fields, and industries. For his dissertation, he’d like to focus on how deep learning can be applied to network and causal inference problems to help identify how we can make science more efficient, productive, and equitable. Bernard is passionate about collaborative science and teaching, and has given workshops on programming, machine learning, and/or computational social science for the National Human Genome Research Institute (NIH), the UCLA Library, and the UCLA Sociology Department.
Gregory DeAngelo is an Associate Professor in the Department of Economic Sciences at Claremont Graduate University. He works closely with public sector agencies to address pressing questions of criminal justice policy, identifying the causal effects of actions by both legal and extra‐legal actors on public safety. His research ranges from the identification of the effect of judicial and prosecutorial incentives on the outcomes of criminal cases to the impact of law enforcement strategies on human trafficking. At the core of his work is a desire to advance criminal justice reform by identifying the causal impacts of policies, incentives, and actions by legal and extra‐legal actors on public safety, and then generating technologies with the potential to counteract any negative externalities of these actions.
Tim Dennis is the Director of the UCLA Library Data Science Center where he provides data science support, including instruction, one-on-one consulting, and community building. He is a regular user of R, Python, SQL and command-line tools and has extensive experience helping researchers and students with these tools. He’s also an instructor with The Carpentries, a global volunteer run educational community that teaches foundational coding and data science skills to researchers.
Dennis Feehan is a demographer and quantitative social scientist whose research interests lie at the intersection of networks, demography, and quantitative methodology. He is an Assistant Professor of Demography at the University of California, Berkeley. Prior to joining the demography department at Berkeley, he received his Ph.D. at Princeton’s Office of Population Research and worked as a Research Scientist at Facebook.
Jungseock Joo is an Assistant Professor in the Department of Communication at the University of California, Los Angeles. He received is Ph.D. in Computer Science from UCLA in 2015 and worked as a Research Scientist at Facebook before returning to UCLA to join the Department of Communication.
Ka-Yuet Liu is an Associate Professor in the Department of Sociology, UCLA, whose research mostly focuses on the intersections between social network analysis and social epidemiology. She received her D.Phil. (Sociology) in 2008 from the University of Oxford and completed a post-doc at Columbia University before joining UCLA as a faculty member in 2012.
Judea Pearl is a computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks. He is credited for developing a theory of causal and counterfactual inference based on structural models. He is the 2011 winner of the ACM Turing Award, the highest distinction in computer science.
Sam Pimentel is an Assistant Professor in the Statistics Department at UC Berkeley. His research centers on methodology for causal inference in observational studies. He develops new ways to form matched comparison groups in large observational datasets using approaches from discrete optimization. These tools allow transparent and interpretable inferences about the effects of interventions, and provide opportunities to study the impact of potential unobserved confounding variables. He is also interested in applying these methods in health services research, public policy, and the social sciences.
Zachary C. Steinert-Threlkeld is an Assistant Professor of Public Policy at the UCLA Luskin School of Public Affairs. His research interests are at the border of international and comparative politics, exploiting in particular vast social media data to study subnational conflict. His current research focuses on the mobilization of mass protest during the Arab Spring and Ukraine’s Euromaidan protests, as well as elite behavior and state repression in authoritarian regimes.
Christ Bail, Justin Grimmer, Alondra Nelson, Beth Noveck, Matt Salganik, and Chris Wiggins
9:00 - 9:15 Local Logistics
9:15 - 9:45 Introduction to computational social science (Princeton Stream)
9:45 - 10:15 Why SICSS? (Princeton Stream)
10:15 - 10:30 Coffee Break
10:30 - 10:45 What is digital trace data? (Local and Princeton Stream)
10:45 - 11:00 Strengths and weakness of digital trace data (Local and Princeton Stream)
11:00 - 11:30 Application Programming Interfaces (Local and Princeton Stream)
11:30 - 12:00 Building Apps and Bots for Social Science Research (Local and Princeton Stream)
12:00 - 1:00 Lunch
1:00 - 2:30 Guest speaker: Alondra Nelson (Princeton Stream)
2:30 - 2:45 Break
2:45 - 3:00 Introduction to the group exercise
3:00 - 5:00 Group exercise
8:45 - 9:00 Logistics
9:00 - 10:00 Chris Wiggins (Princeton Stream)
10:00 - 10:15 Coffee Break
10:15 - 12:00 Supervised Machine Learning
12:00 - 1:00 Lunch
1:00 - 1:15 Introduction to the group exercise
1:15 - 3:15 Group exercise
3:15 - 3:30 Break
3:30 - 5:00 Guest Speaker: TBD
9:00 - 9:15 Logistics
9:15 - 9:30 History of quantitative text analysis (Local and Princeton Stream)
9:30 - 9:45 Basic Text Analysis/GREP (Local and Princeton Stream)
9:45 - 10:00 Dictionary-Based Text Analysis (Local and Princeton Stream)
10:00 - 10:15 Coffee Break
10:15 - 11:15 Topic models/Structural Topic Models (Local and Princeton Stream)
11:15 - 11:30 Text Networks (Local and Princeton Stream)
11:30 - 11:45 Introduction to group exercise
11:45 - 1:00 Lunch
1:00 - 2:15 Guest Speaker: Zachary Steinert-Threlkeld
2:15 - 2:30 Break
2:30 - 5:00 Group Exercise
9:00 - 9:30 Survey research in the digital age (Local and Princeton Stream)
9:30 - 10:05 Probability and non-probability sampling (Local and Princeton Stream)
10:05 - 10:15 Coffee break
10:15 - 10:40 Computer-administered interviews and wiki surveys (Princeton Stream)
10:40 - 11:05 Combining surveys and big data (Princeton Stream)
11:05 - 12:-05 Guest Speaker: Jungseock Joo
12:05 - 1:00 Lunch
1:00 - 2:30 Guest speaker: Justin Grimmer (Princeton Stream)
2:30 - 4:45 Continue group exercise (Not open to public/No livestream)
4:45 - 5:00 Discuss activity and open-source data
9:15 - 10:30 Local logistics
9:15 - 10:30 Speed-dating and group formation for week 2 projects
10:30 - 10:45 Coffee break
10:45 - 11:00 Mini-conference opening and introduction
11:00 - 12:00 Guest Speaker: Greg DeAngelo
12:00 - 1:00 Lunch
1:00 - 2:00 Computational causal research workshop
2:00 - 3:15 Panel I: Digital Demography (Dennis Feehan and Ka-Yuet Liu)
3:15 - 3:30 Break
3:30 - 4:45 Panel II: Computational Causal Inference (Judea Pearl and Sam Pimentel)
4:45 - 5:00 Break
6:00 - 7:30 Reception
9:30 - 10:30 [Optional] Panel on book publishing: Meagan Levinson (Senior Editor, Princeton University Press), Eric Schwartz (Editoral Director, Columbia Univesity Press), and Chris Bail (Editor of the Oxford University Press Series in Computational Social Science) (Princeton Stream)
12:00 - 1:00 Lunch and participant flash talks
1:00 - 2:30 [Optional] Guest speaker: Beth Noveck (Princeton Stream)
12:30 - 1:30 Lunch and participant flash talks
12:30 - 1:30 Lunch and participant flash talks
1:30 - 5:15 Present group projects