Some locations of the 2020 Summer Institutes in Computational Social Science have been postponed because of COVID-19. Other locations are still planning to take place, but each organizer is monitoring the situation carefully.
June 15 to June 26, 2020 | UC Berkeley | Berkeley, California
Sharad Goel is an assistant professor at Stanford in the Department of Management Science & Engineering, in the School of Engineering. He also has courtesy appointments in Computer Science, Sociology, and the Law School. His primary area of research is computational social science, an emerging discipline at the intersection of computer science, statistics, and the social sciences. Sharad is particularly interested in applying modern computational and statistical techniques to understand and improve public policy. Sharad is the founder and executive director of the Stanford Computational Policy Lab, a team of researchers, data scientists, and journalists that addresses policy problems through technical innovation.
David Harding (Ph.D. Harvard, 2005) is a Professor of Sociology and Faculty Director, Social Science D-Lab at at University of California, Berkeley. David J. Harding has taught quantitative methods for over ten years at both the University of Michigan and UC Berkeley. In his own research, he has used various methods for causal inference, including propensity score matching, sensitivity analysis, inverse probability of treatment weighting, panel data models, regression with residuals, field experiments, and natural experiments. His recent work has appeared in the American Sociological Review, American Journal of Sociology, Social Forces, Proceedings of the National Academy of Science, and Nature Human Behaviour, among other journals.
Jae Yeon Kim
Jae Yeon Kim is a PhD candidate in Political Science, a D-Lab data science fellow, and a data science education program fellow at UC Berkeley. He uses behavioral science, statistics, and data science tools to study how people think and behave with a focus on diversity and inclusion issues. His award-winning dissertation applies computational, statistical, and qualitative methods to understand what unites racial minority groups in the United States. His most recent research interest is narrowing the gap between the ethics and practice of using machine learning.
Jaren Haber is a PhD candidate in sociology at the University of California, Berkeley. His research applies computational methods to study how organizational contexts shape the impacts of structural inequalities. Jaren has studied whether charter school identities reinforce stratification by race and class, and at SICSS 2019 he joined Nick and Jae (fellow BAY-SICSS organizers) to conduct experiments evidencing how school websites' racial cues influence perceptions of school quality. He also studies text analysis workflows for social science. Jaren is currently on the job market and appreciates referrals.
Nick Camp is a postdoctoral researcher at Stanford University, where he received his PhD in social psychology in 2018. His research examines racial disparities in the everyday encounters between police officers and citizens, drawing on a range of methods, from computational studies of officer body-worn camera footage, experiments in community and lab settings, to analyses of traffic stop data. Starting Fall 2020, Nick will be an assistant professor of Organizational Studies at the University of Michigan.
AJ is a PhD candidate in Education at Stanford University. His research lies at the intersection of the sociology of education, sociolinguistics, and data science. His ongoing dissertation work examines a large corpus of college admissions essays written by Latinx identifying students to understand the relationships between an applicant's context and the content of their essay. Outside of research, AJ has experience with activism and advocacy in his hometown of Salinas, CA. Prior to starting the PhD program, AJ was a high school English teacher in Miami, FL.
Emily Grabowski is a PhD student in linguistics at UC Berkeley. Her research combines computational and experimental approaches to investigate the relationship between speech perception and production. She also explores applications of machine learning to speech, including the use of supervised and unsupervised methods to discover structure and applications in speech.
Irene is a PhD student in City & Regional Planning at UC Berkeley. Irene’s interests lie at the intersection of sociology, public health, and urban analytics. Her current research examines how macro socioeconomic and political structures affect low-income individuals’ health outcomes in cities. In particular, the role that big corporations play in structuring the food environment and how it affects ultra-processed food exposure at different spatial scales.
Mahnaz is a Postdoc research fellow at Stanford University. She is a computer scientist with data science skills who is interested to collaborate across disciplines and develop and apply statistical and machine learning methods on social science issues. Her current research focuses on applying computational methods to understand behavioral, network and smartphone data to explore how the individuals’ characteristics and behavior shape their position in, or effect on, social networks. Mahnaz is broadly interested in Machine learning and Causal inference to reason better with behavioral and network data to solve societal problems.
Saqib is a PhD student at the Haas School of Business, UC Berkeley. He completed a bachelor's degree in Electrical Engineering from Indian Institute of Technology and a master's degree in Public Policy from the Goldman School of Public Policy. He previously worked on technology policy issues in the Indian context. His research interests include entrepreneurship, technology and innovation policy, and social networks.
Tyler is a PhD student studying sociology at Stanford University. His current research focuses on school policies and individuals’ daily experiences with segregation. Using survey data, he compares racial and socioeconomic segregation experienced - in locations such as friends’ homes, places of worship, and shopping centers - by students who make different school choices. Broadly, he is interested in spatial inequalities, meritocracy, and predictive algorithms.
Host a Location
You can host a partner location of the Summer Institutes of Computational Social Science (SICSS) at your university,
company, NGO, or government agency.