Topics in Social Data

In-depth survey of methods for the analysis of large-scale social and behavioral data. Particular focus on recent developments in preference learning. Connections made to graph-theoretic investigations common in the study of social networks. Topics include discrete choice theory, random utility models, item-response theory, rank aggregation, centrality and ranking on graphs, and random graph models of social networks. Intended for Ph.D. students, but masters students with adequate background and interest in research topics are welcome to apply. Strongly recommended: 200-level courses in stochastic modeling (most specifically, Markov chains), optimization, and machine learning (e.g., MS&E 211, 221, 226, and CS161 or equivalents). Limited enrollment. Please complete the application here: https://forms.gle/MWJaehpMKc2hrrU27