Improving data collection efficiency and accuracy in link-tracing designs (e.g. Respondent driven sampling); Partial graph data collection strategies for networks (e.g. Aggregated Relational Data); Large scale models for anomaly detection on graphs; Developing models to represent structure in networks using low dimensional manifolds; Modeling demographic and health trends in low-resource settings; Developing a decision-making framework for policy decisions based on predictions from statistical and machine learning models
Requirements:
Applicants must have a Ph.D. in Statistics, Computer Science, Economics, Sociology, or related field
Text:
Postdoctoral Scholar - Department of Statistics Improving data collection efficiency and accuracy in link-tracing designs (e.g. Respondent driven sampling); Partial graph data collection strategies for networks (e.g. Aggregated Relational Data); Large scale models for anomaly detection on graphs; Developing models to represent structure in networks using low dimensional manifolds; Modeling demographic and health trends in low-resource settings; Developing a decision-making framework for policy decisions based on predictions from statistical and machine learning models Applicants must have a Ph.D. in Statistics, Computer Science, Economics, Sociology, or related field
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