Neville Granted CAREER Award - Department of Statistics - Purdue University Skip to main content

Neville Granted CAREER Award


Assistant Professor Jennifer Neville has been awarded a grant by the Faculty Early Career Development (CAREER) Program from the Division of Information and Intelligent Systems of the National Science Foundation (NSF). This prestigious award supports junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research, to build a firm foundation for a lifetime of leadership in integrating education and research. Neville's award for her proposal titled "Machine Learning Methods and Statistical Analysis Tools for Single Network Domains" will be funded from January 2012 through December 2016 for an estimated total of $496,640.

Although estimation and inference methods from the field of statistical relational learning have been successfully applied in single-network domains, the algorithms were initially developed for populations of networks, and thus the theoretical foundation for learning and inference in single networks is scant. Jennifer's research will focus on the development of robust statistical methods for single network domains - since many large network datasets about complex systems rarely have more than a few subnetworks available for model estimation and evaluation. Specifically, the aims of the project include:

  1. strengthening the theoretical foundation for learning in single network domains
  2. creating accurate methods for determining the significance of discovered patterns and features
  3. formulating novel model selection and evaluation methods
  4. developing improved approaches for network learning and prediction based on the unique characteristics of single network domains

The research will enhance our understanding of the mechanisms that influence the performance of network analysis methods and drive the development of novel methods for complex network domains. Expanding the applicability of machine learning techniques for single network domains could have a transformational impact across a broad range of areas (e.g., psychology, communications, education, political science) where current methods limit research to the investigation of processes in dyad or small group settings.

Congratulations, Professor Neville!

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