Austin R. Benson

Assistant Professor
Computer Science

Research Interests

My research develops computational frameworks for analyzing and understanding large-scale and complex datasets from the Web, social networks, biology, and other scientific domains. I usually approach problems with a combination of network science, matrix and tensor computations, and applied machine learning.

My research is supported by the ARO and the NSF.


Teaching Interests

  • CS 6241: Numerical Methods for Data Science.
  • CS 2850/INFO 2040/ECON 2040/SOC 2090: Networks

Selected Publications

  • Simplicial closure and higher-order link prediction.
    Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg.
    Proceedings of the National Academy of Sciences (PNAS), 2018.
  • Found Graph Data and Planted Vertex Covers.
    Austin R. Benson and Jon Kleinberg.
    Advances in Neural Information Processing Systems (NeurIPS), 2018.
  • Sequences of Sets.
    Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
    Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018.
  • Higher-order clustering in networks.
    Hao Yin, Austin R. Benson, and Jure Leskovec.
    Physical Review E (PRE), 2018.
  • A Discrete Choice Model for Subset Selection.
    Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
    Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2018.