Austin R. Benson
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
- Spring 2019. CS 6241: Numerical Methods for Data Science.
Office Hours: Tuesdays, 1:30pm–2:30pm, Gates 413B - Fall 2018. 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.