I am a PhD student in computational math at Stanford advised by Jure Leskovec. My research focuses on network science, data mining, and matrix and tensor computations. I have interned with Google (four times—2016, 2015, 2012, 2011), Sandia National Labs (2014), and HP Labs (2013). Before coming to Stanford, I studied EE/CS and Applied Math across the bay at Berkeley.

Honors & Awards

  • Stanford Graduate Fellowship, Stanford University (2012-2015)

Education & Certifications

  • BA, University of California, Berkeley, Applied Mathematics (2012)
  • BS, University of California, Berkeley, Electrical Engineering and Computer Sciences (2012)

Stanford Advisors

Current Research and Scholarly Interests

networks, matrices, and data

All Publications

  • Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices Proceedings of Neural Information Processing Systems (NIPS) Benson, A. R., Lee, J. D., Rajwa, B., Gleich, D. F.
  • On the relevance of irrelevant alternatives 25th International Conference on World Wide Web (WWW) Benson, A. R., Kumar, R., Tomkins, A.
  • Modeling user consumption sequences 25th International Conference on World Wide Web (WWW) Benson, A. R., Kumar, R., Tomkins, A.
  • Learning multifractal structure in large networks 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) Benson, A. R., Riquelme, C., Schmit, S.
  • Higher-order organization of complex networks SCIENCE Benson, A. R., Gleich, D. F., Leskovec, J. 2016; 353 (6295): 163-166


    Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks--at the level of small network subgraphs--remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.

    View details for DOI 10.1126/science.aad9029

    View details for Web of Science ID 000379208400037

    View details for PubMedID 27387949

  • Silent error detection in numerical time-stepping schemes INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS Benson, A. R., Schmit, S., Schreiber, R. 2015; 29 (4): 403-421
  • A Framework for Practical Parallel Fast Matrix Multiplication ACM SIGPLAN NOTICES Benson, A. R., Ballard, G. 2015; 50 (8): 42-53
  • A PARALLEL DIRECTIONAL FAST MULTIPOLE METHOD SIAM JOURNAL ON SCIENTIFIC COMPUTING Benson, A. R., Poulson, J., Tran, K., Engquist, B., Ying, L. 2014; 36 (4): C335-C352

    View details for DOI 10.1137/130945569

    View details for Web of Science ID 000344743800038

  • The Gamma-Ray Imaging Framework IEEE TRANSACTIONS ON NUCLEAR SCIENCE Benson, A. R., Bandstra, M. S., Chivers, D. H., Aucott, T., Augarten, B., Bates, C., Midvidy, A., Pavlovsky, R., Siegrist, J., Vetter, K., Yee, B. 2013; 60 (2): 528-532
  • Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures 2013 IEEE INTERNATIONAL CONFERENCE ON BIG DATA Benson, A. R., Gleich, D. F., Demmel, J. 2013