About

I am a PhD student in Computational Math at Stanford University advised by Jure Leskovec. My research focuses on network science, matrix computations, and data mining. I have interned with Google (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.

My research is currently supported by a Stanford Graduate Fellowship.

News and events

  • 05/21/16: Talk on higher-order network clustering at MX16.
  • 05/11/16: Talk on spacey random walks at Purdue.
  • 03/21/16: Talk on higher-order network clustering at Copper Mountain.
  • 03/03/16: Talk on spacey random walks in the Linear Algebra and Optimization seminar at Stanford.
  • 02/29/16: Talk on spacey random walks in the CME 500 seminar at Stanford.
  • Summer 2015: I interned at Google with Ravi Kumar and Andrew Tomkins.

Papers

  • General tensor spectral co-clustering for higher-order data.
    Tao Wu, Austin R. Benson, and David F. Gleich.
    arXiv, cs.SI:1603.00395, 2016.
  • The spacey random walk: a stochastic process for higher-order data.
    Austin R. Benson, David F. Gleich, and Lek-Heng Lim.
    arXiv, cs.NA:1602.02102, 2016.
  • On the relevance of irrelevant alternatives.
    Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
    In Proceedings of the 25th International Conference on World Wide Web (WWW), 2016.
  • Modeling user consumption sequences.
    Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
    In Proceedings of the 25th International Conference on World Wide Web (WWW), 2016.
  • Improving the numerical stability of fast matrix multiplication algorithms.
    Grey Ballard, Austin R. Benson, Alex Druinksy, Benjamin Lipshitz, and Oded Schwartz.
    arXiv, cs.NA:1507.00687, 2015.
  • Tensor spectral clustering for partitioning higher-order network structures.
    Austin R. Benson, David F. Gleich, and Jure Leskovec.
    In Proceedings of the 2015 SIAM International Conference on Data Mining (SDM), 2015.
  • A framework for practical parallel fast matrix multiplication.
    Austin R. Benson and Grey Ballard.
    In Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), 2015.
  • Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices.
    Austin R. Benson, Jason D. Lee, Bartek Rajwa, and David F. Gleich.
    In Proceedings of Neural Information Processing Systems (NIPS), 2014.
    Selected for spotlight presentation.
  • Learning multifractal structure in large networks.
    Austin R. Benson, Carlos Riquelme, and Sven Schmit.
    In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2014.
  • A parallel directional Fast Multipole Method.
    Austin R. Benson, Jack Poulson, Kenneth Tran, Björn Engquist, and Lexing Ying.
    SIAM Journal on Scientific Computing, 2014 36:4, C335-C352.
  • Silent error detection in numerical time-stepping schemes.
    Austin R. Benson, Sven Schmit, and Robert Schreiber.
    International Journal of High Performance Computing Applications, November 2015 29: 403-421. First published April, 2014.
  • Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures.
    Austin R. Benson, David F. Gleich, and James Demmel.
    In Proceedings of the 2013 IEEE International Conference on Big Data (IEEE BigData), 2013.
  • The Gamma-Ray Imaging Framework.
    Austin R. Benson, Mark S. Bandstra, Daniel H. Chivers, Timothy Aucott, Ben Augarten, Cameron Bates, Adam Midvidy, Ryan Pavlovsky, James Siegrist, Kai Vetter, and Ben Yee.
    IEEE Transactions on Nuclear Science. 60(2):528-532, 2013.

Teaching

  • Instructor, Discrete Mathematics and Algorithms ICME refresher course, Summer 2014.
    Lecture notes are available.
  • Volunteer TA, CME 181: Projects in Applied and Computational Mathematics, Winter 2014.
  • Instructor, CME 193: Introduction to Scientific Python, Spring 2013.
  • Instructor, CME 193: Introduction to Scientific Python, Winter 2013.