## 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.

## News and events

- Summer 2016: I am interning at Google Research in Mountain View.
- 06/23/16: Talk on spacey random walks at LLNL.
- 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.
- Summer 2015: I interned at Google Research 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.