About
I am a PhD student at Stanford advised by
Jure Leskovec.
My research focuses on developing datadriven methods for
understanding complex systems and behavior. Broadly, my research
spans the areas of network science, data mining, applied machine
learning, tensor and matrix computations, and computational social
science. Before coming to Stanford, I studied EE/CS and applied
math across the bay at Berkeley. I have also spent time at Google
(summers 2011, 2012, 2015, 2016), Sandia National Labs (summer
2014), and HP Labs (summer 2013).
I am applying for academic and research positions right now. My job materials are below.
Please contact me about any opportunities: arbenson@stanford.edu.
Research Statement
Teaching Statement
CV
News and events
 06.17.2017. Attending the Householder Symposium on Numerical Linear Algebra.
 04.22.2017. Giving a talk at the AMS Sectional Meeting Special Session on Clustering of Graphs: Theory and Practice.
 02.27.2017. Giving a talk and coorganizing a minisymposium on eigenvectors and decompositions of structured tensors at SIAM CSE17.
 02.09.2017. Presenting our paper on motifs in temporal networks at WSDM.
 01.12.2017. Gave a talk on higherorder network analysis at the Univ. of Chicago Scientific and Statistical Computing Seminar.
 07.08.2016. My paper with David Gleich and Jure Leskovec, Higherorder organization of complex networks, is now out in Science (code and data is available here). This research has been covered by Stanford News, Phys.org, and DARPA.
Papers
 Motifs in Temporal Networks.
Ashwin Paranjape, Austin R. Benson, and Jure Leskovec.
In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM), 2017.
 The spacey random walk: a stochastic process for higherorder data.
Austin R. Benson, David F. Gleich, and LekHeng Lim.
To appear in SIAM Review (Research Spotlights), 2017.  Higherorder organization of complex networks.
Austin R. Benson, David F. Gleich, and Jure Leskovec.
Science, 353.6295, 163–166, 2016.  General tensor spectral coclustering for higherorder data.
Tao Wu, Austin R. Benson, and David F. Gleich.
In Proceedings of Neural Information Processing Systems (NIPS), 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.
Grey Ballard, Austin R. Benson, Alex Druinksy, Benjamin Lipshitz, and Oded Schwartz.
SIAM Journal Matrix Analysis & Applications, 37:4, 1382–1418, 2016.
 Tensor spectral clustering for partitioning higherorder 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 nearseparable tallandskinny 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, 36:4, C335–C352, 2014.
 Silent error detection in numerical timestepping schemes.
Austin R. Benson, Sven Schmit, and Robert Schreiber.
International Journal of High Performance Computing Applications, 29: 403–421, 2014.
 Direct QR factorizations for tallandskinny 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.
Teaching
 TA, CS 224W: Social and Information Network Analysis, Fall 2016.
 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.