Assistant Professor of Computer Science
Field Member of Applied Math
Cornell University
My research develops computational frameworks for analyzing and
understanding largescale 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.
[Curriculum
Vitae]
My research is supported by the ARO and the
NSF.
Recent news and events

11.09.2018. My research with Rediet Abebe, Michael Schaub, Ali Jadbabaie, and Jon Kleinberg on Simplicial closure and higherorder link prediction was published in PNAS. See the Cornell Chronicle article Predicting future combos, from rap songs to pharmaceuticals for the highlights. 
06.07.2018. I organized a minisymposium on mining and modeling network data at SIAM Discrete Mathematics '18 in Denver. [web] 
05.04.2018. I gave a minitutorial on tensor eigenvectors and stochastic processes with David Gleich at SIAM ALA '18 in Hong Kong. [web] [slides] [code]
Upcoming and recent talks

05.27.2019. Statistical Inference for Network Models NetSci Satellite 
02.13.2019. WSDM '19 [slides] 
02.08.2019. Clarkson Center for Complex Systems Science Seminar [slides] 
10.30.2018. University at Buffalo Applied Mathematics Seminar [slides] 
10.11.2018. Algorithms for Threat Detection Annual Workshop [slides] 
09.10.2018. Cornell Scientific Computing and Numerics (SCAN) seminar [slides] 
08.23.2018. KDD '18 [slides] 
07.13.2018. SIAM Workshop on Network Science [slides] 
07.11.2018. SIAM Annual [slides] 
06.12.2018. HigherOrder Models in Network Science NetSci Satellite [slides] 
06.07.2018. SIAM Discrete Mathematics [slides] 
06.06.2018. Statistical Learning and Data Science / Nonparametric Statistics [slides]
Teaching
Spring 2019. CS 6241: Numerical Methods for Data Science.
Office Hours: Tuesdays, 1:30pm–2:30pm, Gates 413BFall 2018. CS 2850/INFO 2040/ECON 2040/SOC 2090: Networks.
Papers
Preprints
Modeling and Analysis of Tagging Networks in Stack Exchange Communities.
Xiang Fu*, and Shangdi Yu*, and Austin R. Benson (*equal contribution)
arXiv:1902.02372, 2019.
paper pdf code dataThree hypergraph eigenvector centralities.
Austin R. Benson.
arXiv:1807.09644, 2018.
paper pdf codeRandom Walks on Simplicial Complexes and the normalized Hodge Laplacian.
Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie.
arXiv:1807.05044, 2018.
paper pdfComputing tensor Zeigenvectors with dynamical systems.
Austin R. Benson and David F. Gleich.
arXiv:1805.00903, 2018.
paper pdf code
Peerreviewed papers
Corefringe link prediction.
Austin R. Benson and Jon Kleinberg.
To appear in Proceedings of the World Wide Web Conference (WWW), 2019.
paper pdf codeChoosing to grow a graph: Modeling network formation as discrete choice.
Jan Overgoor, Austin R. Benson, and Johan Ugander.
To appear in Proceedings of the World Wide Web Conference (WWW), 2019.
paper pdf codeRandom Spatial Network Models with CorePeriphery Structure.
Junteng Jia and Austin R. Benson.
Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2019.
paper pdf code data posterSampling Methods for Counting Temporal Motifs.
Paul Liu, Austin R. Benson, and Moses Charikar.
Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2019.
paper pdf code dataThe Local Closure Coefficient: A New Perspective On Network Clustering.
Hao Yin, Austin R. Benson, and Jure Leskovec.
Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2019.
paper pdfSimplicial closure and higherorder link prediction.
Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg.
Proceedings of the National Academy of Sciences (PNAS), 2018.
paper pdf supplement code dataFound Graph Data and Planted Vertex Covers.
Austin R. Benson and Jon Kleinberg.
Advances in Neural Information Processing Systems (NeurIPS), 2018.
paper pdf code data posterSequences 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.
paper pdf code data posterHigherorder clustering in networks.
Hao Yin, Austin R. Benson, and Jure Leskovec.
Physical Review E (PRE), 2018.
paper pdf codeA 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.
paper pdf code data posterLocal higherorder graph clustering.
Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich.
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.
paper pdf code dataMotifs in temporal networks.
Ashwin Paranjape*, Austin R. Benson*, and Jure Leskovec. (*equal contribution)
Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2017.
paper pdf code data posterThe spacey random walk: a stochastic process for higherorder data.
Austin R. Benson, David F. Gleich, and LekHeng Lim.
SIAM Review, 2017.
paper pdf code dataHigherorder organization of complex networks.
Austin R. Benson, David F. Gleich, and Jure Leskovec.
Science, 2016.
paper pdf supplement code dataGeneral tensor spectral coclustering for higherorder data.
Tao Wu, Austin R. Benson, and David F. Gleich.
Advances in Neural Information Processing Systems (NeurIPS), 2016.
paper pdf codeOn the relevance of irrelevant alternatives.
Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
Proceedings of the International Conference on World Wide Web (WWW), 2016.
paper pdfModeling user consumption sequences.
Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
Proceedings of the International Conference on World Wide Web (WWW), 2016.
paper pdfImproving the numerical stability of fast matrix multiplication.
Grey Ballard, Austin R. Benson, Alex Druinksy, Benjamin Lipshitz, and Oded Schwartz.
SIAM Journal on Matrix Analysis and Applications (SIMAX), 2016.
paper pdf codeTensor spectral clustering for partitioning higherorder network structures.
Austin R. Benson, David F. Gleich, and Jure Leskovec.
Proceedings of the SIAM International Conference on Data Mining (SDM), 2015.
paper pdf codeA framework for practical parallel fast matrix multiplication.
Austin R. Benson and Grey Ballard.
Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), 2015.
paper pdf codeScalable methods for nonnegative matrix factorizations of nearseparable tallandskinny matrices.
Austin R. Benson, Jason D. Lee, Bartek Rajwa, and David F. Gleich.
Advances in Neural Information Processing Systems (NeurIPS), 2014.
Selected for spotlight presentation .
paper pdf code data posterLearning multifractal structure in large networks.
Austin R. Benson, Carlos Riquelme, and Sven Schmit.
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2014.
paper pdf supplementA parallel directional Fast Multipole Method.
Austin R. Benson, Jack Poulson, Kenneth Tran, Björn Engquist, and Lexing Ying.
SIAM Journal on Scientific Computing (SISC), 2014.
paper pdf codeSilent error detection in numerical timestepping schemes.
Austin R. Benson, Sven Schmit, and Robert Schreiber.
International Journal of High Performance Computing Applications, 2014.
paper pdf supplement codeDirect QR factorizations for tallandskinny matrices in MapReduce architectures.
Austin R. Benson, David F. Gleich, and James Demmel.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData), 2013.
paper pdf code
Theses
Tools for higherorder network analysis.
Austin Reilley Benson.
Stanford PhD thesis, 2017.
Stanford ICME Gene Golub Doctoral Dissertation Award winner .
paper pdf