Austin R. Benson
Quant @ D. E. Shaw
New York City

arbenson@gmail.com
@austinbenson
CV / resume

I am a quantitative analyst at D. E. Shaw & Co. in New York City, where I work on machine learning and quantitative finance.

Before working in finance, I was an Assistant Professor of Computer Science at Cornell University. My research focused on designing numerical methods and algorithmic frameworks to enable new, better, and bigger analyses of complex data. This research was recognized with an NSF CAREER Award, a KDD best paper award, a JP Morgan Chase AI Faculty Award, and the Gene Golub Doctoral Dissertation Award, and was supported by federal research grants from the Army Research Office and the NSF. I published this research in leading journals such as Science, Science Advances, Proceedings of the National Academy of Sciences, and SIAM Review, and at the main machine learning and data science conferences, including NeurIPS, ICML, ICLR, KDD, and WWW. I also taught classes that covered data science, machine learning, network science, matrix computations, and numerical optimization.

Prior to Cornell, I spent nine formative years in the Bay Area, where I completed my PhD and MS at Stanford University and BS and BA at the University of California, Berkeley. During summers, I interned at Google (four times), Sandia National Laboratories, and HP Labs.

My old academic web site is at https://www.cs.cornell.edu/~arb/.


Research papers

  • Nonlinear Feature Diffusion on Hypergraphs. Francesco Tudisco, Konstantin Prokopchik, Austin R. Benson. International Conference on Machine Learning (ICML), 2022.
    paper pdf

  • fauci-email: a json digest of Anthony Fauci's released emails. Austin R. Benson, Nate Veldt, David F. Gleich. International Conference on Web and Social Media (ICWSM), 2022.
    paper pdf code data

  • Diverse and Experienced Group Discovery via Hypergraph Clustering. Ilya Amburg, Nate Veldt, Austin R. Benson. SIAM Data Mining (SDM) 2022.
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  • Hypergraph Cuts with General Splitting Functions. Nate Veldt, Austin R. Benson, Jon Kleinberg. SIAM Review (SIREV), 2022.
    paper pdf

  • A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations. Junteng Jia, Austin R. Benson. SIAM Journal on Mathematics of Data Science (SIMODS), 2022.
    paper pdf code data

  • Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective. Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li. Neural Information Processing Systems (NeurIPS), 2022.
    paper pdf

  • Graph-Based Methods for Discrete Choice. Kiran Tomlinson, Austin R. Benson. arXiv:2205.11365, 2022.
    paper pdf

  • Graph Neural Network Modeling of Grain-scale Anisotropic Elastic Behavior using Simulated and Measured Microscale Data. Darren C. Pagan, Calvin R. Pash, Austin R. Benson, Matthew P. Kasemer. arXiv:2205.06324, 2022.
    paper pdf

  • Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components. Nate Veldt, Austin R. Benson, Jon Kleinberg. Neural Information Processing Systems (NeurIPS), 2021.
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  • Communication-efficient distributed eigenspace estimation. Vasileios Charisopoulos, Austin R. Benson, Anil Damle. SIAM Journal on Mathematics of Data Science (SIMODS), 2021.
    paper pdf code

  • Generative hypergraph clustering: from blockmodels to modularity. Philip S. Chodrow, Nate Veldt, Austin R. Benson. Science Advances, 2021.
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  • The Generalized Mean Densest Subgraph Problem. Nate Veldt, Austin R. Benson, Jon Kleinberg. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.
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  • Choice Set Confounding in Discrete Choice. Kiran Tomlinson, Johan Ugander, Austin R. Benson. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.
    paper pdf code

  • Learning Interpretable Feature Context Effects in Discrete Choice. Kiran Tomlinson, Austin R. Benson. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.
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  • Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform. Derek Lim, Austin R. Benson. International Conference on Web and Social Media (ICWSM), 2021.
    paper pdf code data

  • Higher-order Network Analysis Takes Off, Fueled by Old Ideas and New Data. Austin R. Benson, David F. Gleich, Desmond J. Higham. SIAM News, 2021.
    article [expanded arXiv version]

  • Combining Label Propagation and Simple Models Out-performs Graph Neural Networks. Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, Austin R. Benson. International Conference on Learning Representations (ICLR), 2021.
    paper pdf code

  • Planted Hitting Set Recovery in Hypergraphs. Ilya Amburg, Jon Kleinberg, Austin R. Benson. Journal of Physics: Complexity, 2021.
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  • Nonlinear Higher-Order Label Spreading. Francesco Tudisco, Austin R. Benson, Konstantin Prokopchik. The Web Conference (WWW), 2021.
    paper pdf code video

  • Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis. Katherine Van Koevering, Austin R. Benson, Jon Kleinberg. The Web Conference (WWW), 2021.
    paper pdf code video

  • Higher Order Information Identifies Tie Strength. Arnab Sarker, Jean-Baptiste Seby, Austin R. Benson, Ali Jadbabaie. arXiv:2108.02091, 2021.
    paper pdf

  • Edge Proposal Sets for Link Prediction. Abhay Singh, Qian Huang, Sijia Linda Huang, Omkar Bhalerao, Horace He, Ser-Nam Lim, Austin R. Benson. arXiv:2106.15810, 2021.
    paper pdf code

  • Graph Belief Propagation Networks. Junteng Jia, Cenk Baykal, Vamsi K. Potluru, Austin R. Benson. arXiv:2106.03033, 2021.
    paper pdf code

  • Higher-order Homophily is Combinatorially Impossible. Nate Veldt, Austin R. Benson, Jon Kleinberg. arXiv:2103.11818, 2021.
    paper pdf code

  • Better Set Representations For Relational Reasoning. Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser-Nam Lim, Austin R. Benson. Neural Information Processing Systems (NeurIPS), 2020.
    paper pdf code

  • Entrywise Convergence of Iterative Methods for Eigenproblems. Vasileios Charisopoulos, Austin R. Benson, Anil Damle. Neural Information Processing Systems (NeurIPS), 2020.
    paper pdf code

  • Residual Correlation in Graph Neural Network Regression. Junteng Jia, Austin R. Benson. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
    paper pdf code data [(G)PyTorch implementation from Junwen Bai and Yucheng Lu.]

  • Minimizing Localized Ratio Cut Objectives in Hypergraphs. Nate Veldt, Austin R. Benson, Jon Kleinberg. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
    paper pdf code data

  • Choice Set Optimization Under Discrete Choice Models of Group Decisions. Kiran Tomlinson, Austin R. Benson. International Conference on Machine Learning (ICML), 2020.
    paper pdf code data slides

  • Neighborhood and PageRank methods for pairwise link prediction. Huda Nassar, Austin Benson, David F. Gleich. Social Network Analysis and Mining (SNAM), 2020
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  • Network Interpolation. Thomas Reeves, Anil Damle, Austin R. Benson. SIAM Journal on Mathematics of Data Science (SIMODS), 2020.
    paper pdf code

  • Measuring Directed Triadic Closure with Closure Coefficients. Hao Yin, Austin R. Benson, Johan Ugander. Network Science, 2020.
    paper pdf code

  • Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplacian. Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie. SIAM Review (SIREV), 2020.
    paper pdf [Editor's overview]

  • Clustering in graphs and hypergraphs with categorical edge labels. Ilya Amburg, Nate Veldt, Austin R. Benson. The Web Conference (WWW), 2020.
    paper pdf code data

  • Frozen Binomials on the Web: Word Ordering and Langauge Conventions in Online Text. Katherine Van Koevering, Austin R. Benson, Jon Kleinberg. The Web Conference (WWW), 2020.
    paper pdf code

  • Using cliques with higher-order spectral embeddings improves graph visualizations. Huda Nassar, Caitlin Kennedy, Shweta Jain, Caitlin Kennedy, Austin R. Benson, David F. Gleich. The Web Conference (WWW), 2020.
    paper pdf code video

  • Retrieving Top Weighted Triangles in Graphs. Raunak Kumar, Paul Liu, Moses Charikar, Austin R. Benson. ACM International Conference on Web Search and Data Mining (WSDM), 2020.
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  • Over-parametrized neural networks as under-determined linear systems. Austin R. Benson, Anil Damle, Alex Townsend. arXiv:2010.15959, 2020.
    paper pdf

  • A simple bipartite graph projection model for clustering in networks. Austin R. Benson, Paul Liu, Hao Yin. arXiv:2007.00761, 2020.
    paper pdf code

  • Augmented Sparsifiers for Generalized Hypergraph Cuts with Applications to Decomposable Submodular Function Minimization. Austin R. Benson, Jon Kleinberg, Nate Veldt. arXiv:2007.08075, 2020.
    paper pdf

  • Neural Jump Stochastic Differential Equations. Junteng Jia, Austin R. Benson. Neural Information Processing Systems (NeurIPS), 2019.
    paper pdf code poster

  • Modeling and Analysis of Tagging Networks in Stack Exchange Communities. Shangdi Yu, Xiang Fu, Austin R. Benson. Journal of Complex Networks, 2019.
    paper pdf code data

  • Computing tensor Z-eigenvectors with dynamical systems. Austin R. Benson, David F. Gleich. SIAM Journal on Matrix Analysis and Applications (SIMAX), 2019.
    paper pdf code

  • Unsupervised learning of dislocation motion. Darren C. Pagan, Thien Q. Phan, Jordan S. Weaver, Austin R. Benson, Armand J. Beaudoin. Acta Materialia, 2019.
    paper pdf

  • Automated Grain Yield Behavior Classification. Darren C. Pagan, Jakob Kaminsky, Wesley A. Tayon, Kelly E. Nygren, Armand J. Beaudoin, Austin R. Benson. The Journal of The Minerals, Metals & Materials Society (JOM), 2019.
    paper pdf

  • Pairwise Link Prediction. Huda Nassar, Austin R. Benson, David F. Gleich. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019. Best Paper Award Runner-up.
    paper pdf code

  • Graph-based Semi-Supervised & Active Learning for Edge Flows. Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.
    paper pdf code poster video

  • Network Density of States. Kun Dong, Austin R. Benson, David Bindel. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019. Best Paper Award
    paper pdf code poster video

  • Three hypergraph eigenvector centralities. Austin R. Benson. SIAM Journal on Mathematics of Data Science (SIMODS), 2019.
    paper pdf code

  • Link Prediction in Networks with Core-Fringe Data. Austin R. Benson, Jon Kleinberg. World Wide Web Conference (WWW), 2019.
    paper pdf code poster

  • Choosing to grow a graph: Modeling network formation as discrete choice. Jan Overgoor, Austin R. Benson, Johan Ugander. World Wide Web Conference (WWW), 2019.
    paper pdf code poster

  • Random Spatial Network Models with Core-Periphery Structure. Junteng Jia, Austin R. Benson. ACM International Conference on Web Search and Data Mining (WSDM), 2019.
    paper pdf code data poster

  • Sampling Methods for Counting Temporal Motifs. Paul Liu, Austin R. Benson, Moses Charikar. ACM International Conference on Web Search and Data Mining (WSDM), 2019.
    paper pdf code data

  • The Local Closure Coefficient: A New Perspective On Network Clustering. Hao Yin, Austin R. Benson, Jure Leskovec. ACM International Conference on Web Search and Data Mining (WSDM), 2019.
    paper pdf code

  • Incrementally Updated Spectral Embeddings. Vasileios Charisopoulos, Austin R. Benson, Anil Damle. arXiv:1909.01188, 2019.
    paper pdf code poster

  • Simplicial closure and higher-order link prediction. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.
    paper pdf supplement code data

  • Found Graph Data and Planted Vertex Covers. Austin R. Benson, Jon Kleinberg. Neural Information Processing Systems (NeurIPS), 2018.
    paper pdf code data poster

  • Sequences of Sets. Austin R. Benson, Ravi Kumar, Andrew Tomkins. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018.
    paper pdf code data poster video

  • Higher-order clustering in networks. Hao Yin, Austin R. Benson, Jure Leskovec. Physical Review E (PRE), 2018.
    paper pdf code

  • A Discrete Choice Model for Subset Selection. Austin R. Benson, Ravi Kumar, Andrew Tomkins. ACM International Conference on Web Search and Data Mining (WSDM), 2018.
    paper pdf code data poster

  • Local higher-order graph clustering. Hao Yin, Austin R. Benson, Jure Leskovec, David F. Gleich. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.
    paper pdf code data video

  • Motifs in temporal networks. Ashwin Paranjape, Austin R. Benson, Jure Leskovec. ACM International Conference on Web Search and Data Mining (WSDM), 2017.
    paper pdf code data poster

  • The spacey random walk: a stochastic process for higher-order data. Austin R. Benson, David F. Gleich, Lek-Heng Lim. SIAM Review (SIREV), 2017.
    paper pdf code data

  • Higher-order organization of complex networks. Austin R. Benson, David F. Gleich, Jure Leskovec. Science, 2016.
    paper pdf supplement code data

  • General tensor spectral co-clustering for higher-order data. Tao Wu, Austin R. Benson, David F. Gleich. Neural Information Processing Systems (NeurIPS), 2016.
    paper pdf code

  • On the relevance of irrelevant alternatives. Austin R. Benson, Ravi Kumar, Andrew Tomkins. International Conference on World Wide Web (WWW), 2016.
    paper pdf

  • Modeling user consumption sequences. Austin R. Benson, Ravi Kumar, Andrew Tomkins. International Conference on World Wide Web (WWW), 2016.
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  • Improving the numerical stability of fast matrix multiplication. Grey Ballard, Austin R. Benson, Alex Druinksy, Benjamin Lipshitz, Oded Schwartz. SIAM Journal on Matrix Analysis and Applications (SIMAX), 2016.
    paper pdf code

  • Tensor spectral clustering for partitioning higher-order network structures. Austin R. Benson, David F. Gleich, Jure Leskovec. SIAM International Conference on Data Mining (SDM), 2015.
    paper pdf code video

  • A framework for practical parallel fast matrix multiplication. Austin R. Benson, Grey Ballard. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), 2015.
    paper pdf code

  • Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices. Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich. Neural Information Processing Systems (NeurIPS), 2014. Spotlight Presentation
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  • Learning multifractal structure in large networks. Austin R. Benson, Carlos Riquelme, Sven Schmit. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2014.
    paper pdf supplement video

  • A parallel directional Fast Multipole Method. Austin R. Benson, Jack Poulson, Kenneth Tran, Björn Engquist, Lexing Ying. SIAM Journal on Scientific Computing (SISC), 2014.
    paper pdf code

  • Silent error detection in numerical time-stepping schemes. Austin R. Benson, Sven Schmit, Robert Schreiber. International Journal of High Performance Computing Applications (IJHPCA), 2014.
    paper pdf supplement code

  • Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures. Austin R. Benson, David F. Gleich, James Demmel. IEEE International Conference on Big Data (IEEE BigData), 2013.
    paper pdf code


Teaching material
Cornell classes Expository material