Quant Finance. Since 2021, I have been a quant finance researcher. Most recently, I was in the systematic equities group at DE Shaw (2021-2025, VP 24-25). Right now, I'm on a non-compete period before joining a new firm in fall 2026.
Academia. Before finance, I spent four years in the Department of Computer Science at Cornell University, serving as an Assistant Professor for three years following a one-year postdoc. My research focused on designing numerical methods and algorithmic frameworks to enable new, better, and bigger analyses of complex data. These articles highlight the main ideas of this research:
- How Big Data Carried Graph Theory Into New Dimensions, Quanta Magazine
- Higher-order Network Analysis Takes Off, Fueled by Old Ideas and New Data, SIAM News
This work 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 ARO and the NSF. I published in leading venues including Science, Science Advances, PNAS, and SIAM Review, as well as top machine learning and data science conferences like NeurIPS, ICML, ICLR, KDD, and WWW. I also taught courses spanning data science, machine learning, network science, matrix computations, and numerical optimization. Throughout this, I mentored postdocs, PhD students, and undergraduate researchers who have gone on to faculty positions, industry roles at companies like Meta and Microsoft Research, and top PhD programs.
Education. Prior to Cornell, I spent nine formative years in the Bay Area, where I completed my PhD and MS in computational mathematics at Stanford University and my BS in EECS and BA in applied math at UC Berkeley. During summers, I interned at Google four times, as well as at Sandia National Laboratories and HP Labs, gaining experience across industry research and large-scale systems.
The Early Years. I was born and raised in and around Madison, Wisconsin. I like to think that I maintain my Midwestern values.
Selected Publications
A complete list of publications is available in my CV.
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Higher-order organization of complex networksScience, 2016
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Simplicial closure and higher-order link predictionProceedings of the National Academy of Sciences (PNAS), 2018
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A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and CombinationsSIAM Journal on Mathematics of Data Science (SIMODS), 2022
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Combining Label Propagation and Simple Models Out-performs Graph Neural NetworksInternational Conference on Learning Representations (ICLR), 2021
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Choosing to grow a graph: Modeling network formation as discrete choiceThe Web Conference (WWW), 2019
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Random Walks on Simplicial Complexes and the normalized Hodge 1-LaplacianSIAM Review, 2020
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Hypergraph Cuts with General Splitting FunctionsSIAM Review, 2022
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On the relevance of irrelevant alternativesThe World Wide Web Conference (WWW), 2016
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The spacey random walk: a stochastic process for higher-order dataSIAM Review, 2017
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Motifs in temporal networksThe International Conference on Web Search and Data Mining (WSDM), 2017