Phil Chodrow


I am a Hedrick Visiting Assistant Adjunct Professor in the Department of Mathematics at UCLA, where I am mentored by Mason Porter. My interests include network science, nonlinear dynamics, applied probability, and machine learning. In my research, I build principled tools for studying the structure and dynamics of complex, networked systems. Recently, I’ve been thinking about spectral properties of hypergraphs, dominance hierarchies in biosocial systems, and nonlinear opinion dynamics on graphs.

I am also a passionate educator, enthusiastic about evidence-based pedagogy, student-centered design, and inclusion in the classroom. I teach subjects including data science, mathematical network science, applied probability, and computing. I am a member of the Gold ‘21 Cohort of MAA Project NeXT, a professional development program for teacher-scholars in mathematics.

I believe deeply in the role of data science in promoting equity and social justice. I am currently working with undergraduate collaborators on projects related to gender representation in mathematical subfields and racial disparities in criminal sentencing. I am a Partner at QSIDE, the Institute for the Quantitative Study of Inclusion, Diversity, and Equity.

I did my PhD at MIT’s Operations Research Center under the mentorship of Marta González and Patrick Jaillet. My research at MIT was supported by the NSF Graduate Research Fellowship. Prior to MIT, I was an undergraduate at Swarthmore College and a Fulbright Research Scholar at the University of Oslo, Norway.


  • February, 2022: I will be a co-organizer of the second edition of the workshop on Graphs And More Complex Structures For Learning And Reasoning at AAAI in February, 2022.
  • January, 2022: There will be a session at Joint Mathematics Meetings on “Projects Across the Mathematics Curriculum”! I am organizing it together with Bradley Burdick (Hanover College).
  • December, 2021: Invited talk at the 2021 Winter Canadian Mathematical Society (CMS) Meeting in a special session on “Graph Theory and its Applications.”
  • November 18th, 2021: Invited colloquium at Grinnell College on generative approaches for hypergraph clustering. Slides here.
  • September 20th, 2021: Invited seminar at the Claremont Center for the Mathematical Sciences on “Eigenvector Methods for Community Detection in Hypergraphs.” I’ll be presenting some ongoing work with Jamie Haddock (Mathematics, Harvey Mudd) and Nicole Eikmeier (Computer Science, Grinnell). Slides here.
  • August 25th, 2021: “Space-based observational constraints on NO2 air pollution inequality from diesel traffic in major US cities” is now published in Geophysics Research Letters! This collaboration, led by Angelique Demetillo (Environmental Science and Atmospheric Chemistry, UVA) and Sally Pusede (Environmental Science, UVA), uses satellite data and spatial analysis to study class, ethnic, and racial disparities in NO₂ pollution in major US cities. We used the information-geometric tools from “Structure and information in spatial segregation” to study ethnic and racial segregation. Read the new paper here!
  • August 25th, 2021: Our explainer article “Networks, Dynamics, and Prestige: How Hierarchies Emerge from Individual Choices ” is now published on the SIAM News blog! This article, joint with Mari Kawakatsu, Nicole Eikmeier, and Dan Larremore, gives an accessible overview of our recent paper “Emergence of Hierarchy in Networked Endorsement Dynamics” at PNAS.
  • August 2nd, 2021: Thrilled to attend MAA Project NExT as part of the Gold ‘21 cohort! Also attending other sessions at MAA MathFest.
  • July 15th, 2021: Invited talk on feedback loops and ranks in networks at UCLA’s REU group on “Kaczmarz Methods for Large-Scale Data Analysis,” led by Jamie Haddock.
  • July 7th, 2021: “Generative hypergraph clustering: from blockmodels to modularity” with Nate Veldt and Austin Benson is now published in Science Advances.

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