Some topics I’m working on (or have worked on recently) include:

- Applications of configuration-type random graph models in data science.
- Adaptive and evolving networks.
- Dynamic hierarchies in evolving networks.
- Community detection and network inference.
- Information-theoretic methods in spatial analysis.

Some of my **recent collaborators** are:

**Nate Veldt**, Cornell University**Austin Benson**, Cornell University**Nicole Eikmeier**, Grinnell College**Mari Kawakatsu**, Princeton University**Dan Larremore**, UC Boulder**Marta González**, UC Berkeley**Andy Mellor**, Oxford**Peter Mucha**, UNC Chapel Hill**Mason Porter**, UCLA**Alice Schwarze**, University of Washington

Phil Chodrow
(2020).
SIMODS.

What’s the expected adjacency matrix of a uniformly random multigraph with fixed degree sequence?

Phil Chodrow
(2020).
Journal of Complex Networks.

Random null models for principled hypothesis-testing in polyadic data.

Phil Chodrow, Peter Mucha
(2020).
SIAM Journal on Applied Mathematics.

New approximations for opinion dynamics on coevolving graphs.

Phil Chodrow, Andrew Mellor
(2020).
Applied Network Science.

Sampling theory, metrics, and vignettes for studying higher-order networks with node-edge incidence annotations. Generalizes directed …

Phil Chodrow
(2017).
Proceedings of the National Academy of Sciences.

Information geometry and machine learning for studying spatial segregation.

Data-informed modeling and optimization of multimodal transportation systems.

Novel bounds for a class of dynamical systems on the nonnegative cone.

A statistically-motivated extension of network modularity to hypergraphs.

A mathematical model of hierarchy in networks, which we study analytically and statistically.

Mathematics of estimating mean subsystem entropy in jointly Gaussian dynamical systems.

Phil Chodrow
(2017).
arXiv: 1708.07459.

An invitation to information theory for complex systems scientists and other nonspecialists.