Phil Chodrow

Applied math and data science @UCLA (he/him/his)



I am a Hedrick Visiting Assistant Adjunct Professor in the Department of Mathematics at UCLA, 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 networked systems. Recent projects include models of random graphs and hypergraphs; dynamics of adaptive networks; inference in generative network models; and techniques for clustering higher-order network data.

I am a passionate educator, and teach subjects ranging from applied probability to network science to computer programming. I am enthusiastic about evidence-based, inclusive teaching; project-based learning; and a human-centered approach to creative, quantitative problem-solving. I am also a Partner and Affiliated Data Scientist at QSIDE, the Institute for the Quantitative Study of Inclusion, Diversity, and Equity. At QSIDE, I support projects that build quantitative insight about disparity in the United States, and mentor undergraduates on modern tools and practices for data science.

I did my PhD at MIT’s Operations Research Center under the mentorship of Marta Gonz├ílez and Patrick Jaillet. My research at MIT was supported by the NSF Graduate Research Fellowship.


Upcoming talks