Max Jerdee, a Complexity Postdoctoral Fellow at SFI, builds statistical tools to study the structure of networks — not just who connects to whom, but how patterns of hierarchy and community vary across systems. “There are many different networks we could consider,” he says. “Networks of friendship, networks in your brain, networks of transportation … and somehow you can come up with a unified language to talk about all these things.” His research develops that language: mathematical and statistical tools for revealing structure in complex systems.
Jerdee focuses on building interpretable, unbiased methods for analyzing network data. Using tools such as Bayesian inference and related probabilistic approaches, he develops models that identify network structures while rigorously quantifying uncertainty. “I think a lot about how our methods shape what we believe we see in data,” he says. “If the instrument is biased, everything downstream will be too.”
Building on this approach, his recent work compares systems with different degrees of hierarchy — from strongly hierarchical animal dominance networks to more evenly matched outcomes in sports leagues — and how the same mathematical framework can describe both.
At SFI, Jerdee plans to extend these tools into a broader framework for comparing network organization and for investigating the mechanisms that give rise to it. He also develops algorithmic improvements — drawing on techniques from statistical physics — that make it possible to apply these models more efficiently and at larger scales, opening new possibilities for studying how structure and function coevolve in complex systems.
Jerdee earned an A.B. in physics from Princeton University and a Ph.D. in physics at the University of Michigan, where he works with SFI External Professor Mark Newman. His earlier research ranged from astrophysical-image analysis to high-energy theory before turning to network science, applying the same mathematical curiosity to the social and biological systems that shape our world.