Julian’s research centers around applications of probability and statistical physics, towards the extraction of useful patterns from genomics data. He received his mathematics Ph.D. in 2017 from UCLA. As a graduate student advised by Marek Biskup, he specialized in percolation — a cornerstone of random network models. 

Julian was then an NSF postdoctoral fellow at Northwestern University, where he worked with Antonio Auffinger on first-passage percolation and spin glasses. First-passage percolation concerns the metric properties of randomly weighted networks, while spin glasses are prototypical models of disordered and complex physical systems. Under Prof. Auffinger’s mentorship, Julian developed his interest in the applications of these topics to problems in quantitative biology. Most recently, he is interested in causal discovery, and in using causal structural models for simulation and transfer learning.