Galvin has been a PhD candidate at the Complexity Science Hub’s Digital Innovation School since December 2024. He holds a bachelor’s degree in Quantitative Finance from the National University of Singapore and a master’s degree in Applied Mathematics from École Polytechnique in France. Galvin conducted his master’s thesis research on generating synthetic supply networks using shallow learning, in collaboration with the Institute of New Economic Thinking (INET) at the University of Oxford.

He first joined the CSH as a summer intern, where he worked on reconstructing international trade networks using Bill of Lading data. Galvin’s research interests lie in supply networks, particularly their applications in trade and the environment.