Computational Methods for Human Networks and High-Stakes Decisions

Explore a colloquium talk by Stanford University PhD candidate Serina Chang on computational methods for human networks and high-stakes decision-making. Delve into the challenges of understanding large-scale human networks and their impact on policymaking, focusing on three key areas: inferring unobserved networks from data, modeling complex processes like disease spread over networks, and estimating the effects of decisions on human networks. Learn about Chang's research applications in COVID-19 pandemic response, including the development of network inference and epidemiological modeling methods, as well as the deployment of decision-support tools for policymakers. Gain insights into other network-driven challenges such as political polarization and supply chain resilience. Discover how Chang's work in machine learning and network science addresses complex societal issues, with her research published in prestigious venues and recognized through various awards and fellowships.

Allen School Colloquium: Serina Chang (Stanford University)