News & Events

Computational methods for human networks and high-stakes decisions

Serina Chang (Stanford University)

Colloquium

Tuesday, February 13, 2024, 3:30 pm

Abstract

In an interconnected world, effective policymaking increasingly relies on understanding large-scale human networks. However, there are many challenges to understanding networks and how they impact decision-making, including (1) how to infer human networks, which are often unobserved, from data, (2) how to model complex processes, such as disease spread, over networks and inform decision-making, (3) how to estimate the impacts of decisions, in turn, on human networks. In this talk, I'll discuss how I've addressed each of these challenges in my research. I'll focus mainly on COVID-19 pandemic response as a concrete application, where we've developed new methods for network inference and epidemiological modeling, and deployed decision-support tools for policymakers. I'll also touch on other network-driven challenges, including political polarization and supply chain resilience.

Bio

Serina Chang is a PhD candidate in Computer Science at Stanford University. Her research develops machine learning and network science methods to tackle complex societal challenges, from pandemics to polarization to supply chains. Her work has been published in venues including Nature, PNAS, KDD, AAAI, EMNLP, and ICWSM, and featured by over 650 news outlets, including The New York Times and The Washington Post. Her work is also recognized by the KDD 2021 Best Paper Award, NSF Graduate Research Fellowship, Meta PhD Fellowship, EECS Rising Stars, Rising Stars in Data Science, and Cornell Future Faculty Symposium.