Zoom: https://illinois.zoom.us/j/89403092528?pwd=SnBZTm4yQ3Z1K0JUcDVjLytzU3Fkdz09
Abstract:
In an interconnected world, effective policymaking increasingly relies on understanding complex human networks and processes, such as diseases spreading over contact networks, polarization over social networks, and disruptions in supply chains. However, there are many challenges to understanding networks and how they impact decisions, including (1) how to infer human networks from data, when they are only partially observed, (2) how to model complex processes over networks and inform decision-making, (3) how to estimate the causal impacts of decisions, in turn, on human networks. In this talk, I'll discuss how we've addressed each of these challenges in the context of COVID-19 pandemic response, where we've developed new methods for network inference and epidemiological modeling, and deployed decision-support tools for public health officials. I'll also briefly describe our work in other domains, including GNNs to model supply chains and language models to measure political polarization.
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 in Computational Social Science, EECS Rising Stars, Rising Stars in Data Science, and Cornell Future Faculty Symposium.
Faculty Host: Jim Rehg
Meeting ID: 894 0309 2528 ; Password: csillinois
