Inferring Networks and Behaviors from Data for High-stakes Decisions
On-campus talk
Serina Chang
PhD Candidate, Stanford University
Past Talk
Hybrid talk
Monday
Dec 4, 2023
Watch video
11:00 am
EST
Virtual
177 Huntington Ave.
11th floor
11th floor
Devon House
58 St Katharine's Way
London E1W 1LP, UK
58 St Katharine's Way
London E1W 1LP, UK
In an interconnected and fast-moving world, effective policymaking increasingly relies on understanding complex human networks and behaviors. For example, pandemic response requires understanding how disease spreads through contact between individuals and how individuals alter their behavior in response to policies and disease. My research develops computational methods to infer robust signals about networks and behaviors from data, then leverages these signals to tackle societal challenges from pandemics to polarization to supply chains.
In this talk, I'll discuss one line of research on mobility networks and disease modeling, including (1) inferring fine-grained mobility networks from aggregated location data, (2) modeling the spread of COVID-19 over mobility networks to inform reopening strategies and analyze disparities. This talk will focus on the two works below, and also touch on our related works in KDD'21 and AAAI'23.
Papers:
1. Chang*, Pierson*, Koh* et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature 2021.
2. Chang*, Qu*, Leskovec, and Ugander. Inferring networks from marginals using iterative proportional fitting. Under review.
About the speaker
About the speaker
Serina Chang is a PhD candidate in Computer Science at Stanford University, co-advised by Jure Leskovec and Johan Ugander. 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. For details, visit https://serinachang5.github.io.
Serina Chang is a PhD candidate in Computer Science at Stanford University, co-advised by Jure Leskovec and Johan Ugander. 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. For details, visit https://serinachang5.github.io.
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