Uploaded February 2024 | Updated September 2026, 2 weeks ago
Allen School Colloquia Series
Title: Computational methods for human networks and high-stakes decisions
Speaker: Serina Chang (Stanford University)
Host: Sheng Wang
Date: Tuesday, February 13, 2024
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.
This video is closed captioned.
Allen School Colloquia Series
Title: Computational methods for human networks and high-stakes decisions
Speaker: Serina Chang (Stanford University)
Host: Sheng Wang
Date: Tuesday, February 13, 2024
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.
This video is closed captioned.
![[Audio Descriptions] Faculty In Focus: Nirvan Tyagi](https://i.ytimg.com/vi/v8NzVoue-Ac/mqdefault.jpg "[Audio Descriptions] Faculty In Focus: Nirvan Tyagi
In this episode of the Allen School’s “Faculty in Focus” series, Assistant Professor Nirvan Tyagi discusses his work on cryptography techniques such as zero knowledge proofs, which would enable systems used in banking and other services to provide strong privacy for users while protecting them against bad actors. His research aims to give technology users more control over their data while maintaining efficiency and performance.
For a version without audio descriptions, visit https://youtu.be/PMaUVhjEJlA.")
