Mobility Network Models of COVID-19 Explain Inequities and Inform Reopening
Speakers: Jure Leskovec & Serina Chang, Stanford University
Bio: Jure Leskovec is an Associate Professor of Computer Science at Stanford University where he is a member of the InfoLab and the AI lab. He is also an investigator at Chan Zuckerberg Biohub, where I focus on developing new methods for the analysis of biomedical data.
HIs general research area is applied machine learning and data science for large interconnected systems and focuses on modeling complex, richly-labeled relational structures, graphs, and networks for systems at all scales, from interactions of proteins in a cell to interactions between humans in a society. Applications include commonsense reasoning, recommender systems, computational social science, and computational biology with an emphasis on drug discovery.
This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. It has also been featured in popular press outlets such as the New York Times and the Wall Street Journal. I received my bachelor's degree in computer science from University of Ljubljana, Slovenia, PhD in machine learning from Carnegie Mellon University , and postdoctoral training at Cornell University.
Serina Chang currently a PhD candidate in CS at Stanford, advised by Prof. Jure Leskovec and Prof. Johan Ugander. Previously, she completed her undergrad at Columbia, where she studied CS and Sociology, and was advised by Prof. Kathy McKeown. Her research develops computational methods to tackle complex societal challenges, from pandemics to polarization to supply chains.
She leverages novel data sources - such as cell phones, search logs, and social media - to understand human networks and behaviors at the center of such challenges. These data sources provide new opportunities to capture individuals at scale, with the potential to improve decisions that affect billions every day. However, novel data also introduce challenges for analysis, such as how to infer fine-grained networks from aggregated data (Nature'21), how to estimate causal spillover effects of policies (AAAI'23), and how to extract precise signals from vast unlabeled data such as search logs (arXiv'23), speeches (PNAS'22), news articles (EMNLP'19), and social media (EMNLP'18). To address these challenges, her research develops new methods blending machine learning, network science, and natural language processing. She uses these methods to develop policy insights and tools (KDD'21, IAAI'22), which have been widely used by policymakers.