Serina Chang

I recently completed my PhD in CS at Stanford, advised by Jure Leskovec and Johan Ugander. I look forward to starting at Berkeley in July 2025 as an Assistant Professor, with a joint appointment in EECS and Computational Precision Health! Before then, I'll be doing a postdoc at Microsoft Research NYC in the Computational Social Science group. I'm recruiting students from Berkeley EECS and CPH in Fall 2024, so please note my name in your application if you'd like to work with me.

My research falls at the intersection of AI, public health, and social science. Specifically, I develop AI methods to improve public health, understand human behavior, and guide data-driven policymaking. I'm interested in leveraging novel data sources - such as mobility data and search logs - to better understand human networks and behaviors at the center of societal 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 new challenges, such as how to infer networks from aggregated data (Nature'21, ICML'24), estimate causal spillover effects of policies (AAAI'23), or extract precise behavioral 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, I develop new methods blending machine learning, network science, and natural language processing. I use these methods to develop policy insights and tools (KDD'21, IAAI'22), which have been widely used by policymakers.

My work is recognized by a KDD Best Paper Award, NSF Graduate Research Fellowship, Meta PhD Fellowship, EECS Rising Stars, Rising Stars in Data Science, and Cornell Future Faculty Symposium, and has been featured by over 650 news outlets, including The New York Times and The Washington Post.