Photo credit: Aurora Brachman
serinac@berkeley.edu
Pronouns: she/her
[CV]
[Google Scholar]
[Twitter]
I'm an incoming Assistant Professor at UC Berkeley (starting in July 2025), with a joint appointment in EECS and Computational Precision Health. I recently completed my PhD in CS at Stanford University, advised by Jure Leskovec and Johan Ugander, and I'm currently a postdoc at Microsoft Research NYC in the Computational Social Science group. I'm recruiting students from Berkeley EECS and CPH this cycle (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 and graph methods to study human behavior, improve public health, and guide data-driven policymaking. Please see Research below for details. 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.
Some research directions I'm currently excited about include (but are not limited to):
1) Inferring networks & behaviors from novel data sources. How do we infer hard-to-observe behaviors from novel data sources? How do we leverage aggregated or unlabeled data, balance privacy needs, and validate our inferences?
Examples: inferring mobility networks from location data + modeling COVID-19 spread (Nature'21, ICML'24),
estimating vaccine uptake and hesitancy from anonymized search logs (Nature Comm'24),
measuring polarization and immigration attitudes from political speeches (PNAS'22).
2) Simulating behaviors with LLMs. How do we simulate behaviors at all scales, from individual responses to society-level simulations? How do we validate simulations and address challenges, such as realism, diversity, bias, generalization, and simulation costs? How should we integrate simulators into real-world pipelines (eg, survey studies, AI evaluation) to improve on status quo?
Examples: generating social networks with LLMs (preprint),
simulating public opinion with fine-tuning (ongoing),
simulating generative AI users for better evaluation (ongoing).
3) Supporting policy and public health with AI. How can AI help to guide more effective and equitable decision-making? How do we translate scientific advances into real-world decision-support tools?
Examples: deploying reopening dashboard for Virginia Department of Health (KDD'21),
delivering vaccine site recommendations (IAAI'22),
estimating pandemic policy spillover effects (AAAI'23),
collaboration with United Nations Development Programme (ongoing).
Please see my CV for a full list of papers.
LLMs generate structurally realistic social networks but overestimate political homophily
Serina Chang*, Alicja Chaszczewicz*, Emma Wang, Maya Josifovska, Emma Pierson, and Jure Leskovec
Presented at IC2S2 2024 as Plenary Talk (2.8% submissions)
[paper] [code]
Measuring vaccination coverage and concerns of vaccine holdouts from web search logs
Serina Chang, Adam Fourney, and Eric Horvitz
Nature Communications 2024
Also presented at KDD 2023 Workshop on Epidemiology Meets Data Mining and Knowledge Discovery (oral) and KDD 2023 Workshop on Data Science for Social Good (oral)
[paper] [data & code]
Inferring dynamic networks from marginals with iterative proportional fitting
Serina Chang*, Frederic Koehler*, Zhaonan Qu*, Jure Leskovec, and Johan Ugander
ICML 2024
Also presented at Learning on Graphs 2023 (extended abstract)
[paper] [code]
Estimating geographic spillover effects of COVID-19 policies from large-scale mobility networks
Serina Chang, Damir Vrabac, Jure Leskovec, and Johan Ugander
AAAI 2023
Also presented at KDD 2022 Workshop on Data-driven Humanitarian Mapping and Policymaking (oral) and IC2S2 2022
[paper] [code]
Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration
Dallas Card, Serina Chang, Chris Becker, Julia Mendelsohn, Rob Voigt, Leah Boustan, Ran Abramitzky, and Dan Jurafsky
PNAS 2022
Article in Stanford HAI News by Edmund L. Andrews
[paper] [code]
Supporting COVID-19 policy response with large-scale mobility-based modeling
Serina Chang, Mandy L. Wilson, Bryan Lewis, Zakaria Mehrab, Komal K. Dudakiya, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky, Madhav Marathe, and Jure Leskovec
KDD 2021, Applied Data Science Track - Best Paper Award
[paper] [code] [blog post]
Mobility network models of COVID-19 explain inequities and inform reopening
Serina Chang*, Emma Pierson*, Pang Wei Koh*, Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec
Nature 2021
Commentary in Nature News and Views by Kevin Ma and Dr. Marc Lipsitch
Interactive article in The New York Times by Yaryna Serkez
Selected media coverage: The New York Times, The Washington Post, Bloomberg, CNN, Wired, MIT Technology Review, and Stanford Press
Also presented at Networks 2021 (oral), NeurIPS 2020 Workshop on Machine Learning for Health, NeurIPS 2020 COVID-19 Symposium, and OECD-ODISSEI Webinar on Open Data Infrastructure
[paper] [code] [talk] [website]
Automatically inferring gender associations from language
Serina Chang and Kathleen McKeown
EMNLP 2019 (short paper, oral)
[paper] [code] [talk]
Detecting gang-involved escalation on social media using context
Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Chris Kedzie, Desmond Patton, William Frey, and Kathleen McKeown
EMNLP 2018 (long paper, oral)
[paper] [code] [talk]