معرفی
Annie Liang is an Associate Professor of Economics and courtesy Associate Professor of Computer Science at Northwestern University. She holds a PhD from Harvard University (2016) and dual SB degrees in Mathematics and Economics from MIT (2011). Her research bridges economic theory, machine learning, and behavioral economics, focusing on welfare implications of algorithms, improving economic models via ML, and dynamic information strategies. She has held tenured positions since 2024 and previously taught at University of Pennsylvania and University of Pennsylvania (2017–2020).
Education: PhD in Economics (Harvard, 2016); SB Mathematics & SB Economics (MIT, 2011). Professional roles include Program Committee roles at EC conferences, NSF grants (CAREER award 2022–2027), and service as a referee for top journals like *American Economic Review* and *Econometrica*. Teaching includes courses on data economics, information economics, and algorithmic decision-making.
Research highlights include work on algorithmic fairness trade-offs, model completeness measurement, and information aggregation dynamics. Her work has been recognized with the Economic Theory Fellow award (2022) and the Kravis Teaching Award (2018–2019).
Grants/awards include $1.2M NSF CAREER grant (SES-2145352) and multiple conference recognitions (e.g., EC '21 Exemplary Paper). Active in interdisciplinary initiatives such as the Machine Learning in Economics Summer Conference (2024) and NeurIPS workshops on AI/ML societal impact.
