Kristina Gligorić

Bio: Kristina Gligorić is a Postdoctoral Scholar at Stanford University Computer Science Department, advised by Dan Jurafsky at the NLP group. Previously she obtained her Ph.D. in Computer Science at EPFL, where she was advised by Robert West. Her research focuses on developing computational approaches that help solve burning societal issues, understand and improve human well-being, and promote social good. She leverages large-scale textual data and digital behavioral traces and tailors computational methods drawn from AI, NLP, and causal inference. She puts a strong emphasis on understanding and addressing the biases, limitations, and social implications of computational approaches deployed in high-stakes scenarios. Her work has been published in top computer science conferences (such as ACM CSCW, AAAI ICWSM, and TheWebConf) and broad audience journals (Nature Communications and Nature Medicine). She is a Swiss National Science Foundation Fellow. She received awards for her work, including CSCW 2021 Best Paper Honorable Mention Award, ICWSM 2021 and 2023 Best Reviewer Award, and EPFL Best Teaching Assistant Award.

Talk Title: Computational Approaches for Studying Dietary Behaviors with Digital Traces

Abstract: Human dietary habits shape our health, daily life, societies, the environment, and life on earth. However, it remains challenging to understand and attempt to change dietary behaviors using traditional methods due to
measurement and causal identification challenges.

In this talk, I will describe computational and causal approaches leveraging large-scale passively sensed digital traces to shed new light on our dietary behaviors and derive novel scientific insights. I study dietary behaviors in two contexts: campus-wide and worldwide, based on digital traces capturing behaviors of tens of thousands of people on campus and millions of internet users.

First, I will present studies based on situated on-campus food purchase logs. The first study reveals how, when a person acquires a new eating partner on campus or has a meal together, the healthiness of their food choice shifts significantly in the direction of their new eating partner’s dietary patterns. Second, I will describe a study leveraging online information-seeking traces (Google search query logs). Studying worldwide dietary behaviors, I identify and describe global shifts in dietary interests during the first wave of the COVID-19 pandemic. Third, I critically investigate the limits to how much computational approaches can reveal about dietary behaviors in the general population. I outline a framework for reasoning about biases of digital traces and present a case study of food consumption in Switzerland.

Overall, these novel scientific findings and methodological advances contribute to the existing knowledge about human dietary behaviors and inform the design of food systems, policies, and interventions.